Grand Challenges 

for Biological and 

Environmental 

Research:

Progress and

Future Vision

November 2017

A report from the Biological and Environmental Research Advisory Committee

DOE/SC–0190
Suggested citation for this report: BERAC. 2017. Grand Challenges for Biological and Environmental Research: Progress and 

Future Vision; A Report from the Biological and Environmental Research Advisory Committee, DOE/SC–0190, BERAC Subcommittee 

on Grand Research Challenges for Biological and Environmental Research (science.energy.gov/~/media/ber/berac/pdf/Reports/

BERAC-2017-Grand-Challenges-Report.pdf).

Cover

Images represent a broad range of natural systems that drive the science supported by the Office of Biological and 

Environmental Research within the U.S. Department of Energy Office of Science. These systems are not only structurally and 

spatially complex with many different interacting parts spanning molecular to global scales, but they also are dynamically 

complex, encompassing processes that occur over time scales ranging from nanoseconds to centuries.

Image credits: Ball-and-stick representation of atoms within a molecule from Pacific Northwest National Laboratory. Ribosome 

molecular complex image from Ditlev Brodersen, Aarhus University. Microscopic image of fungal hyphae on a root surface, 

copyright Merton Brown, Visuals Unlimited. Forest ecosystem image from the U.S. National Park Service. Mountain and Earth 

images from iStockphoto. Cover developed at Oak Ridge National Laboratory.
Grand Challenges for Biological 

and Environmental Research:

Progress and Future Vision

A Report from the Biological and Environmental 

Research Advisory Committee

November 2017

Chair

Gary Stacey (University of Missouri)

Prepared by the BERAC Subcommittee on 

Grand Research Challenges for Biological and Environmental Research

Report available online at 

science.energy.gov/~/media/ber/berac/pdf/Reports/BERAC-2017-Grand-Challenges-Report.pdf

U.S. Department of Energy

DOE/SC–0190
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Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Biological and Environmental Research Advisory Committee November 2017
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Grand Challenges for Biological and Environmental Research: Progress and Future Vision

November 2017 Biological and Environmental Research Advisory Committee

Chair

Gary Stacey

University of Missouri

Sarah M. Assmann

Pennsylvania State University

Dennis D. Baldocchi

University of California, Berkeley

Amy M. Brunner

Virginia Polytechnic Institute 

and State University

James R. Ehleringer

University of Utah

James J. Hack

Oak Ridge National Laboratory

Bruce A. Hungate

Northern Arizona University

Anthony C. Janetos

Boston University

Andrzej Joachimiak

Argonne National Laboratory

Cheryl R. Kuske

Los Alamos National Laboratory

L. Ruby Leung

Pacific Northwest National Laboratory

Gerald A. Meehl

National Center for Atmospheric Research

Jerry M. Melillo

Marine Biological Laboratory

Gloria K. Muday

Wake Forest University

Kristala L. Jones Prather

Massachusetts Institute of Technology

David A. Randall

Colorado State University

James T. Randerson

University of California, Irvine

Karin A. Remington

Computationality, LLC

G. Philip Robertson

Michigan State University

Karen Schlauch

University of Nevada, Reno

Daniel Segrè

Boston University

David A. Stahl

University of Washington

Judy D. Wall

University of Missouri

John P. Weyant

Stanford University

Minghua Zhang

State University of New York at Stony Brook

Huimin Zhao

University of Illinois at Urbana-Champaign

Designated Federal Officer

Tristram West

U.S. Department of Energy Office of Biological 

and Environmental Research

Biological and Environmental Research Advisory Committee
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Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Biological and Environmental Research Advisory Committee November 2017

BERAC Subcommittee on Grand Research Challenges 

for Biological and Environmental Research

Gary Stacey, BERAC Chair

University of Missouri 

James J. Hack

Oak Ridge National Laboratory 

Bruce A. Hungate

Northern Arizona University 

Anthony C. Janetos

Boston University

Andrzej Joachimiak

Argonne National Laboratory

Cheryl R. Kuske

 Los Alamos National Laboratory

Gerald A. Meehl

National Center for Atmospheric Research

Jerry M. Melillo

Marine Biological Laboratory

David A. Randall

Colorado State University

James T. Randerson

University of California, Irvine

Karin A. Remington

Computationality, LLC

G. Philip Robertson

Michigan State University

Daniel Segrè

Boston University

David A. Stahl

University of Washington

John P. Weyant

Stanford University

Minghua Zhang

State University of New York at Stony Brook

Huimin Zhao

University of Illinois at Urbana-Champaign
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Grand Challenges for Biological and Environmental Research: Progress and Future Vision

November 2017 Biological and Environmental Research Advisory Committee

Contributing Authors

Chapter 2: Grand Challenges in Biological Systems Science

Chair/Lead Writer: Cheryl R. Kuske

Co-Chair/Facilitator: Daniel Segrè

Writers: Mary Dunlop, Andrzej Joachimiak, John Shanklin, and Huimin Zhao

Chapter 3: Grand Challenges in Earth and Environmental Systems Sciences

Chair/Lead Writer: L. Ruby Leung

Co-Chair/Facilitator: Minghua Zhang

Writers: David A. Randall, James T. Randerson, Charles Jackson, Gerald A. Meehl, and Michael Wehner

Chapter 4: Grand Challenges in Microbial to Earth System Pathways

Chair/Lead Writer: Bruce A. Hungate

Co-Chair/Facilitator: David A. Stahl

Writers: Kirsten Hofmockel, Jennifer Pett-Ridge, Richard Phillips, Will Wieder, and Victoria Orphan

Chapter 5: Grand Challenges in Energy Sustainability

Chair/Lead Writer: G. Philip Robertson

Co-Chair/Facilitator: John P. Weyant

Writers: Susan Hubbard, Anthony C. Janetos, James T. Randerson, and David A. Stahl

Chapter 6: Grand Challenges in Data Analytics and Computing

Chair/Lead Writer: Karin A. Remington

Co-Chair/Facilitator: Karen Schlauch

Writers: Shibu Yooseph and Kerstin Kleese van Dam

Chapter 7: User Facilities and Research Infrastructure

Chair/Lead Writer: Andrzej Joachimiak

Co-Chair/Facilitator: Susannah Tringe

Writers: Kirsten Hofmockel, Paul Langan, David A. Randall, Daniel Segrè, and Minghua Zhang

Chapter 8: Emerging Technologies

Chair/Lead Writer: Andrzej Joachimiak

Co-Chair/Facilitator: Susannah Tringe

Writers: Kirsten Hofmockel, Paul Langan, David A. Randall, Daniel Segrè, and Minghua Zhang

Report Preparation

This report was edited and prepared for publication by the Biological and Environmental Research Information 

System group at Oak Ridge National Laboratory (Kris Christen, Holly Haun, Brett Hopwood, Sheryl Martin, 

Stacey McCray, Marissa Mills, Judy Wyrick, and Betty Mansfield).
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Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Biological and Environmental Research Advisory Committee November 2017
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Grand Challenges for Biological and Environmental Research: Progress and Future Vision

November 2017 Biological and Environmental Research Advisory Committee

Contents

Executive Summary .................................................................................................................................ix

Grand Challenges in Biological Systems Science...................................................................................................x

Grand Challenges in Earth and Environmental Systems Sciences........................................................................xi

Grand Challenges in Microbial to Earth System Pathways ................................................................................... xii

Grand Challenges in Energy Sustainability .......................................................................................................... xiii

Grand Challenges in Data Analytics and Computing............................................................................................ xiv

Supporting Grand Challenges Through the Use of Facilities, 

Research Infrastructure, and Emerging Technologies ......................................................................................... xiv

Common Actions................................................................................................................................................... xvi

1. Introduction............................................................................................................................................1

History of BER Science ...........................................................................................................................................1

	 Defining New BER Grand Challenges.....................................................................................................................2

2. Grand Challenges in Biological Systems Science.............................................................................3

Harnessing the Power of Systems Biology for Energy and Environment................................................................3

Building on Progress Related to the 2010 Grand Challenges .................................................................................6

Grand Challenge Research Recommendations ......................................................................................................9

3. Grand Challenges in Earth and Environmental Systems Sciences................................................21

Improving Predictive Understanding of Earth System Variability and Change ......................................................21

Building on Progress Related to the 2010 Grand Challenges ...............................................................................23

Grand Challenge Research Recommendations ....................................................................................................24

4. Grand Challenges in Microbial to Earth System Pathways.............................................................43

Determining the Pathways that Link Microbial Activities to the Earth System Scale .............................................43

Building on Progress Related to the 2010 Grand Challenges ...............................................................................44

Grand Challenge Research Recommendations ....................................................................................................46

5. Grand Challenges in Energy Sustainability......................................................................................57

Envisioning 20-Year, Resilient Energy Strategies..................................................................................................57

Building on Progress Related to the 2010 Grand Challenges ...............................................................................58

Grand Challenge Research Recommendations ....................................................................................................61

Discussion of Action Items.....................................................................................................................................67
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Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Biological and Environmental Research Advisory Committee November 2017

6. Grand Challenges in Data Analytics and Computing ......................................................................71

Ensuring that Multiscale Scientific Data Support Scientific Rigor, from Collection to Analysis..............................71

Building on Progress Related to the 2010 Grand Challenges ...............................................................................74

Grand Challenge Research Recommendations ....................................................................................................77

Discussion of Action Items.....................................................................................................................................80

7. User Facilities and Research Infrastructure .....................................................................................85

Providing Resources for BER Science ..................................................................................................................85

Building on Progress Related to the 2010 Grand Challenges ...............................................................................88

Applying User Facilities to BER Grand Challenges...............................................................................................89

Maintaining and Advancing Facility and Infrastructure Capabilities.......................................................................91

8. Emerging Technologies......................................................................................................................95

Developing Capabilities to Characterize and Represent Biological and Environmental Processes......................95

Building on Progress Related to the 2010 Grand Challenges ...............................................................................95

Emerging Technologies and the Importance of Integration Across Scales............................................................98

Appendices ............................................................................................................................................115

Appendix A: Charge Letter ................................................................................................................................117

Appendix B: Grand Challenges Workshop Agenda ...........................................................................................119

Appendix C: Grand Challenges Workshop Participants.................................................................................... 121

Appendix D: Fig. 2.2 Image Credits and Permissions ...................................................................................... 125

Appendix E: References ................................................................................................................................... 127

Appendix F: Acronyms and Abbreviations ........................................................................................................ 137
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Executive Summary

November 2017 Biological and Environmental Research Advisory Committee

Executive Summary

The Biological and Environmental Research 

(BER) program within the U.S. Department 

of Energy (DOE) Office of Science supports 

research focusing on the interconnections between 

energy production and the living environment. This 

fundamental research, conducted at universities, 

DOE national laboratories, and research institutions 

across the country, explores organisms and ecosystems that can influence the U.S. energy system 

and advances understanding of the relationships 

between energy and environment from local to 

global scales. 

Research within BER can be categorized into 

biological systems and Earth and environmental 

systems. Biological systems research seeks to characterize and predictively understand microbial 

and plant systems using genomic science, computational analyses, and experimental approaches. 

Foundational knowledge of the structure and 

function of these systems underpins the ability to 

leverage natural processes for energy production, 

including the sustainable development of biofuels 

and other bioproducts. Earth and environmental 

systems research seeks to characterize and predictively understand feedbacks between Earth and 

energy systems and includes studies on atmospheric physics and chemistry, ecosystem ecology 

and biogeochemistry, and development and validation of Earth system models extending from 

regional to global scales. To promote world-class 

research in these areas, BER supports user facilities 

that enable observation and measurement of atmospheric, biological, and biogeochemical processes 

using the latest technologies.

BER regularly solicits input from the scientific community to help guide its programs. The Biological 

and Environmental Research Advisory Committee 

(BERAC) is chartered under the Federal Advisory 

Committee Act to advise BER on its research portfolio and user facilities. To facilitate a synthesis of 

community input, the director of DOE’s Office of 

Science charged BERAC in March 2016 to review 

research progress and establish and deliver a revised 

long-term vision for BER by fall 2017. Questions 

considered during this process included:

• To what extent has BER successfully met the 

challenges outlined in the 2010 report, Grand 

Challenges for Biological and Environmental 

Research: A Long-Term Vision?

• What are the greatest scientific challenges that 

DOE faces in the long term (20-year horizon), 

and for which of these should BER take 

primary responsibility?

• How should DOE position BER to address 

these challenges?

• What new tools should be developed 

to integrate and analyze data from 

different disciplines?

• What unique opportunities exist to partner 

with, or leverage assets from, other programs 

within the DOE Office of Science?

• What scientific and technical advances 

are needed to train the future workforce 

in integrative science, including complex 

systems science?
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Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Biological and Environmental Research Advisory Committee November 2017

Through a series of BERAC meetings, white papers, 

and a research community workshop, BERAC 

addressed these questions, identifying future grand 

challenges in five areas: biological systems, Earth 

and environmental systems, microbial to Earth 

system pathways, energy sustainability, and data 

analytics and computing. Providing critical support for these challenges are BER user facilities, 

research infrastructure, and emerging technologies. This report represents a synthesis of these 

grand challenges and the supporting facilities 

and technologies.

Grand Challenges in 

Biological Systems Science

Biological systems naturally transform and distribute energy through photosynthesis and subsequent 

decomposition of photosynthetic material. Conversion of energy to biomass can occur via various 

mechanisms, including photosynthetic and chemical pathways with oxygen (i.e., aerobic) and without oxygen (i.e., anaerobic). Greater insights are 

needed into the regulation of these pathways, the 

genes responsible for the reactions, and environmental influences on the reactions. This improved 

understanding is a precursor to enabling changes in 

pathways that may uncover new or more efficient 

energy sources.

BER facilitates the understanding of complex biological systems by supporting research that develops 

and uses high-throughput genomic, proteomic, 

metabolomic, and computational approaches as well 

as more traditional advanced experimentation. In 

the past 10 years, the program has made significant 

progress in sequencing and synthesizing nucleic 

acids, rapidly generating high-quality omics datasets, 

advancing understanding of single-cell metabolism 

and mechanisms of cellular regulation, and devising approaches to manipulate cellular processes. 

Research has expanded systems approaches and 

model organism status to bacteria, fungi, and plant 

systems increasingly relevant to DOE missions. 

In these endeavors, BER has partnered with other 

DOE Office of Science user facilities (e.g., light 

and neutron sources) and with other federal agencies, including the U.S. Department of Agriculture, 

National Science Foundation, and National Institutes of Health, to leverage different areas of expertise across the government. 

Building on and emerging from these successes are 

a number of grand challenges for the next decade or 

longer including the following:

2.1 Understand the biological complexity of 

plant and microbial metabolism and interfaces across scales spanning molecules to 

ecosystems. 

2.2 Develop technologies to identify DOE 

mission–relevant metabolic capabilities 

and engineering possibilities in bacteria, 

fungi, archaea, viruses, plants, and mixed 

communities.

2.3 Optimize the use of large datasets that 

integrate omics surveys with biochemical 

Biological Systems Science 

Action Items

■ Conduct experiments that enhance 

cooperation among BER-supported 

user facilities and other DOE user facilities (e.g., DOE Nanoscale Science 

Research Centers). 

■ Lead coordinated efforts to improve and 

validate genomic annotation approaches.

■ Improve the performance of metabolomics 

approaches for BER-relevant science.

■ Establish standards across data platforms 

so investigators can efficiently link 

genomes with phenotypes. 

■ Coordinate and align research to understand dynamic linkages and feedbacks 

between environmental conditions and 

complex biological systems.
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Executive Summary

November 2017 Biological and Environmental Research Advisory Committee

and biophysical measurements to generate 

knowledge and identify biological paradigms.

2.4 Understand the links between genotype and 

phenotype in single but very diverse organisms and in communities of organisms that 

interact in terrestrial ecosystems.

2.5 Effectively exploit new and emerging technologies in systems biology and physical 

measurements (e.g., miniaturization) to 

accelerate biological discoveries.

Grand Challenges in Earth and 

Environmental Systems Sciences

Research on Earth and environmental systems 

includes terrestrial and atmospheric systems as 

they relate to Earth’s natural and human-driven 

energy systems. The study of environmental systems often includes biotic (living) and abiotic 

(nonliving) variables. Earth and environmental 

systems science seeks to understand and model 

the interactions among natural systems, human 

systems, and biotic and abiotic variables that 

comprehensively represent observed systems at 

local to global scales.

This research has advanced greatly over the past 

decade. Earth system models, of which climate is 

one component, have benefited from the use of 

high-performance computing. Moreover, targeted 

environmental science activities, particularly in 

the Arctic, have improved the understanding of 

ecosystem dynamics in traditionally understudied 

regions, thereby helping to reduce uncertainty in 

Earth system models. Particular successes include 

improved high-resolution aerosol and cloud modeling; advances in research on abrupt tipping points 

(e.g., thawing permafrost and melting sea ice); better 

coupling of human-based biogeochemical drivers 

with Earth system models; and quantification of 

model uncertainties. To make these advances, BER 

has partnered with other DOE Office of Science 

programs (e.g., Advanced Scientific Computing 

Research) and has coordinated research activities 

with other federal agencies through participation in 

the U.S. Global Change Research Program.

Earth and Environmental Systems Sciences Action Items

■ Develop fine-scale process models and data 

assimilation capabilities for data-model integration to advance model development and 

prediction of Earth system processes.

■ Advance high-resolution modeling in different simulation and prediction modes 

supported by exascale computing to improve 

understanding and prediction of extreme or 

high-impact events.

■ Develop a computational user facility for 

rapid design, generation, evaluation, and 

diagnosis of Earth system model simulations, as well as analysis of ensemble predictions and data-model synthesis.

■ Develop and maintain a hierarchy of models 

for hypothesis testing, model development, 

and uncertainty analysis of the complex 

human-Earth system.

■ Develop and integrate new sensing technologies and optimize field deployments in Atmospheric Radiation Measurement, AmeriFlux 

Network, Next-Generation Ecosystem 

Experiments, and other BER programs to 

explore interactions across different scales 

of biological organization and biosphereatmosphere feedbacks.

■ Create new integrated field laboratories 

that target biogeochemical, energy, and 

water flows between urban areas and 

surrounding ecosystems.
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Biological and Environmental Research Advisory Committee November 2017

From this work, new and future challenges include:

3.1 Advance Earth system modeling using a 

hierarchy of models, from process-resolving 

coupled models to reduced-order models, to 

transform understanding of the coupled Earth 

system and to produce useful and credible 

simulations and predictions of Earth system 

behavior at multiple time scales.

3.2 Establish new observational technologies 

and use them to understand human and 

Earth system processes, such as landatmosphere interactions, biogeochemical 

cycles, and subsurface soils, to estimate critical process parameters using novel analysis 

methods, such as machine learning and data 

science, and to quantify model errors.

3.3 Advance basic knowledge and scale-aware 

simulation capability for Earth system feedbacks associated with aerosols and moist 

processes to better quantify aerosol forcing, 

precipitation changes, and extreme events 

with consequences for energy and water 

cycles, global distribution of nutrients, and 

human health.

3.4 Advance modeling and understanding of 

important ecological, biological, and carbon cycle interactions and feedbacks in the 

climate system to identify potential tipping 

points and possible energy strategies.

3.5 Characterize, understand, and model the 

complex, multiscale water cycle processes in 

the Earth system including the subsurface to 

understand and predict water availability and 

human system response to extremes.

3.6 Understand the time-dependent processes 

and mechanisms associated with melting glaciers, ice caps, and ice sheets and their contributions to regional sea level rise.

3.7 Quantify the interplay between internally 

generated climate variability and externally 

forced response involving anthropogenic 

and natural factors and their relative roles in 

Microbial to Earth System 

Pathways Action Items

■ Develop and deploy sensors suitable for 

use in the field that integrate the effects of 

microbial activity at the ecosystem scale.

■ Promote integrated studies that explicitly 

test predicted microbial network interactions and attempt to assess membership 

and species-specific and collective functional capabilities within ecologically 

coherent microbial communities. 

■ Conduct experiments that help determine 

the influence of microbial processes at 

larger, aggregate scales. 

■ Promote research teams to integrate 

microbial community dynamics into Earth 

system models.

the time evolution of regional variability to 

understand predictability of the Earth system.

3.8 Understand the long-term Earth system 

stability in response to possible future Earth 

system outcomes and address the level of 

confidence and identify emergent constraints 

for the range of model projections.

Grand Challenges in Microbial 

to Earth System Pathways

Microbial communities influence soil and plant 

systems that in turn affect regional and global environments. However, research at one scale does not 

always translate to other scales because of challenges 

in data formats, relevance at different scales, and fundamental understanding of the links among scales. 

The 2010 BERAC Grand Challenges report indicated that BER was well positioned to undertake the 

grand challenge of coordinating research and analyzing results across scales. Since this time, advanced 

capabilities have enabled researchers to functionally 

and rapidly annotate microbial genomes using highthroughput technology and assemble microbial 
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Executive Summary

November 2017 Biological and Environmental Research Advisory Committee

communities in the laboratory to represent fundamental biotic interactions and systems-level 

processes. However, development of more complex 

model communities is needed to better capture 

relevant environments and associated biological 

processes at multiple scales. To address this need, a 

number of grand challenges were identified:

4.1 Characterize the biogeochemical exchanges 

driven by food web and plant-microbe interactions and evaluate their process-level impacts, 

sensitivity to disturbances, and shifting 

resource availability under changing environmental regimes.

4.2 Define the sphere of influence and key 

elements of microbial communities in 

space and time relevant for predicting 

larger-scale ecosystem phenomena for Earth 

system understanding.

4.3 Integrate molecular and process data to 

improve the ability to define ecologically significant traits of individual taxa and communities and use trait-based models to develop 

predictive links between community dynamics and ecosystem processes.

4.4 Align and deepen connections among 

conceptual understanding, measurements, 

and models related to the roles of microbes 

in determining the rate of transformation, 

uptake, and loss of chemical elements 

from ecosystems.

Grand Challenges in 

Energy Sustainability

Energy production and use are inherently connected to land, air, and water resources. Comprehensively understanding these interactions is 

therefore important for guiding current and future 

energy production that will optimize energy availability and environmental quality. In recent years, 

research on bioenergy conversion and associated 

environmental considerations has progressed 

substantially, accompanied by an increased understanding of energy-food-environment tradeoffs and 

improved characterization of spatial and temporal 

variabilities of targeted ecosystems. Other significant 

advances include further development of integrated 

assessment models, climate models, integrated Earth 

system models, and the coupling of these models 

where appropriate to fully address sustainability 

science questions. Moving forward, four grand challenges will take this research into the next decade 

and help resolve important questions:

5.1 Further develop the science of coupling 

energy, water, and land use across different 

spatial and temporal scales to understand 

environmental impacts and changing climate and to better predict net biogeochemical fluxes.

5.2 Use observational, experimental, and modelbased approaches to explore the sustainability of alternative energy systems, incorporating stability and resilience analysis, 

uncertainty, transition paths from current 

Energy Sustainability Action Items

■ Establish a strategically distributed network of energy sustainability testbeds 

for addressing crucial research questions 

associated with specific energy strategies 

and air-water-land feedbacks at multiple scales. The combined testbeds will 

address several grand challenges while 

leading to a predictive understanding of 

couplings between energy systems and 

natural systems.

■ Create an energy sustainability modeling 

and synthesis center for multidisciplinary 

teams to address key energy-water-land 

research challenges. The center will organize multidisciplinary teams to understand 

impacts (e.g., energy-water-land influences 

on energy infrastructure), sustainability 

working groups to resolve short-term 

research questions, and also facilitate and 

house integrated data products at resolutions needed for sustainability analyses.
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Biological and Environmental Research Advisory Committee November 2017

infrastructures, and the use of appropriate 

common metrics.

5.3 Understand how variability and change in 

natural systems affect energy system structure and function and determine how best to 

build this knowledge into models.

5.4 Create new data streams and more effectively use existing observations to ensure 

the availability of scale-appropriate data, 

particularly related to high-resolution land 

use, landscape infrastructure, demographic 

change, and energy-land-water research.

Grand Challenges in Data 

Analytics and Computing

Data challenges in BER research programs have 

increased by orders of magnitude over the past few 

years. New techniques and services are required to 

leverage the wealth of research results and transform them into world-leading scientific discoveries. Although the data differ in format, common 

approaches can be employed among BER programs 

for archiving, accessing, processing, and generating 

enhanced data products.

Over the past decade, further progress has been 

made in developing archives, developing software 

to handle large amounts of program-specific data, 

and linking BER researchers with high-performance 

computing capabilities. However, the growth in data 

size, complexity, and heterogeneity has far outpaced 

these efforts, and the challenges are continually 

escalating with the development of new instruments 

and sensors that stream data in real time. As a result, 

future grand challenges are expected to include:

6.1 Develop robust approaches for large-scale 

data collection, curation, annotation, 

and maintenance.

6.2 Develop computing and software infrastructure to enable large-scale data (i.e., Big Data) 

storage and analysis.

6.3 Conduct research to develop suitable algorithms and programming models that can 

harness current and future computer architectures to effectively model complex coupled systems and analyze extreme-scale data.

6.4 Engineer advanced computational modeling 

combined with data integration across temporal and spatial scales.

6.5 Conduct research and develop activities that 

support human understanding of large-scale, 

multimodal data streams, including the ability to steer experiments in real time.

Supporting Grand Challenges 

Through the Use of Facilities, 

Research Infrastructure, and 

Emerging Technologies

Dedicated facilities and new or improved technologies often can increase the speed and efficiency with 

which grand challenges are met. DOE, and BER 

specifically, invests in user facilities and the development and application of technologies to advance 

Data Analytics and Computing Action Items 

■ Prioritize building and maintaining an integrated facility for long-term data storage, 

archiving, and data analysis.

■ Evolve current and develop new ontologies 

and metadata standards, as well as strengthen 

compliance in funding announcements and 

reporting standards.

■ Facilitate and encourage increased interactions across disciplines (e.g., computer 

scientists, statisticians, data analysts, and 

experimentalists).

■ Foster coordination and collaboration within 

DOE and among other agencies.
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Executive Summary

November 2017 Biological and Environmental Research Advisory Committee

scientific understanding. As BER research challenges evolve, so too will capabilities within BER 

and Office of Science user facilities. Potential opportunities to further enable facilities and emerging 

technologies to support BER research and identified 

grand challenges are described here. 

Facilities and Research Infrastructure

BER fundamental science is empowered by BER, 

other Office of Science user facilities, and experimental and observational infrastructure that integrate different technologies to enable state-of-the-art 

research in plant, microbial, environmental, and 

Earth system sciences. User facilities are intended to 

provide unique research capabilities that are beyond 

the ability of single or small groups of investigators 

to manage for use by national and international 

communities. Distributed across the DOE complex, national user facilities provide a concentrated 

portfolio of research technologies, methodologies, 

instruments, and computational hardware that 

would be too expensive or sophisticated to duplicate 

in multiple locations. These facilities will continue to 

enable scientific breakthroughs in genomics, imaging, structural characterization, and atmospheric 

research only if their technologies are cutting edge. 

Efforts to improve current capabilities are thus 

critical, along with the continued development of 

advanced new technologies and their deployment 

in existing or new user facilities. These resources 

can enable key experiments needed to understand 

processes important to BER-aligned research efforts. 

Periodic assessments of the potential need for new 

or modified facilities to meet growing and changing 

BER research requirements are prudent.

Recommended improvements in capabilities 

and access to BER and Office of Science user 

facilities include:

7.1 Foster a spirit of collaboration to enable integrative capabilities among BER and Office of 

Science user facilities, as well as other federal 

research facilities and infrastructure, thereby 

promoting a fully interdisciplinary approach 

to BER-relevant science.

7.2 Solicit input from the BER research community regarding researchers’ needs and train 

them in new experimental, observational, 

and modeling approaches, thus propagating the knowledge and skills for generating 

high-impact scientific results.

7.3 Develop innovative enabling technologies 

and construct and acquire state-of-the-art 

instruments that exploit the world-leading 

characteristics of each user facility. This will 

boost capabilities for basic research in biological systems and Earth and environmental 

systems science, thereby providing DOE and 

the nation with leading-edge capabilities for 

biological and environmental science. 

7.4 Develop multimodal imaging and remotesensing capabilities at user facilities for interrogating length scales ranging from atomic 

to mesoscale and time scales ranging from 

nanoseconds to days.

7.5 Build upon existing investments and capabilities at the DOE Office of Science light and 

neutron science user facilities, continuing to 

align them with BER missions.

7.6 Further develop the necessary infrastructure 

at user facilities to study organisms in their 

natural habitats.

7.7 Develop and adopt technologies to convert 

genome sequence data into functional understanding at appropriate BER user facilities.

In addition to these recommendations, BERAC 

reviewed the grand challenges with respect to existing or potential links to BER and Office of Science 

user facilities and experimental infrastructure. A 

summary matrix of grand challenges and BER user 

facilities (see Table ES-1, p. xvii) indicates that 

(1) BER grand challenges require a diverse set of 

capabilities within user facilities; (2) coordination 

and partnerships across facilities are necessary for 

addressing complex modeling, data management, 

and data analysis priorities; and (3) some challenges 

are more readily met by existing user facilities than 
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Biological and Environmental Research Advisory Committee November 2017

others, potentially requiring additional consideration for filling existing capability gaps.

Emerging Technologies

Emerging technologies play a key role in state-ofthe-art research. Experimental and modeling needs 

can drive technology or software development. 

Conversely, existing technologies can be brought 

together in unique ways to explore existing or new 

research questions. Suggested development of technologies or capabilities include:

8.1 Characterize the genotype and phenotype 

of individual cells, including genomics, transcriptomics, proteomics, and metabolomics, 

to enable high-resolution predictive biology. 

8.2 Increase throughput and integration of 

genomics, transcriptomics, proteomics, and 

metabolomics to enable improved translation from the molecular to cellular realm. 

8.3 Characterize key processes of aerosols, 

clouds, turbulence, atmospheric convective 

systems, and their interactions to enable 

better modeling of water, carbon, and energy 

cycles of the Earth system and predictions of 

future climate change and extremes in atmospheric, terrestrial, and watershed events. 

8.4 Integrate data covering broad time and 

length scales—from seconds to years 

and from Ångströms to the Earth scale—

to enable multiscale comprehension 

and simulation. 

8.5 Develop integrative and interpretive computational approaches that can handle large, 

disparate data types from multiple and heterogeneous sources using advanced and exascale computing. 

8.6 Explore the development and application of 

nanoscience and nanotechnology to further 

understand biological systems at nanoscales 

or larger scales (e.g., nanosensors).

8.7 Explore the development and application 

of microsatellites and unmanned aerial 

systems as mechanisms for observation of 

atmospheric and terrestrial variables.

Common Actions

Understanding of the living environment and its 

association with energy cycles, production, and use 

continues to develop over time, along with new capabilities and technologies. Since the previous review 

of BER grand challenges in 2010, biological and environmental sciences have progressed substantially. 

For example, scientific understanding of single-cell 

metabolism, microbial communities, and associated 

systems-level processes has advanced significantly, 

as has the use of bacteria, fungi, and plants as model 

systems to address DOE missions in energy production and use. Concurrently, targeted experiments 

in understudied regions (e.g., the Arctic) have filled 

knowledge gaps in Earth system models. These 

models have been coupled with integrated assessment models, and their complexity and resolution 

have increased through the use of high-performance 

computing. The interaction between biological and 

environmental sciences also has fostered progress in 

bioenergy conversion, energy-environment interactions, and increased capabilities to address sustainability science questions.

With increased understanding of research areas 

comes increased complexity and the need to integrate and link data streams and model parameters 

that help build predictive capabilities. This need was 

reiterated across identified grand challenges, along 

with other common actions that would facilitate 

progress on BER grand challenges. Common actions 

identified among grand challenges include:

• Promote integrative science across BER programs, such that research disciplines benefit 

more from one another.

• Establish standards for data and metadata across 

platforms and research disciplines.

• Identify and integrate key components 

(e.g., molecules, microbial processes, and Earth 
xvii

Executive Summary

November 2017 Biological and Environmental Research Advisory Committee

* See Chapter 7 for an expanded table that includes other Office of Science user facilities and other BER research 

infrastructure.

** See text in respective chapters for descriptions of individual grand challenges.

*** Existing capabilities and partial capabilities that could be leveraged for the grand challenges are denoted by

l and m, respectively.

Table ES-1. Capability Assessment of Biological and Environmental Research (BER) 

User Facilities to Address BER Grand Challenges*
xviii

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Biological and Environmental Research Advisory Committee November 2017

system parameters) that are most significant and 

impactful to DOE missions. 

• Identify and develop links among different scales 

of analysis to obtain more comprehensive representation of individual and system dynamics.

• Increase predictive capabilities of Earth 

and environmental systems and biological 

systems research.

• Explore the use of remote sensors at different 

scales and within different disciplines to promote greater understanding, measurement, and 

evaluation of research findings.

• Promote interactions among researchers that 

more strongly link experiments, scientific theories, observation infrastructure, and modeling.

These common actions resonated across science focus areas and therefore were suggested as 

cross-cutting activities for BER (see Fig. ES-1, 

this page). The ability to conduct complementary 

and integrative research across different scales of 

space and time, along with understanding complex 

systems for predictive purposes, is a strength that 

can be found within the combined research and 

facilities network of DOE’s Office of Science. The 

research challenges and suggested actions documented in this report are intended to help guide 

BER over the next decade or more. The information 

provided herein will need to be balanced with DOE 

mission objectives, research diversity, and available 

funding to determine which challenges are ultimately met. Also anticipated is that any implementation of these challenges would require additional 

planning and organization through workshops and 

research community engagement, along with continued partnerships and coordination with other 

federal programs.

Fig. ES-1. Current Biological and 

Environmental Research (BER) program research focuses on biological 

systems science and Earth and environmental systems science, which 

can be used to address integrative 

research topics including energy 

sustainability. In addition to these 

core research areas, the Biological and Environmental Research 

Advisory Committee has identified 

grand challenges in conducting 

research across scales, developing 

predictive capacities, and further 

developing computational and 

technological capabilities in support 

of BER research. U.S. Department of 

Energy facilities and research infrastructure provide a foundation for 

state-of-the-art, complex research.
November 2017 Biological and Environmental Research Advisory Committee 1

Chapter 1 — Introduction

Introduction 1

The Biological and Environmental Research 

(BER) program within the U.S. Department of Energy (DOE) Office of Science 

supports transformative science and scientific user 

facilities to achieve a predictive understanding 

of complex biological, Earth, and environmental 

systems for energy and infrastructure security and 

resilience. This research, conducted across DOE 

national laboratories, universities, and research 

institutions, focuses on interconnections between 

energy and the environment. The program aims to 

understand fundamental biological, biogeochemical, and physical principles to be able to predict 

processes occurring at scales ranging from the 

molecular and genomics-controlled smallest scales 

to environmental and ecological processes at the 

scale of planet Earth.

Starting with the genetic information encoded in 

organisms’ genomes, biological research, housed 

within BER’s Biological Systems Science Division, 

seeks to discover the principles that guide translation of the genetic code into the functional proteins 

and metabolic and regulatory networks underlying 

the systems biology of plants and microbes as they 

respond to and modify their environments. This 

predictive understanding will enable the design and 

re-engineering of microbes and plants for improved 

energy resilience and sustainability, including 

advanced biofuels and bioproducts, enhanced 

carbon storage capabilities, and controlled biological transformation of materials such as nutrients 

and contaminants in the environment. Earth and 

environmental systems research, housed within 

BER’s Climate and Environmental Sciences Division, advances fundamental understanding of the 

dynamic, physical, and biogeochemical processes 

required to systematically develop and validate 

Earth system models (ESMs) that integrate across 

the atmosphere, land masses, oceans, sea ice, and 

subsurface. These ESMs are required for predictive tools and approaches needed to inform future 

energy and resource needs. To facilitate world-class 

research in these areas, BER also supports three user 

facilities—Joint Genome Institute, Environmental 

Molecular Sciences Laboratory, and Atmospheric 

Radiation Measurement Research Facility—that 

enable observations and measurements of biological, biogeochemical, and atmospheric processes 

using the latest technologies.

History of BER Science

BER has a long and productive history of research 

dating back to the Atomic Energy Act passed by 

Congress in 1946, which established the Atomic 

Energy Commission (AEC). A mission of AEC, 

which became the Department of Energy in 1977, 

was to understand the effects of radioactive material on human health and other living matter. This 

endeavor led to research on plants, ecosystems, 

atmosphere, and biology from cellular to organismal 

levels to understand the impacts of radiation exposure in the early period of the Cold War. From this 

research, atmospheric transport models evolved, 

which later developed into climate models and then 

the ESMs in use today. Likewise, BER research on 

understanding DNA replication during cell division 

developed into the international Human Genome 

Project and launched a new era of modern biotechnology and genomics-based systems biology. The 

significant and substantial impact that BER has had 

on the fundamental understanding of biotic and 

abiotic processes at the organismal to Earth scale has 
2 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

been profound, and it was made possible through 

long-term planning and investment in fundamental 

research, as well as coordination with other U.S. governmental agencies, universities, and industry.

Defining New BER Grand Challenges

Input from the science community to the planning process is essential for guiding world-class 

research. Such scientific input to BER is provided 

throughout each annual funding cycle via conferences, workshops, principal investigator meetings, 

program reviews, and proposal panel reviews. BER 

also receives mission guidance via the Executive 

Administration’s Office of Science and Technology 

Policy and Congress, either directly or via multiagency administrative working groups and congressional committees.

To synthesize the scientific input for identification 

of long-term grand research challenges, the Biological and Environmental Research Advisory Committee (BERAC) convened a subset of the community 

in 2009 to review the status of research related to 

BER and recommend future research challenges 

that BER may consider. A report from this effort 

was published in 2010 (BERAC 2010). BER has 

accomplished much since then, and the research 

challenges have changed based on the progression 

of research and technology. As a result, the director of DOE’s Office of Science charged BERAC in 

March 2016 to review BER’s progress and establish 

a revised long-term vision for delivery in fall 2017 

(see Appendix A: Charge Letter, p. 117; Appendix B: Workshop Agenda, p. 119; and Appendix C: 

Workshop Participants, p. 121).

Questions considered during this process included:

• To what extent has BER successfully met the 

challenges outlined in the 2010 report, Grand 

Challenges for Biological and Environmental 

Research: A Long-Term Vision?

• What are the greatest scientific challenges that 

DOE faces in the long term (20-year horizon), 

and for which of these should BER take primary 

responsibility?

• How should DOE position BER to address 

these challenges?

• What new tools should be developed 

to integrate and analyze data from 

different disciplines?

• What unique opportunities exist to partner 

with, or leverage assets from, other programs 

within the DOE Office of Science?

• What scientific and technical advances 

are needed to train the future workforce 

in integrative science, including complex 

systems science?

This report represents a synthesis of future grand 

challenges related to these questions and is organized with respect to the following topic areas: 

Biological Systems Science, p. 3; Earth and Environmental Systems Sciences, p. 21; Microbial to Earth 

System Pathways, p. 43; Energy Sustainability, p. 57; 

Data Analytics and Computing, p. 71; and the role of 

user facilities and research infrastructure, p. 85, and 

emerging technologies, p. 95, in supporting these 

grand challenges.
November 2017 Biological and Environmental Research Advisory Committee 3

Chapter 2 — Biological Systems Science

Grand Challenges in 

Biological Systems Science

Harnessing the Power of Systems 

Biology for Energy and Environment

Over the past 10 years, the U.S. Department of 

Energy’s (DOE) Biological and Environmental Research program (BER) has led the scientific community in implementing high-throughput 

approaches to understand the complexity and 

responses of microbial and plant communities. These 

approaches are directed toward DOE missions of 

advancing biofuels generation and understanding 

terrestrial carbon cycling. Four major themes emerged 

from the 2017 Grand Challenges workshop that will 

guide current and future investigations in systems 

biology over the next 10 years: (1) addressing the 

challenge of biological complexity, (2) managing and 

optimizing large-scale data (i.e., Big Data), (3) intensively studying system interfaces, and (4) widening 

the range of organisms targeted for BER research. 

These themes are represented in a number of grand 

challenges and recommended action items for biological systems (see Biological Systems Science Summary 

of Grand Challenges and Action Items, p. 4).

Embracing Biological Complexity

Although a significant challenge, understanding 

the complexity of molecular interactions, cellular 

Overarching Grand Challenge and 20-Year Vision

Microbial cells, plants, communities, and ecosystems are complex, diverse, and have highly 

interactive components and, hence, are best studied as systems and at a depth that provides 

information to support predictive capabilities. Researchers must embrace the complexity of these 

systems and employ intensive investigation at the interfaces of system components.

2

regulation, and fine-tuning of metabolism in single 

cells interacting with their environment will be 

important in developing manageable and tunable 

cell lines for energy generation and development of 

a bioeconomy. Future studies will include natural 

and synthetically engineered systems using combinations of high-throughput sequencing, omics tools, 

and synthetic biology, together with advanced physical and biochemical measures.

The Biological and Environmental Research Advisory Committee (BERAC) identified biological 

complexity as a grand challenge in its 2010 Grand 

Challenges report (BERAC 2010), and biological 

complexity is expected to remain a challenge over 

the next 2 decades. One aspect of the tremendous 

recent progress is that the full extent of biological 

complexity is coming into sharper focus, primarily 

resulting from the application of new technologies 

and increased abilities toward understanding (i.e., 

comprehending) the large amounts of data being 

generated. However, even with this progress, capabilities for integrating, synthesizing, and applying 

this knowledge to enable “predictive biology”—

BERAC’s overarching vision for systems biology—

clearly are still in a very early stage.
4 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

The overarching theme of BER systems biology efforts is to enable predictive biology. To be predictive, not 

only do major elements (e.g., regulatory molecules, pathways, and keystone organisms) and their functions 

(i.e., the “parts list”) need identification, but also needed is the understanding of their interactions and how 

those interactions are regulated at the interfaces. These interactions must be understood well enough to 

enable predictions of their responses under different environmental perturbations (e.g., pH, ionic strength, 

water, nutrients, and temperature). The immense difficulty with this theme is that environmental conditions 

are diverse, complex, and interactive in their own right, and they elicit major reactions and responses (often 

conflicting) from single microbes, microbial populations, communities, and terrestrial ecosystems. With 

a long-term vision of enabling predictive biology, investigating these biological systems across multiple 

environmental scenarios and scales is crucial; gleaning only the potential responses to stimuli is not sufficient.

The five BER grand challenges for systems biology, outlined here, are described in more detail in the Grand 

Challenge Research Recommendations section, p. 9.

2.1. Understand the biological complexity 

of plant and microbial metabolism and 

interfaces across scales spanning molecules 

to ecosystems.

2.2. Develop technologies to identify DOE 

mission–relevant metabolic capabilities 

and engineering possibilities in bacteria, 

fungi, archaea, viruses, plants, and 

mixed communities.

2.3. Optimize the use of large datasets that integrate 

omics surveys with biochemical and biophysical 

measurements to generate knowledge and 

identify biological paradigms.

2.4. Understand the links between genotype 

and phenotype in single but very diverse 

organisms and in communities of organisms 

that interact in terrestrial ecosystems.

2.5. Effectively exploit new and emerging 

technologies in systems biology and physical 

measurements (e.g., miniaturization) to 

accelerate biological discoveries.

To begin addressing these grand challenges, the 

community identified five immediate action items.

• Conduct experiments that enhance 

cooperation among BER-supported user 

facilities and other DOE user facilities (e.g., DOE 

Nanoscale Science Research Centers).

• Lead coordinated efforts to improve and 

validate genomic annotation approaches.

• Improve the performance of metabolomics 

approaches for BER-relevant science.

• Establish standards across data platforms so 

investigators can efficiently link genomes 

with phenotypes.

• Coordinate and align research to understand 

dynamic linkages and feedbacks between 

environmental conditions and complex 

biological systems.

Summary of Grand Challenges and Action Items

Biological Systems Science
November 2017 Biological and Environmental Research Advisory Committee 5

Chapter 2 — Biological Systems Science

Managing and Optimizing Big Data

Integrated studies resulting in large datasets and 

computer-intensive analyses are posing a Big Data 

crisis because generating data is easier than inferring 

functional activities or deriving knowledge from 

the integration of disparate datasets. Innovative 

approaches are needed to organize, decipher, and 

annotate datasets and make them publicly available 

to the research community. This effort will require 

advances in computational capabilities, as well as 

experiment-based provision of robust and accurate 

functional inferences that provide the foundation 

for predicting and assigning molecular functions to 

genes and their proteins.

The transformation of biology from an observational 

to a data-intensive predictive and computational 

science is accelerating, raising numerous challenges. 

Among them are the processing of Big Data and 

completeness of the data being generated, as well as 

their veracity and reproducibility. The utility (e.g., 

ease of use and cost) of many high-throughput technologies rapidly has increased to the point that they 

are employed almost universally, with a concomitant 

increase in required data generation and analysis. 

However, not all technologies are advancing at the 

same high rate. Improvement in metabolomics, for 

example, is needed to ensure that the provided coverage approaches that of other technologies (e.g., 

transcriptomics). The lack of completeness among 

the various datasets generated, as well as questions 

of spatial and temporal scales in biological systems, 

largely limits the ability to integrate dissimilar data, 

making them much less useful for generating new 

knowledge. These are all critical issues with regard to 

integrating and, ultimately, deploying this information to support BER’s mission areas (see also Chapter 6 on Data Analytics and Computing, p. 71.)

Investigating System Interfaces

Understanding complex microbial cells, plants, communities, and ecosystems at a depth that enables 

predictive capabilities requires intensive investigation at system interfaces. This research spans 

interfaces across vast scales of interaction: (a) at the 

molecular and cellular scale between types of molecular measurements, between molecules (i.e., genes, 

proteins, enzymes, and substrates), and between 

cellular structures; (b) at the cell-environment and 

cell-cell scales, including environmental and interorganism signaling and regulation; and (c) at the 

organism-organism scale through tracking the metabolic interplay among different organisms (e.g., 

bacteria, fungi, and plants) that regulate cascades 

of metabolic responses to changing environments. 

Synthetic biology would be used to generate and test 

hypotheses based on this data collection to make 

informed predictions (see sidebar, Synthetic Biology, this page).

Synthetic Biology

As defined in the 2010 Grand Challenges report, 

synthetic biology is the design and construction 

of new biological parts, devices, and systems; the 

redesign of existing, natural biological systems for 

useful purposes; and the inspiration for new materials 

and processes derived from the design, functions, 

and products of living cells. While systems biology 

generates hypotheses based on data collection over 

the whole system, it also enables testing of these 

hypotheses by direct manipulation of the system’s 

parts. Like many of the most exciting advancements 

in science, synthetic biology arose at the interface of 

converging disciplines, specifically engineering and 

biology. Indeed, one aspect of synthetic biology is 

the application of engineering design principles to 

biological systems. Although these aspects were the 

drivers for its development, synthetic biology is quickly 

outstripping its beginnings and developing into a 

powerful technology that will transform biology, with 

profound carry-over effects to society in general.
6 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Expanding Research Beyond 

Model Organisms

Much information has been generated from a few 

model organisms over recent years. However, BER 

research is branching out to multiple, very different organism hosts for biochemical synthesis and 

process management. The emphasis is turning to 

bacterial, algal, and fungal hosts from soils and 

other environments that have innate abilities to 

withstand harsh chemical environments and have 

high-efficiency carbon substrate decomposition 

abilities (e.g., biofuels research). These hosts often 

are more difficult to grow and manipulate in the laboratory and require design and development of new 

engineering and metabolic regulation approaches.

In addressing climate change response and feedbacks, filamentous fungi are critical for managing the 

direction and flow of carbon in the terrestrial environment, but detailed knowledge of their metabolism is lacking. For both these applications, the 

understanding of plant metabolism, breeding and 

maintenance of introduced traits, and interactions 

with bacteria and fungi is critical. Extrapolating metabolic paradigms from model bacterial systems is not 

possible, and investigations must focus on organisms 

that are actually in environments of DOE mission 

interest. At the same time, an emphasis should be 

placed on leveraging the foundational knowledge 

that can be gained quickly with laboratory-deployed 

organisms, maximizing the implementation of this 

knowledge to accelerate understanding and guide 

design of experimentation in environmental strains. 

To maximize the knowledge gained, needs include 

prioritizing organisms by mission relevance and 

sharing information across laboratories.

In summary, BER has led and will continue to lead 

the development and application of systems biology and synthetic biology to address critical needs 

regarding adaptation to environmental change, 

enhancing energy sustainability and contributing to 

the developing bioeconomy. Ultimately, the aim is to 

contribute significantly to the growth of the bioeconomy. These contributions include defining alternative pathways to energy sustainability for informing 

the design of future energy systems to answer the 

question: Can energy pathways be identified that 

provide optimal economic and environmental outcomes that promote efficient energy production and 

human well-being?

Building on Progress Related 

to the 2010 Grand Challenges

Systems biology has seen significant progress in 

four major areas: (1) sequencing nucleic acids and 

increasing the ability to rapidly generate high-quality 

omics datasets; (2) advancing understanding of 

single-cell metabolism and disentangling the mechanisms of cellular regulation; (3) devising clever 

schemes to manipulate key cellular processes in 

single organisms for detection and synthesis (i.e., 

synthetic biology, see sidebar, p. 5); and (4) expanding systems biology approaches to tackle more 

DOE mission–relevant bacteria, fungi, and plants as 

model systems.

Expanded Sequencing and Omics Approaches.

Since the 2010 Grand Challenges report, the ability 

to sequence DNA, RNA, and proteins and generate synthetic molecules has greatly expanded (see 

also Chapter 8 on Emerging Technologies, p. 95). 

Data quality and quantity have both increased, 

and new research approaches and tools have been 

developed (see Fig. 2.1. Dramatic Increase in 

Genome Sequencing Throughput, p. 7). Rapidly 

maturing sequencing subdisciplines are metagenomics, metatranscriptomics, and metaproteomics, 

with publications now appearing that examine a 

variety of natural environments, in addition to the 

well-publicized efforts on animal and human health. 

In general, however, all these efforts remain at the 

level of phenomenology, lacking the detailed, mechanistic information needed to develop predictive 

models of such systems, especially those that link 

across scales and predict community functions and 

processes under changing conditions.

Advanced the Understanding of Single-Cell 

Metabolism and Cellular Regulation. The 2010 

report called for research at the single-cell level, and 

scientists have achieved significant success with a 
November 2017 Biological and Environmental Research Advisory Committee 7

Chapter 2 — Biological Systems Science

few model systems. Over the past few years, there 

have been great advances in implementing computationally useful and biologically sound representations of structured metabolic networks in several 

model organisms at the whole-cell level. In parallel, 

the growth and increased accuracy of transcriptomic and proteomic datasets have advanced understanding of regulatory networks, potentially adding 

to the scaffold of genome-scale representations 

provided by metabolic stoichiometry. However, full 

integration of metabolic and regulatory networks 

remains an open and significant challenge, with the 

incorporation of kinetic information into genomescale models even more challenging. In addition, 

incorporating the effects of enzyme promiscuity 

and cell-to-cell metabolic variability into models 

remains a key goal.

Moreover, genome annotation (i.e., a detailed 

characterization of gene function for all genes in 

the genome) remains a limiting factor in the construction of genome-scale models. Challenges in 

associating genes with metabolic functions include 

the capacity to obtain enough metabolomic datasets 

and interpret their complexity. In addition, molecular complexes are important organizational units 

in cells, but their structures and functions are not 

yet understood. The ability to derive initial models 

based on genomic data has advanced greatly. Yet, 

the persisting challenge is to integrate multiple data 

types and multiple levels of biological description 

(i.e., kinetic, metabolic, and probability models 

for cellular phenotypes) into unified mathematical 

frameworks. More effort will be needed to establish 

The DOE JGI supports short- and longread sequencers, where a read refers to 

a sequence of DNA bases. Short-read 

sequencers produce billions of 300-base 

reads used for quantification such as 

in gene expression analysis. Long-read 

sequencers currently average 12,000-base 

reads and are used for de novo genome 

assembly.

Sequence Output

(in billions of bases or GB) 

Massively Parallel Short-Read Single Molecular Long-Read 

Sequencing Sequencing

2012 2014 2013 2015 2016

210

492

596

1,470

1,907

2008 2009 2010 2011 2012 2014 2013 2015 2016

62 1,004

6,041

55,905

70,370

30,000

100,013

141,707 139,964

Fig. 2.1. Dramatic Increase in Genome Sequencing Throughput. Over the past decade, new technologies have enabled an 

exponential increase in the generation, analysis, and comparison of sequencing datasets at the U.S. Department of Energy’s 

Joint Genome Institute (JGI) and other sequencing centers. Today, JGI generates petabytes of high-quality sequence and 

analysis data. [Image courtesy JGI]
8 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

pipelines for facilitating data collection and integration toward automated model construction.

Challenges also remain in the area of metabolism, 

where little progress has been made toward integrating data across scales (e.g., up to the population 

level). Also lacking is significant progress in the 

ability to analyze single cells in situ in natural environments and to determine in vivo kinetics in cells. 

However, there is currently more recognition of subcellular compartmentalization and cell type variation within a microbial population or within a plant.

Improved Biosystems Design Approaches and 

Tools. Elegant schemes devised for the discovery 

and manipulation of regulatory elements are revolutionizing systems biology and enabling the design 

of synthetic metabolic systems. One such scheme 

involves clustered regularly interspaced short palindromic repeats, or CRISPRs (see sidebar, A New 

Era in Precise Genome Editing for Synthetic Biology, this page). Also undergoing great strides is the 

design of cells and systems as sensors, with a primitive proof of concept indicating the feasibility and 

A New Era in Precise Genome Editing for Synthetic Biology

One of the exceptional benefits of conducting wholegenome sequencing is the potential to discover 

new regulatory systems in biological organisms that 

would be missed with single-gene approaches. Since 

the 2010 Grand Challenges report (BERAC 2010), a 

technology based on clustered regularly interspaced 

short palindromic repeats (CRISPRs) has emerged 

from discovery, through genetic manipulation, to 

widespread use as a tool for generating organisms 

with desired metabolic properties (Horvath and 

Barrangou 2010; Wiedenheft et al. 2012; Lander 2016). 

CRISPRs originally were identified by their pattern 

of repeats in metagenomic datasets before their 

functions were known (Bolotin et al. 2005; Mojica and 

Montoliu 2016; Pourcel et al. 2005). Associated with 

CRISPRs is an RNA-guided protein named Cas (CRISPRassociated endonuclease). Cas is a molecular scissor 

that uses the spacer repeats in CRISPRs to recognize 

and cut exogenous DNA. CRISPR-Cas systems were 

first identified in bacterial and archaeal systems 

where they are now known to function as an acquired 

immune system against phages. More recently, these 

systems have been found in eukaryotes. Modifications 

have led to the CRISPR-Cas9-guide RNA (gRNA) 

system, which edits genomes at specific sites, inserting 

a target gene into the genome at a precise location 

to replace a natural gene (Cong et al. 2013; Mali et 

al. 2013). Widely applicable, this technology, when 

multiplexed and democratized, can work successfully 

with DNA from microorganisms (e.g., bacteria and 

archaea), model eukaryotic animals, plants, and 

humans. Since 2013, use of the CRISPR-Cas system has 

grown exponentially. Its successful implementation 

in a wide variety of organisms has revolutionized the 

ability to engineer organisms with desirable metabolic 

properties. Current applications include microbial 

engineering, plant and crop breeding, and human and 

animal gene therapy treatments.

reality of metabolic engineering. This area should 

continue to advance quickly, combining computational protein structure prediction and design 

algorithms, new molecular engineering techniques, 

and the collection of in-depth datasets containing 

cellular performance and processes. In addition to 

data collection and integration, advances in synthetic biology, such as the ubiquitous implementation of gene editing using CRISPR, are opening new 

avenues for systems biology research—enabling 

the engineering of in silico and in vivo mutants with 

increasing efficiency and the testing of model capacities to capture the phenotypic effects of genetic 

perturbations.

Advanced Engineering and Study of HigherOrder Organisms. Expansion of systems biology to 

a wider variety of DOE mission–relevant organisms 

is needed to fulfill DOE goals (U.S. DOE 2015). 

Eukaryotic organisms such as fungi and plants are 

key players in the DOE missions of biofuels generation and understanding of global carbon cycling. 

Since the 2010 Grand Challenges report, significant advances have been made in studying and 
November 2017 Biological and Environmental Research Advisory Committee 9

Chapter 2 — Biological Systems Science

engineering the metabolism of fungi and plants. 

The cellular and tissue structures inherent in these 

higher-order organisms add to their complexity, 

making those structures even more difficult to understand, regulate, and predict than those in bacteria. 

With baselines now established, disentangling the 

many levels of metabolism, regulation, and communication within and among eukaryotic cells and 

tissues may be possible.

Grand Challenge Research 

Recommendations

Grand Challenge 2.1 

Understand the biological complexity of plant and 

microbial metabolism and interfaces across scales 

spanning molecules to ecosystems.

Predictive Modeling for Complex Systems

Genomes, transcriptomes, and proteomes contain 

the full instruction sets for living organisms, but 

epigenetic processes can modify phenotypic expression. Significant progress over the past 7 years has 

shown that metabolism in single-cell systems may 

be understood well enough to enable engineering 

and manipulation toward production of desired 

industrial products or phenotypic characteristics 

(e.g., zip-lignin). Major challenges are to assemble 

these simple systems into more complex systems 

more closely resembling reality and to predict their 

performance by integrating modeling scenarios into 

biochemical pathways. Genome-scale predictive 

modeling should be designed for easy extension 

to other organisms, as well as cellular assemblies 

and communities of DOE mission relevance (see 

Fig. 2.2. Overview of the Length and Time Scales 

Spanning Biological Systems Science, p. 10).

Research Needs and Knowledge Gaps

Dynamically Perturbing Systems and Integrating Results with Measurements, Modeling, and 

Diverse Data Types. Predictive systems-level 

modeling will require tight integration of theoretical 

studies, computer simulations, and experimental 

measurements that include both omics and imaging metrics. As researchers move from genotype 

to phenotype, obtaining robust, quantitative data 

still poses technical challenges. Although genomic, 

transcriptional, and flux data could provide the basis 

for genome-scale quantification of an organism’s 

reaction rates, the integration of other layers of biological complexity is needed for accurate description 

of cellular functions and communication between 

organisms. The capacity to quantitatively predict 

functions in biological systems is not limited to 

static snapshots. To enable accurate understanding 

of dynamic processes, tools are needed to precisely 

probe systems in temporally or spatially controlled 

ways. Tools that introduce perturbations, such as in 

gene expression or signaling, can be coupled with 

measurements of outcomes at the transcriptional, 

translational, and phenotypic levels. Well suited to 

enable model validation, these methods, with precise perturbations introduced, can test, refine, and 

revise modeling predictions. Examples of tools that 

enable perturbations applicable to systems biology 

include optogenetics, small-molecule inducers, and 

microfluidics. Overall, the ability to precisely and 

dynamically perturb a system is invaluable for model 

validation and will move research beyond a static 

understanding of cellular processes.

Understanding the Role of Membrane Channels 

and Transport Proteins in Cellular Function. An 

additional aspect of biological complexity that must 

be explicitly introduced in computational descriptions is the hierarchical compartmentalization of 

systems and subsystems. Biological compartmentalization plays a key role in controlling the flow of 

molecules and metabolites, whether from outside to 

inside a prokaryotic cell or between compartments 

within a eukaryotic cell to form the basis for energy 

flow within living systems. Though lipids form the 

matrix of these compartmental barriers, the flow of 

compounds and ions is mediated mostly by key proteins that vary in nature from simple small-molecule 

channels to multicomponent protein assemblies. 

Because integral membrane proteins are difficult 

to purify and analyze, they are among the least well 
10 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

characterized protein complements of intra- and 

intercellular transporters. The serious knowledge 

gap involving the biochemistry and physiological 

functioning of these membrane proteins must be 

filled to create models that can robustly predict cellular behavior. Thus, a key component of this grand 

challenge is to understand contributions to cellular 

functions made by membrane channels and transporter proteins.

Expanding Single-Species Experiments and 

Modeling to Multicellular Systems and Communities. In addition to performing experimental 

measurements, constructing models, and integrating 

data and models at the single-species level, expanding the current approaches for these activities will 

be crucial for studying multicellular systems and 

microbial communities in complex environments. 

This expansion will require establishing clear strategies for combining single-organism models into 

ecosystem-level models and testing predictions at 

different levels. For example, essential capabilities 

will include those to measure both intracellular 

fluxes and metabolic exchanges between different 

organisms. Because the ecological time scales often 

overlap with evolutionary time scales, model design 

should enable simulations of evolutionary processes, 

Fig. 2.2. Overview of the Length and Time Scales Spanning Biological Systems Science. Biological systems research 

includes complex processes that span a size range from Ångströms to a meter and a dynamic range from femtoseconds 

to a week. Representative processes and their elements are illustrated. Key: Å, Ångström; cm, centimeter; fs, femtosecond; 

m, meter; mm, millimeter; min, minute; μm, micron; μs, microsecond; ms, millisecond; nm, nanometer; ns, nanosecond; ps, 

picosecond; s, second; sub-ps, subpicosecond. [Figure from BER. 2017. Technologies for Characterizing Molecular and Cellular 

Systems Relevant to Bioenergy and the Environment, DOE/SC-0189, U.S. Department of Energy Office of Biological and Environmental Research. See Appendix D, p. 125, to view credits and permissions of individual images.]
November 2017 Biological and Environmental Research Advisory Committee 11

Chapter 2 — Biological Systems Science

from standard mutation and selection dynamics to 

horizontal gene transfer among species. Extension 

of current models along these directions will benefit not only from the aforementioned coupling of 

computer modeling and experimentation, but also 

from the exploration of completely novel theoretical frameworks. This new knowledge could draw 

from the experience of diverse quantitative sciences, 

including theoretical ecology, statistical physics, and 

applied mathematics.

Grand Challenge 2.2 

Develop technologies to identify DOE mission–

relevant metabolic capabilities and engineering 

possibilities in bacteria, fungi, archaea, viruses, 

plants, and mixed communities.

Leveraging Advances in Synthetic Biology

As a technology, synthetic biology offers dramatic 

potential to reform the developmental processes of 

research related to biological systems design and 

analysis. Up to this point, biology has been limited 

by what exists in the natural world. For example, 

expansive sequencing is now providing a reservoir 

of sequenced genomes, proteins, and regulatory 

elements, as well as the potential to assemble them 

in different orders to design new synthesis pathways. However, advances in recoding of microbial 

genomes, greater understanding of protein structure, and sophisticated gene design tools all have 

the potential to create innovative, de novo pathways, 

structures, and molecules beyond those currently 

existing in nature—truly transformational ideas 

and methods. BER is well positioned to capitalize 

on these revolutions given its strong background 

and capabilities in synthetic biology, working 

with diverse organisms, and exceptional genomic 

sequencing. Following are descriptions of several 

specific opportunity areas in which synthetic biology can contribute to BER-relevant research.

Research Needs and Knowledge Gaps

Deploying Synthetic Biology to Efficiently Produce Products. Bacteria, fungi, algae, and plants 

can be engineered to produce biofuels and bioproducts, offering improvements over conventional, 

petroleum-based processes in terms of sustainability.

Democratizing Genetic and Omics Technologies 

for Multiple Species. The goal is to enable genetic 

manipulation and the use of genomic, transcriptomic, 

metabolomic, and proteomic analysis methods across 

a wide and diverse set of species. In the envisioned 

setting, scientists could manipulate the genomes of 

environmental isolates and species of BER mission 

relevance, studying uncultivable microbes in situ

and broadly applying omics approaches. Achieving 

this goal requires new technologies that enable tool 

expansion to reach these targets.

Transforming Recalcitrant Strains. A specific 

challenge associated with genetic manipulation 

is the need to transform genetic material in recalcitrant strains. Many methods for transformation 

exist (e.g., electroporation, chemical approaches, 

heat shock, and mechanical disruption), but the 

best approach is often unclear a priori. Although 

technologies like CRISPR have been used in a wide 

variety of hosts, inserting DNA or proteins into cells 

is a technical challenge that can limit the use of these 

technologies. To overcome this recalcitrance, the 

CRISPR-guide RNA (gRNA) complex potentially 

can be directly transformed into cells as demonstrated in mammalian cells.

Leveraging tools from nanotechnology will be necessary to further address this challenge, with the 

subsequent use of high-throughput approaches to 

screen for successful transformation. For example, 

microfluidic devices can be used to interrogate the 

electrical properties required to transform cells in 

a high-throughput fashion by imposing a gradient 

of electrical fields on cells (Garcia et al. 2016). 

High-throughput approaches also may be useful 

for systematically interrogating an array of different 

transformation conditions, especially when working 

with recalcitrant strains. The goal is to enable efficient 
12 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

transformation of genetic material in a wide variety of 

species, ranging from microbes to plants.

Applying Gene Editing to Diverse Microbial and 

Plant Species. New methods for genome editing 

and manipulation hold great promise for use as 

synthetic biology tools. These methods include 

CRISPR-associated protein-9 nucleases (Cas9s), 

transactivation-like effector nucleases (TALENs), 

zinc finger nucleases (ZFNs), and recombinases 

(Copeland et al. 2014). These methods enable 

insertion, deletion, or editing of genomic material. 

For instance, the technologies can be used to knock 

out genes, modify specific regions of the genome 

sequence, or replace promoter regions with alternative sequences. In addition to having DNA-editing 

capacity, variants on these methods offer the ability to regulate gene expression, such as repressing 

or activating transcription of a gene of interest 

(Dominguez et al. 2016).

Workshop participants envisioned applying these 

methods to diverse microbial and plant species for 

biological system design. This technological advance 

will enable precise interrogation of gene function, 

testing of the role of particular sequence features, 

and potentially up- and down-regulation of expression in a programmable fashion. Critical for deploying the synthetic biology toolbox to a wide variety of 

species, these techniques possess the essential ability 

to manipulate DNA and regulate gene expression.

Accelerating Development of Technologies 

for the Design-Build-Test-Learn Cycle and 

High-Throughput Capabilities. Synthetic biology 

requires the ability to design gene circuits and metabolic networks to achieve production of a desired 

output such as a bioproduct or biofuel. Engineering these systems and gene circuits is challenging, 

however, and there may be many different possible 

designs to consider. For instance, in a microbial 

biofuel production strain, many genes may encode 

enzymes required for production, and their order 

of appearance in a genetic construct and their 

expression level can dramatically influence final 

production levels. Therefore, the ability to conduct 

experiments in a way that enables rapid learning and 

redesign is critical to the success of synthetic biology 

for biofuel and biochemical production.

Several technologies can support acceleration of 

the design-build-test-learn cycle. Technologies that 

reduce the time or effort involved in conducting 

experiments are valuable because a particular design 

round may be only one in a set of many iterations. 

Examples of technologies that can speed this process include in vitro systems for synthetic biology, 

genome-engineering methods, high-throughput 

screening or selections, and improvements in methods for cloning or gene synthesis.

In vitro methods such as the cell-free transcription 

translation (TXTL) system use cell extracts to 

conduct cell processes (Garamella et al. 2016), but 

because these approaches can be conducted in a test 

tube and do not require the use of cells, researchers can avoid steps associated with cell growth and 

replication. As such, linear DNA obtained through 

polymerase chain reaction (PCR) or gene synthesis 

can be used directly as a transcription template. This 

process reduces the time required to collect data 

from a few days to a few hours, thus enabling the 

testing of many design iterations and faster learning 

of design principles. These in vitro methods can be 

coupled with microfluidics to achieve cell-like conditions, replicating cell division and other processes 

in a carefully controlled setting (Tayar et al. 2015).

Genome-engineering approaches are powerful 

tools for synthetic biology and can be used in 

concert with screening and selection methods to 

design desired traits (Bao et al. 2016). Examples of 

successes for metabolic engineering applications 

include multiplex automated genome engineering 

(MAGE), CRISPR use for multiplexed genome 

engineering, and oligo-mediated approaches (Wang 

et al. 2009; DiCarlo et al. 2013; Ryan et al. 2014; 

Jakočiūnas et al. 2015). These technologies rapidly 

introduce genetic variation at the genome level; 

however, to select or screen for a desired phenotype, 

they can be coupled to sensor-selector strategies 

(Dietrich et al. 2013; Yang et al. 2013; Raman et al. 

2014). For instance, direct or indirect readout methods can be used with fluorescence-activated cell 
November 2017 Biological and Environmental Research Advisory Committee 13

Chapter 2 — Biological Systems Science

sorting (FACS) or other high-throughput methods 

to screen for improved phenotypes. These methods 

enable rapid engineering of metabolic pathways and 

are relevant to biofuel and bioproduct production.

Rapidly Synthesizing De Novo DNA and Assembling Large DNA Molecules. The ability to synthesize DNA fragments and assemble them into large 

DNA molecules rapidly and cost-effectively is transforming the way synthetic biology research is conducted (Kosuri and Church 2014; Chao et al. 2015), 

and BER should continue to encourage efforts in 

this area. DNA synthesis can greatly advance the 

ability to perform experiments using genetic material derived from diverse hosts. For example, gene 

synthesis can optimize codons to enable improved 

expression in a heterologous host, avoiding codons 

that are rare in the host of interest. As a second 

example, DNA synthesis is critical for constructing 

custom CRISPR single-guide RNAs (sgRNAs), 

including the use of multiplexed approaches that 

target multiple genes simultaneously. This area also 

encompasses a vast quantity of exploitable genome 

sequence information. Users anywhere in the world 

can obtain sequence information and synthesized 

DNA of interest for their particular task.

Grand Challenge 2.3 

Optimize the use of large datasets that integrate 

omics surveys with biochemical and biophysical 

measurements to generate knowledge and identify 

biological paradigms.

Maximizing the Wealth of BER Data

Over the past 10 years, DNA sequencing has 

exploded into multiple platforms for highthroughput, high-quality sequencing from DNA 

and RNA templates. DOE’s Joint Genome Institute 

( JGI) has taken advantage of these platforms to 

sequence the genomes of thousands of bacteria 

and fungi and dozens of plant and algal genomes 

to broaden phylogenetic references and advance 

DOE missions in bioenergy and carbon cycling 

(see also Chapter 7 on User Facilities and Research 

Infrastructure, p. 85). These technological advances 

are providing the in-depth datasets needed to 

disentangle the complexity of individual cellular 

regulation, metabolic responses to environmental 

stimuli, and interactions among different microbial 

communities (i.e., bacteria, fungi, and archaea) and 

plants in terrestrial ecosystems. Scientists can now 

test hypotheses about cellular status, community 

responses, and microbe-plant interactions at resolutions not achievable 10 years ago. Laboratory 

and field experiments can be interrogated with 

sufficient technical replication, across time series, 

and with gradients and combinations of environmental conditions to facilitate statistical analyses. 

High-throughput sequencing and the ability to 

synthesize DNA have enabled rapid generation 

of high-coverage sequence datasets for individual 

organisms across multiple physiological states, 

defined mixtures of organisms, natural microbial 

communities, and microbe-plant interactions.

This expansive sequencing capability presents 

a new set of challenges for the next 10 years in 

overcoming the bottleneck of data analysis and 

interpretation. To maximize the utility of these 

datasets, three new challenges must be overcome 

and managed: (1) a simple, managed program of 

data organization, archiving, and retrieval, both 

for individual investigative teams and the scientific community as a whole; (2) sequence datasets 

accompanied by or interfaced with their related 

metadata, such as chemical and physical measures, 

cellular conditions, environmental conditions, and 

references containing the appropriate information; 

and (3) sequence data that are quality controlled 

and functionally annotated using appropriate, 

community-vetted standards and readily available 

to all (see also Chapter 6 on Data Analytics and 

Computing, p. 71). The lack of high confidence 

in annotation is recognized as a significant barrier to enabling predictive biology; however, little 

progress has been made because of the problem’s 

daunting scale. A potential solution would be to 

develop capabilities to accelerate discovery and 

prioritize efforts for providing meaningful, but not 

particularly onerous, requirements (e.g., veracity of 
14 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

annotation to genes with demonstrated relevance to 

BER missions). This solution represents an opportunity to integrate breakthroughs in accelerating 

experimentally grounded gene-function prediction 

with advanced computational and bioinformatics approaches. Each of these three challenges 

and potential approaches for achieving success is 

described in more detail in the following section.

Research Needs and Knowledge Gaps

Organizing, Archiving, and Retrieving Data. 

With guidance from BERAC’s 2010 Grand Challenges report, DOE BER user facilities have begun 

offering joint proposals. For example, DOE’s JGI 

and Environmental Molecular Sciences Laboratory (EMSL) issued a joint call as part of the 

Facilities Integrating Collaborations for User 

Science (FICUS) program, which helps facilitate 

innovative and efficient use of BER facilities (see 

Chapter 7 section, Collaborative Research Among 

Scientists and Facilities, p. 91). Currently, however, 

users typically partition samples and send them to 

each facility where they are relabeled according to 

that facility’s convention. This situation exemplifies the critical need for user facility coordination 

to manage sample integrity and organization. 

These coordinated datasets need to be housed in a 

publicly available resource accessible by all DOE 

researchers and organized in a way that links them 

to their associated manuscripts. Sophisticated 

tools must be available with intuitive and easyto-use web-based interfaces that do not require 

extensive training in computer science. Moreover, 

the user facilities could be used as a framework 

for integrating Big Data approaches across BER 

mission areas (e.g., biofuels, climate responses, and 

carbon cycling).

To begin addressing data challenges, BER also 

might consider supporting a community workshop focusing on the issues associated with the 

deposition, archiving, retrieval, management, and 

exploration of large-scale sequencing and related 

process datasets. Participants, including experts 

in cell biology and synthetic biology, microbial 

communities, microbe-plant interactions, and 

plant metabolism, could devise scenarios to maximize knowledge from existing and future datasets. 

Critically needed, though, are guidance and a 

framework for facilitating standard practices across 

disciplines (see also Chapter 6 on Data Analytics 

and Computing, p. 71).

Coordinating Sequence Datasets with Their 

Associated Chemical, Physical, Temporal, and 

Environmental Treatment Datasets. DOE investigators have studied the responses and feedbacks 

of terrestrial ecosystems to elevated carbon dioxide, 

warming, nitrogen deposition, ozone, heavy metals, 

and organic solvents using the full gamut of BER 

molecular, chemical, physical, and modeling tools. 

Over the past 5 years, multiple meta-analyses have 

emerged in attempts to derive emergent general 

principles from the wealth of past individual studies. 

The difficulty has been, and continues to be, that 

datasets are too sparse and too poorly replicated to 

identify consistent response trends. This difficulty 

is due in part to different experimental designs, 

sampling approaches, reporting requirements, and 

misidentification of sequences housed in that time’s 

“state-of-the-art” databases. Bringing the datasets 

from multiple past studies together and providing a 

robust assessment of their collective findings have 

been challenging and are resulting in much confusion in the literature. With new technologies in 

hand, BER can now advance from single-organism 

investigations across more complex environmental 

conditions to mixed-community studies toward sustainable production of biofuels and whole-ecosystem 

assessments of climate change responses and feedbacks. The standardized deposition of all related 

datasets into public resources is imperative for 

mining these data efficiently and appropriately and 

deriving general principles. Specifically needed are 

new approaches for integrated science designed to 

aid knowledge mining from omics datasets (e.g., conduct imaging chemistry and physical measurements 

to phenotype cellular outcomes as necessary). These 

datasets should be housed in interactive, accessible 

locations for multiple investigators to enable reanalysis and meta-analysis as desired.
November 2017 Biological and Environmental Research Advisory Committee 15

Chapter 2 — Biological Systems Science

Providing Quality Control and Functional 

Annotation. The depth of coverage enabled by current and emerging sequencing approaches provides 

datasets of unprecedentedly high quality. This is true 

of PCR-based amplicon sequencing, a technology 

that can interrogate more than 300 samples on a single MiSeq run, and whole-cell or whole-community 

metagenomic and metatranscriptomic datasets that 

enable multiplexing of samples to maximize data 

output. Although current sequencing approaches 

have multiple technical constraints, the ability 

to appropriately implement quality control metrics is critical to downstream data analysis. Poor 

sequences must be culled early in the process to 

avoid later misinterpretation. Providing quality 

control standards and approaches is a critical DOE 

responsibility because most DOE-funded research 

laboratories can easily generate DNA and RNA 

sequence datasets. Most universities and national 

laboratories currently house sequencing facilities. 

To enable cross analysis of data from different studies and different regions, guidance and standards 

for all BER-generated data must be provided or at 

least presented concisely with the datasets. Largescale experimental efforts are needed to “prime 

the pump” with respect to computational propagation of functional inference because the quality of 

sequencing data does not address the lack of appropriate experimentally derived functional annotations of genes that contribute to traits of relevance 

to BER missions.

Grand Challenge 2.4 

Understand the links between genotype and phenotype in single but very diverse organisms and in 

communities of organisms that interact in terrestrial ecosystems.

Building on Genotyping and Phenotyping 

Advances for System Prediction

The genome encodes an organism’s full potential, 

but the manifestation of this potential with regard 

to environmental interactions defines the organism’s 

phenotype. Although the genome is largely fixed 

(i.e., excluding nonheritable, epigenetic effects), 

the phenotype can be quite variable, reflecting 

genotype-by-environment interactions (i.e., varying 

responses of different genotypes to changes in the 

environment). Therefore, understanding biological 

complexity to the point of predicting outcomes 

requires a complete understanding of both genotype 

and phenotype. High-throughput sequencing has 

enabled the full sequencing of various microbial, 

fungal, and plant genomes, although at different 

levels of completeness and quality. Continued 

improvements expected in sequencing technology 

will, over the next 20 years, provide at least the 

potential to sequence the full genomes of all living 

organisms. Applying these approaches to messenger RNA (mRNA) provides a platform to examine 

gene expression (i.e., transcriptomics), while other 

applications enable the identification of epigenetic 

marks (e.g., methylome), chromatin structure (HiCseq), and protein-DNA interaction (e.g., ChIP-seq). 

Access to these technologies, coupled with the 

ability to extract nucleic acids directly from environmental samples, underlies the exciting area of 

metagenomics, where microorganisms that cannot 

be cultured in the laboratory can now be identified 

and investigated.

Although high-throughput nucleic acid approaches 

have developed at an exponential rate, the ability to 

query the corresponding phenotypes lags behind, 

especially when the questions to be answered 

require in situ analysis of organisms (i.e., in their 

natural habitat). However, exciting new technologies 

are being applied to these questions. For example, 

many universities, borrowing from technology 

deployed in the agricultural biotechnology industry, have installed automated plant growth chambers and greenhouses that provide relatively high 

throughput analysis of various phenotypic features 

such as growth rates, photosynthetic efficiency, and 

biomass. These platforms are useful for basic plant 

and algal discovery research. However, measurements made in these growth facilities thus far do not 

translate readily to experiments conducted in the 

field, hence, arguably contributing only marginally 
16 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

to the ultimate goal of enabling predictive biology in 

the natural environment.

The good news is that the importance of phenotyping is now readily apparent, in large part due to 

the stark contrast with the ability to genotype (i.e., 

by sequencing). Technological advancements now 

enable measurements of various parameters both 

in the laboratory and the field, which then can be 

statistically associated with genotype. For example, 

one such approach, the genome-wide association 

study (GWAS), makes use of natural genotypic variation in the population of a given species. Statistical 

association with specific alleles identified by this 

variation with measured phenotypic traits enables 

the identification of new genes controlling those 

traits. One advantage to this approach is its capacity 

for use in the field (e.g., with crop plants), providing 

greater potential for accurately reflecting the phenotypes expressed under natural conditions. An example is the application of GWAS to identify key alleles 

governing important crop biomass traits for biofuel 

production (Slavov et al. 2014).

Research Needs and Knowledge Gaps

Applying Multiple Functional Genomics 

Approaches to Single Samples and Cells. At 

another level, the application of various functional 

genomics approaches (e.g., transcriptomics, proteomics, and metabolomics) also defines phenotype 

when applied to bacteria, fungi, and plants. These 

measurements can be related to genotype, such as 

through the use of mutant populations, GWAS, 

or other approaches. However, in most cases, 

researchers have applied only one or a few of these 

experimental approaches (e.g., transcriptomics 

and proteomics) in a given study. One remaining 

challenge is to gain the ability to apply a multiomics 

approach to a single sample, an essential capability for accurate data comparison and integration. 

Another issue is that of tissue dilution. In many 

functional genomics approaches, the samples used 

are multicellular organisms or communities of 

organisms in which the phenotype may vary from 

cell to cell in response to a specific environmental 

change. Hence, if such cell assemblages are used, 

the data obtained represent an average of cellular 

responses across all cells, obscuring the fine details 

of responses at the level of an individual cell. Thus, 

truly understanding regulatory mechanisms and 

integrating dissimilar datasets are difficult, even if 

the measurement data are taken from the same sample. Consequently, as described in the 2010 BERAC 

Grand Challenges report, there remains great interest in conducting functional genomic measurements 

(i.e., phenotyping) on single cells, especially if they 

can be sampled in situ without extensive manipulation that could change the phenotypic response (see 

also Chapter 8 on Emerging Technologies, p. 95).

Developing a Hierarchal Annotation Pipeline 

that Integrates Experimental and Computational 

Approaches to Assess Functional Annotation 

Quality. Given the relative speed with which an 

organism’s genotype can be defined, researchers 

often explore the DNA sequence to infer phenotype, mining the genome annotation (i.e., description of the genome function) for information

such as specific enzymes that encode a pathway 

of interest. The success of this approach depends 

on the ability to accurately annotate the genome 

to provide accurate information that defines the 

true biochemical function of each gene, regulatory 

site, and epigenetic mark. The classic tenet that 

one gene leads to one enzyme is clearly false, with 

one gene potentially encoding proteins of differing 

functions (whether via a promiscuous active site, 

gene splicing, or multifunctional proteins and complexes), adding further complexity to the annotation 

process. There also is a growing appreciation for 

the impact of post-translational modifications on 

protein function or compartmentalization and the 

myriad forms these impacts can take (i.e., beyond 

the more-studied role of protein phosphorylation), 

a discovery which adds even more complexity to 

gaining a full understanding of the cellular function 

that a single protein may play. When these issues 

are considered in the context of the exponential 

growth in genome sequences deposited in databases, accurate genome annotation, while essential, 

also clearly becomes an extremely challenging and 
November 2017 Biological and Environmental Research Advisory Committee 17

Chapter 2 — Biological Systems Science

perhaps impossible task. Currently, the volume of 

accumulating genomic information mostly enables 

only in silico, automated genome annotation. This 

work usually is based on comparisons to previously 

defined annotations that may be incorrect. Hence, 

a real and growing risk is that the ever-increasing 

knowledge of genomes is being continually corrupted by incorrect and misleading annotation. 

At the same time, the means to broadly engineer 

genomes is advancing more rapidly than a basic 

understanding of potential engineering targets. The 

advent of technologies such as the CRISPR-Cas9 

system underscores the urgent need for breakthroughs in addressing the gene function knowledge 

gap and advancing predictive understanding.

How should critical issues related to accurate 

genome annotation be addressed or at least 

curtailed? Clearly, experimental biochemistry 

approaches are the only accurate means to properly 

define, for example, the enzymatic function of a 

specific protein. Equally clear, however, is that such 

approaches, even if conducted in a high-throughput 

manner, are unlikely to catch up with genome 

sequencing advances. Experimental biochemistry 

or physiological approaches will remain largely in 

the realm of specific studies that require this level 

of annotation confirmation, but platforms designed 

to identify genes of importance to achieving BER 

missions provide a potential approach to prioritizing 

efforts. In other cases, especially as a prelude to such 

detailed studies, other available approaches need 

concentrated development and application.

Computational approaches are the only means 

currently addressing the full scope of genome annotation. One challenging area that may hold great 

promise is the use of methods that do not rely on 

pre-existing knowledge (e.g., Bayesian methods). 

Such methods are being used to predict protein 

structure solely from the amino acid sequence 

(Li et al. 2014). Nevertheless, protein structures 

predicted by comparison to known, experimentally 

derived structures are far superior. Hence, Bayesian 

approaches currently do not provide the level of 

confidence that comes, for example, from comparison to current databases.

“Perfect” (i.e., experimentally validated) annotation 

is the ultimate goal, yet perfect should not become 

the enemy of the good. Therefore, BERAC’s recommendation is to develop a rational, hierarchal 

approach to genome annotation that can provide a 

continuous gradient of useful information to constrain a given gene’s annotation. This approach will 

provide a starting point for deeper investigation 

by specialists when needed. For example, transcriptomic approaches can define where, when, 

and under what conditions a gene is expressed, 

while imaging approaches can define where in the 

cell the encoded protein is expressed. Computational and experimental approaches can determine in a high-throughput fashion the proteins 

with which a specific target protein may interact. 

High-throughput genetic, biochemical, and structural experimental approaches focused on processes 

of central importance to BER missions can provide 

underlying functional inferences to catalyze function prediction and provide a level of confidence to 

computational predictions. Graphic methods using, 

for example, mRNA or protein expression data can 

be used to define co-expression networks, providing 

a nearest-neighbor approach to further confine the 

annotation. None of these approaches ultimately 

leads to a perfect annotation. Yet, concerted BER-led 

efforts would develop a hierarchal annotation pipeline that fosters breakthroughs in combining experimental and computational approaches to address 

functional annotation veracity. Such work would 

greatly increase the success of sequencing-based 

techniques in understanding BER-relevant processes. The establishment of a database [e.g., DOE’s 

Systems Biology Knowledgebase (KBase)] for storing and efficiently querying this information would 

greatly support BER research, while also likely contributing a platform for recognizing currently incorporated genome annotation data.

Tackling Genomic “Dark Space” by Characterizing Proteins of Unknown or Uncertain Function. 

The ability to sequence genomes and quantify a 
18 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

microbe’s or plant’s transcriptome, proteome, and 

metabolome during any set of prescribed conditions 

or stresses is far outpacing current understanding 

of the transcriptome-encoded proteins (i.e., components of the proteome) and determinants of the 

metabolome. Available omics data provide invaluable functional insight, but more than half of genes 

in relevant bioenergy crop genomes encode proteins 

whose functional annotation is either uncertain or 

lacking altogether. This critical knowledge gap—the 

genomic dark space—undermines the ability of 

BER systems scientists to leverage genomic information from photosynthetic and microbial organisms 

to meet national energy needs. As stated in the 

2010 Grand Challenges document, the uncertainty 

of functional annotations constitutes a barrier to 

enabling predictive biology, and this barrier still 

exists today. Indeed, the means to broadly engineer 

plant and microbial genomes is advancing more 

rapidly than protein functional discovery. Most 

notably, the advent of technologies such as the 

CRISPR-Cas9 system underscores the urgent need 

to address this knowledge gap and advance predictive understanding.

Grand Challenge 2.5 

Effectively exploit new and emerging technologies 

in systems biology and physical measurements 

(e.g., miniaturization) to accelerate biological 

discoveries.

A number of opportunities exist to accelerate systems biology research using emerging technologies in (1) nanotechnology and microfluidics (see 

also Chapter 8 on Emerging Technologies, p. 95), 

(2) imaging, and (3) capabilities that capitalize on 

foundational knowledge at DOE user facilities (see 

also Chapter 7 on User Facilities and Research Infrastructure, p. 85).

Research Needs and Knowledge Gaps

Using and Coupling Nanotechnology and Microfluidics. Advances in nanotechnology offer great 

promise for systems biology research. For instance, 

these methods can enable single-cell resolution measurements and perturbations and precise recording 

of interactions between microbes within a community. In addition, devices can be designed to conduct 

in situ experiments, allowing a coupling of the context in which an organism is isolated and the experimental test conditions.

Microfluidic devices also offer great potential 

because these devices can be used to culture biological samples with precision control over dynamic, 

time-varying environments. Microfluidic chips often 

are coupled with imaging to visualize the outcome of 

experimental perturbations, often at the single-cell 

level. Their small size affords reduced sample size 

requirements, smaller volumes of reagents, and the 

ability to measure many different conditions in parallel or in a repeatable, programmable fashion.

Coupling the microfluidic approaches with nanotechnology could accelerate and automate synthetic 

biology. For instance, these devices can achieve 

automated assembly of DNA fragments on a single chip (Shih et al. 2015). When coupled with 

next-generation cloning methods such as Gibson 

assembly or Golden Gate (Engler et al. 2008, 2009; 

Gibson et al. 2009), this technology can be used to 

rapidly construct prototype assemblies for testing 

and analysis. Another significant need in this area is 

the development of additional methods to optically 

report cellular concentrations and activities [e.g., 

fluorescent reporters for adenosine triphosphate 

(ATP) synthesis or glycolysis].

Some potential outcomes of advances in nanotechnology and microfluidics include rapid construction 

and screening of gene constructs for biofuel production. In addition, researchers can use these methods 

to assess microbial communities. They can construct 

synthetic communities using pairs or groups of 

microbial species and then subject these communities to time-varying environments that allow for 

slow, moderate, or rapid changes of environmental 

conditions to assess their impact on community 

structure. They also can assess spatial organization at 

this level, especially when coupling spatial organization with imaging methods.
November 2017 Biological and Environmental Research Advisory Committee 19

Chapter 2 — Biological Systems Science

Applying Imaging Technologies. For many years 

cryo-electron microscopy (cryoEM) was useful for 

identifying large protein complexes to provide an 

overall shape and relative orientation with respect 

to membranes or other subcellular structures. 

Researchers routinely used single-particle cryoEM 

analysis in conjunction with gold-labeled antibody 

binding to map the location of individual proteins 

within large protein complexes. Recent advances in 

direct electron detectors, along with the development of new algorithms for data analysis, have revolutionized cryoEM applications so that obtaining 

near atomic-scale (<3 Ångström) resolution is now 

possible for large proteins and protein complexes.

This technique complements crystallography in 

probing three-dimensional (3D) structures of macromolecules at the near-atomic level. Additional 

emerging electron microscopic techniques address 

sample scales above and below those accessible to 

single-particle cryoEM. For example, cryo-electron 

tomography (cryoET) of cells or cell sections <500 

nanometers (nm) thick (Villa et al. 2014) produced 

by focused ion beam (FIB) milling yields 3D images 

in the 5- to 10-nm resolution range (Diebolder et al. 

2012) and can be combined with single-molecule 

techniques in correlative studies (Hampton et al. 

2017). Protein complexes within tomograms can be 

viewed by single-particle subtomogram averaging, 

and micro-electron diffraction (microED) from 

radiation-sensitive micro- and nanocrystals yields 

high-resolution structures that have proven to be 

intractable by other means (Shi et al. 2016). This 

array of electron-based imaging techniques, in addition to a host of emerging X-ray imaging techniques 

now possible with the coherent beams produced by 

new-generation synchrotron lattices, will enable new 

and more detailed visualizations of biological and 

environmental samples important to BER.
20 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision
November 2017 Biological and Environmental Research Advisory Committee 21

Chapter 3 — Earth and Environmental Systems Sciences

Improving Predictive 

Understanding of Earth 

System Variability and Change

One of the greatest challenges for Earth and 

environmental systems sciences in the 

coming decades lies in addressing the need 

for better information that enables communities to 

assess, quantify, and plan for climate risks. These 

risks could affect energy and other engineering infrastructures, human and ecosystem health, food and 

water supply, conflict, and migration, but the current 

generation of Earth system models (ESMs) and the 

measurements that underpin them still are not well 

suited to inform decisions that need to be made in 

the context of these changing risks. Needed capabilities include (1) the ability to explore and quantify 

model aspects that are uncertain, inadequate, or 

lacking key components; (2) expanded capabilities 

for observing and measuring not only atmospheric 

processes, but also terrestrial, coastal, and cryosphere 

systems; and (3) coordinated research and modeling 

of ecosystems and processes representing key uncertainties in the coupled human-Earth system.

The U.S. Department of Energy’s (DOE) Office 

of Biological and Environmental Research (BER) 

has made great strides in addressing some of these 

challenges through strategic investment in capabilities and facilities that advance understanding and 

modeling. Building on previous successes and anticipating new opportunities, the Biological and Environmental Research Advisory Committee (BERAC) 

has identified eight new grand challenges in this 

report for Earth and environmental systems research 

(see Earth and Environmental Systems Sciences 

Summary of Grand Challenges and Action Items, 

p. 22). Successfully addressing these challenges will 

transform predictive understanding of Earth system 

variability and change in the coming decades.

Earth System Models

Current ESMs have a number of limitations including (1) each model is a central estimate, (2) models are not independent, (3) ensemble size is too 

small to fully sample the changing likelihood of 

extreme events, and (4) uncertain parameters are 

undersampled. A significant factor limiting ESM 

fidelity is model resolution, which is restricted by 

computing resources; consequently, key Earth system processes cannot be resolved. As exascale computing is realized in the coming decade, researchers 

have a significant opportunity to improve ESM 

Grand Challenges in Earth and 

Environmental Systems Sciences

Overarching Grand Challenge and 20-Year Vision

Earth system evolution is governed by complex processes and their interactions spanning a 

significant range of temporal and spatial scales. Credible simulations and predictions require 

synergistic advances in observation, modeling, data-model integration, and computing, as 

well as novel experimentation for mechanistic understanding of integrated system behavior 

and uncertainty.

3
22 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Earth and Environmental Systems Sciences

Summary of Grand Challenges and Action Items

The overarching goal of BER Earth and environmental systems sciences is to improve predictive understanding of Earth 

system variability and change. BERAC has identified eight grand challenges to advance modeling and observation of the 

Earth system and its key processes including aerosols and clouds, terrestrial and ocean biogeochemistry, the water cycle, 

and cryosphere. Addressing these challenges synergistically will improve predictions of water availability, sea level rise, 

and extreme events, from weather to climate time scales, and will provide an understanding of Earth system stability and 

low-probability, high-risk outcomes. These grand challenges are listed here and then described in more detail in the Grand 

Challenge Research Recommendations section, p. 24.

3.1. Advance Earth system modeling using a hierarchy of 

models, from process-resolving coupled models to 

reduced-order models, to transform understanding 

of the coupled Earth system and to produce useful 

and credible simulations and predictions of Earth 

system behavior at multiple time scales.

3.2. Establish new observational technologies and 

use them to understand human and Earth system 

processes, such as land-atmosphere interactions, 

biogeochemical cycles, and subsurface soils, to 

estimate critical process parameters using novel 

analysis methods, such as machine learning and data 

science, and to quantify model errors.

3.3. Advance basic knowledge and scale-aware 

simulation capability for Earth system feedbacks 

associated with aerosols and moist processes 

to better quantify aerosol forcing, precipitation 

changes, and extreme events with consequences 

for energy and water cycles, global distribution of 

nutrients, and human health.

3.4. Advance modeling and understanding of 

important ecological, biological, and carbon cycle 

interactions and feedbacks in the climate system 

to identify potential tipping points and possible 

energy strategies.

3.5. Characterize, understand, and model the complex, 

multiscale water cycle processes in the Earth system 

including the subsurface to understand and predict 

water availability and human system response 

to extremes.

3.6. Understand the time-dependent processes and 

mechanisms associated with melting glaciers, ice 

caps, and ice sheets and their contributions to 

regional sea level rise.

3.7. Quantify the interplay between internally generated 

climate variability and externally forced response 

involving anthropogenic and natural factors and their 

relative roles in the time evolution of regional variability 

to understand predictability of the Earth system.

3.8. Understand the long-term Earth system stability 

in response to possible future Earth system 

outcomes and address the level of confidence 

and identify emergent constraints for the range of 

model projections.

Associated with the grand challenges are six action 

items recommended for initiating progress toward 

the challenges:

• Develop fine-scale process models and data 

assimilation capabilities for data-model integration 

to advance model development and prediction of 

Earth system processes.

• Advance high-resolution modeling in different 

simulation and prediction modes supported by 

exascale computing to improve understanding and 

prediction of extreme or high-impact events.

• Develop a computational user facility for rapid 

design, generation, evaluation, and diagnosis of 

Earth system model simulations, as well as analysis of 

ensemble predictions and data-model synthesis.

• Develop and maintain a hierarchy of models 

for hypothesis testing, model development, 

and uncertainty analysis of the complex 

human-Earth system.

• Develop and integrate new sensing technologies 

and optimize field deployments in Atmospheric 

Radiation Measurement, AmeriFlux Network, NextGeneration Ecosystem Experiments, and other BER 

programs to explore interactions across different 

scales of biological organization and biosphereatmosphere feedbacks.

• Create new integrated field laboratories that target 

biogeochemical, energy, and water flows between 

urban areas and surrounding ecosystems.
November 2017 Biological and Environmental Research Advisory Committee 23

Chapter 3 — Earth and Environmental Systems Sciences

spatial resolution for cloud-resolving atmosphere 

modeling, eddy-resolving ocean modeling coupled 

with detailed sea ice and ice sheet dynamics, and 

fine-scale terrestrial modeling of ecosystems and 

surface and subsurface processes of hydrology and 

biogeochemistry. DOE is in a leadership position 

to advance exascale computing and Earth system 

modeling in tandem.

Supported by computational user facilities, shortterm climate predictions can be used as a testbed 

for accelerating ESM improvement and evaluation 

and for providing information on which impacts 

will be felt from the interplay between internal 

variability and externally forced response. At the 

same time, a hierarchy of models with different 

complexities is needed for large-ensemble modeling, hypothesis testing, process understanding, and 

uncertainty analysis.

Measurements and Observations

To improve understanding and modeling, observational capabilities need to be expanded. The concept 

and capabilities of BER’s Atmospheric Radiation 

Measurement (ARM) Research Facility are unique 

and significant, but the ARM Facility focuses on 

atmospheric measurements, whereas progress in 

modeling will require measurements for the coupled 

Earth system. Measurements of terrestrial, coastal, 

and cryosphere systems have significant gaps. The 

data challenge for ESMs is to (1) fill these measurement gaps; (2) identify niche areas to integrate 

models and data; (3) develop data assimilation capabilities for model-data integration; and (4) leverage 

data analytics to develop new understanding and 

address key uncertainties in aerosol and cloud processes, terrestrial and ocean biogeochemistry, and 

water cycle and cryosphere processes.

Coordinated Research and Modeling 

of Key Uncertainties in the Coupled 

Human and Earth Systems

Using new variable-resolution capabilities, DOE is 

uniquely suited to address the significant challenge 

of regional sea level rise and coastal impacts 

related to ice sheet dynamics, ocean-ice interactions, ocean thermal expansion, coastal inundation, and other impacts. High-latitude changes 

and their global influence will continue to be an 

important target over the next 20 years. DOE is 

well positioned to address this challenge with 

coordinated research activities in Earth system 

modeling, analysis of cryosphere processes, and 

measurements from BER’s Next-Generation Ecosystem Experiments (NGEE)–Arctic project and 

the ARM supersite on Alaska’s North Slope. Lastly, 

through the development of human–Earth system 

models, DOE can further develop and use the 

capability to explore Earth system stability and the 

potential for low-probability, high-risk outcomes.

Building on Progress Related 

to the 2010 Grand Challenges

The 2010 BERAC report, Grand Challenges for 

Biological and Environmental Research: A Long-Term 

Vision (BERAC 2010), posed six grand challenges 

in climate research. Important progress and some 

remaining challenges are briefly summarized:

Developed Higher-Resolution Modeling. DOE 

has contributed to the development of highresolution versions of the Community Earth System 

Model (CESM), global variable-resolution models, 

and super- and ultraparameterizations. Researchers 

have used high-resolution models to understand 

the water cycle and extreme events. Efforts are well 

under way to develop DOE’s Energy Exascale Earth 

System Model (E3SM) as a very high resolution 

model, but more relevant processes must be integrated into ESMs.

Improved Parameterizations of Aerosols that 

Affect Clouds. Significant progress has been made 

through research supported by multiple DOE programs to develop parameterizations of aerosols and 

aerosol-cloud interactions and perform modeling 

experiments to understand and quantify aerosol 

direct and indirect effects. However, indirect aerosol forcing and some basic knowledge of aerosols 

remain uncertain.
24 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Developed Ecosystem-Observing Systems to 

Monitor Biogeochemical Cycles. DOE has established ecosystem-observing systems in a few regions, 

but they are not positioned to monitor biogeochemical cycles on global scales. Ecosystem models have 

been tested against some observations, but efforts 

have not yet narrowed enough to estimate critical 

process parameters.

Advanced Understanding of Important Biological Interactions and Feedbacks. DOE has 

advanced understanding of potential rates of permafrost carbon losses and the influence of climate and 

land use change on wildfires. However, understanding regarding the long-term stability of other carbon 

stores and biological systems is still lacking.

Improved Integration of Anthropogenic Climate 

Forcings into ESMs. DOE has made important 

progress in developing an integrated Earth system 

model (iESM) that couples integrated assessment 

models (IAMs) with ESMs to represent humanEarth interactions. DOE is well positioned to further enhance integration of anthropogenic forcings 

into ESMs for improving climate projections and 

mitigation, as well as impacts, adaptation, and vulnerability research.

Established New Observational Technologies to 

Quantify Model Errors. Some notable new observational technologies include the ARM supersites, 

which represent significant progress in providing 

data critical for improving cloud and aerosol modeling, and DOE’s AmeriFlux Network for providing 

carbon and energy fluxes in different terrestrial 

ecosystems. Important advances have been made 

in model-experiment integration as exemplified by 

(1) the NGEE–Arctic and NGEE–Tropics projects, 

(2) the development of frameworks for diagnostics 

and metrics such as the Program for Climate Model 

Diagnosis and Intercomparison (PCMDI), and 

(3) the International Land Model Benchmarking 

(ILAMB) project to comprehensively evaluate and 

diagnose model errors.

Grand Challenge Research 

Recommendations

Grand Challenge 3.1 

Advance Earth system modeling using a hierarchy 

of models, from process-resolving coupled models 

to reduced-order models, to transform understanding of the coupled Earth system and to produce 

useful and credible simulations and predictions of 

Earth system behavior at multiple time scales.

Increasing the Predictive Power 

of Earth System Models

Comprehensive ESMs are the primary tools used in 

predicting Earth system response to external perturbations. However, large uncertainty and lack of 

spatial detail have limited the usefulness of ESMs for 

addressing DOE’s energy mission. ESMs could be 

advanced to produce useful and credible simulations 

and predictions to support planning of energy infrastructure and energy production and use through 

(1) the use of a modeling hierarchy, (2) systematic 

efforts in developing data assimilation systems that 

advance measurement-to-modeling integration, 

(3) user facilities that enable rapid development and 

evaluation of simulations and forecasts, and (4) collaborations among model developers and climate 

and data analytics scientists.

ESMs include a wide range of physical processes, 

from large-scale circulations of the atmosphere and 

oceans to atmospheric convection and mesoscale 

ocean eddies, to cloud microphysics and radiative 

transfer, to the biophysics of photosynthesis and respiration on the land surface and in the ocean. ESMs 

are used to simulate and predict phenomena on time 

scales ranging from days to centuries, but the results 

also depend on small-scale processes that have time 

scales of minutes or less. For these reasons, investigators have always run ESMs on the most powerful 

computing systems available.

ESMs are able to simulate many important phenomena of the real Earth system, including important 

modes of internal variability such as El Niño and 
November 2017 Biological and Environmental Research Advisory Committee 25

Chapter 3 — Earth and Environmental Systems Sciences

the system’s response to external forcings. However, 

ESM-based simulations of future anthropogenic 

climate change are subject to uncertainties caused 

by insufficient resolution, incomplete understanding 

of physical processes, and large internal variability 

that obscures the signals. Models cannot directly 

simulate processes on scales smaller than their grid 

spacing, so parameterizations are used to represent 

subgrid-scale processes. A major challenge in Earth 

system modeling is reducing uncertainty, an aim 

that may be achieved by making the grid finer but 

requires more computing power. Increased spatial 

resolution has several immediate benefits, including 

simulation of a wider range of fluid dynamical scales, 

leading to more realistic representations of a host of 

processes. Another way to reduce uncertainty is to 

improve the realism of the parameterizations. Computing power helps with that too, because, as spatial 

resolution increases, the nature of the unresolved 

parameterized processes becomes simpler, although 

the models also can become less constrained as the 

number of degrees of freedom increases.

Research Needs and Knowledge Gaps

Leverage New Computing Architectures for ESM 

Evolution. The operational numerical weather prediction (NWP) community already has achieved 

atmospheric grid spacing decreases from around 100 

kilometers (km) to around 10 km, probably achieving within the next few years a convection-resolving 

grid spacing of 4 km or finer. Decreases in grid 

spacing, while short of being convection resolving, 

always provide improvements in short-range forecasts but hardly decreases the errors of longer-range 

forecasts beyond a few days (Buizza 2010). An interpretation is that the errors in long-range forecasts 

are due mainly to deficiencies in the physical parameterizations, suggesting that ESMs will benefit less 

from increased resolution, relative to NWP models. 

Enabled by computers of the coming decade, the 

eventual transition to convection-resolving resolution may allow ESMs to benefit from increased 

resolution. Similar arguments also can be made for 

eddy-resolving ocean models and process-resolving 

land surface and sea and land ice models that play 

important roles in Earth system evolution. A strong 

collaboration between the Earth system modeling 

and high-performance computing communities is 

needed to facilitate a smooth transition in which 

ESMs evolve to take advantage of new computer 

architectures (see also Chapter 6 on Data Analytics 

and Computing, p. 71).

Improve Process Parametrizations. Increased computing power also can improve physical ESM parameterizations. Superparameterization (Randall et al. 

2016) and ultraparameterization (Parishani et al. 

2017) improve physical representations by embedding a simplified cloud-resolving model and large 

eddy simulation (LES) model, respectively, within 

each atmospheric grid column of an ESM. As resolution increases, the model parameterizations become 

simpler in the sense that fewer processes have to be 

parameterized. However, even if extreme increases in 

computing power eventually permit 100-meter grid 

spacing over the entire globe, ESMs will still need 

parameterizations of turbulence, radiation, and cloud 

microphysics including the effects of aerosols. Of 

these, cloud microphysics and aerosols are likely to 

be the most problematic (see Grand Challenge 3.3, 

related to aerosols and moist processes, p. 29).

Develop Fine-Scale, Process-Resolving Models with Data Assimilation Capabilities. ESM 

parameterizations inevitably involve numerical 

parameters whose values must be specified based on 

observations. One approach is to adjust the parameter settings iteratively to reduce the errors, but this 

approach does not improve understanding of the 

errors and may reduce errors for the wrong reason. 

With finer grids, the parameterized grid-scale processes are more amenable to direct comparison with 

field data, making model evaluation more straightforward. As more processes are explicitly resolved, 

enabling parameterizations to be increasingly physically based, observations can be used more effectively 

to estimate the numerical values of model parameters. 

This applies to models of all Earth system components and is another motivation for increasing model 

resolution. ARM data are ideally suited for assigning 

values to parameters associated with atmospheric 
26 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

physical processes. The ARM Research Facility has 

been exploring use of LES for the weather observed 

at its Southern Great Plains site. LES data can fill in 

gaps associated with processes that are not observed 

or not sufficiently well sampled by the observing system. Assimilating field measurements into LES models can make this strategy more effective. Similarly, 

fine-scale plant and subsurface hydrology and biogeochemistry models are important tools for linking 

measurements to models to improve terrestrial system models. A systematic effort to develop fine-scale 

models with data assimilation capability may support 

these objectives across multiple DOE programs.

Develop Reduced-Order Models to Understand 

Earth System Interactions and Responses to 

Perturbations. As the resolution and complexity 

of ESMs increase, understanding model behaviors 

and quantifying the uncertainty in their predictions 

become more challenging. ESMs of intermediate 

complexity and reduced-order models are important 

tools on the other end of the modeling hierarchy 

for understanding interactions among Earth system 

components and for unraveling the system response 

to perturbations and quantifying its uncertainty. 

Developing and maintaining a hierarchy of models 

and simulation capabilities should be an important 

part of a strategy to improve ESMs and advance 

their use in understanding and predicting Earth 

system variability and change across all time scales 

relevant to stakeholder decision making.

Collaborate with Computational User Facilities 

for Model Testing and Improvement. To further 

ESM development and utility, computational user 

facilities need to be expanded for on-demand, rapid 

generation, evaluation, and diagnosis of ensemble 

simulations and experimental ensemble predictions. 

One example is to test ESMs through extensive 

forecasting on time scales ranging from days (e.g., 

Palmer et al. 2008) to those that are subseasonal 

to seasonal, for which observations for model evaluation can be provided in great detail. DOE has 

taken some steps in this direction (e.g., Phillips et al. 

2004), but a more comprehensive and systematic 

effort would be useful. In addition, the ability to 

run decade-to-century simulations is important for 

understanding both fast and slow responses (see 

Grand Challenge 3.7, related to Earth system predictability, p. 39). The user facilities should engage 

model developers and computational scientists and 

also collaborate with scientists in climate research 

and machine learning, an essential area for turning large-scale data (i.e., Big Data) into knowledge 

useful for improving understanding and predicting 

the Earth system. Additionally, increased in-house 

expertise is needed in physical oceanography to 

complement DOE’s existing in-house expertise in 

ocean modeling and to improve understanding of 

the coupled Earth system in which the ocean plays a 

key role.

Grand Challenge 3.2 

Establish new observational technologies and use 

them to understand human and Earth system 

processes, such as land-atmosphere interactions, 

biogeochemical cycles, and subsurface soils, to estimate critical process parameters using novel analysis methods, such as machine learning and data 

science, and to quantify model errors.

Building on BER Success in Earth System 

Measurements and Observations

Observations provide the foundational knowledge 

upon which hypotheses about the function of the 

complex Earth system and its response to perturbations are developed and tested in combination with 

modeling. BER’s leadership in the development of 

observing systems and experiments has created a 

vibrant community of scientists who have provided 

answers to some of the most urgent questions challenging the ability to develop a predictive understanding of the Earth system. This research has a 

rich tradition of transforming Earth system science 

by developing new measurement technologies and 

deploying them at remote field sites throughout 

the world. Important examples of these flagship 

systems include the ARM fixed sites and mobile 

facilities, AmeriFlux terrestrial ecosystem flux network, Free-Air Carbon Dioxide (CO2) Enrichment 
November 2017 Biological and Environmental Research Advisory Committee 27

Chapter 3 — Earth and Environmental Systems Sciences

(FACE) experiments, and NGEE projects (see also 

Chapter 7 on User Facilities and Research Infrastructure, p. 85). Each of these observing systems 

operates at a scale that is not possible to sustain 

with university or private-sector investments. Practitioners of this emerging new field of Earth system 

data science are harnessing machine learning and 

other data science approaches to search for patterns 

and create new process-level understanding of 

important biological and physical phenomena.

The AmeriFlux Network provides high-quality 

information about carbon and energy fluxes in 

different terrestrial ecosystems. Thousands of scientists download AmeriFlux and Fluxnet datasets 

annually, and publications from this data use have 

transformed current understanding of the processes regulating photosynthesis, respiration, and 

environmental factors that influence net ecosystem carbon balance. With 110 registered active 

sites, the network is arguably the largest and most 

successful federally funded ecosystem-observing 

network in the United States. The ARM Research 

Facility provides measurements of the physical 

and chemical properties of atmospheric aerosols, 

clouds and their microphysical compositions, and 

radiative fluxes, as well as a suite of other meteorological measurements at four fixed sites and two 

mobile facilities. These measurements represent 

the most comprehensive observations of aerosols 

and clouds available in the world. ARM data have 

been used by thousands of scientists worldwide 

to understand aerosol and cloud processes, as well 

as aerosol-cloud-precipitation interactions, and to 

improve models. The ARM measurement strategies 

are being adopted by other countries. NGEE–Arctic 

and NGEE–Tropics have created important new 

science infrastructures for investigating ecosystem responses to global environmental change in 

remote and logistically challenging environments. 

NGEE–Arctic has led to recent breakthroughs 

in understanding how permafrost influences 

hydrologic flows and biogeochemical processes in 

high-latitude ecosystems. NGEE–Tropics, in turn, 

has provided insight about the sensitivity of tropical 

forests to drought and other environmental changes 

associated with El Niño. More recently, BER has 

developed the concept of integrated field laboratories (IFLs) with the aim of studying interactions 

along important lateral and vertical gradients that 

link human population centers with surrounding 

natural environmental systems. The IFL conceptual 

design included themes associated with coastal 

regions, arid lands, mountain watersheds, agriculture, and urban ecosystems.

Building on these BER successes, new observational 

technologies would further advance the understanding of environments in rapidly changing regions 

both within and outside the United States. Such 

understanding is strategically important for energy 

security, as environmental and societal changes are 

connected through myriad energy, water, biogeochemistry, and socioeconomic pathways at a broad 

range of temporal and spatial scales.

Research Needs and Knowledge Gaps

Outfit AmeriFlux with New Technologies for 

In Situ Sequencing and Expanded Measurements. New investment in the AmeriFlux Network 

has the potential to transform ecosystem science. 

One challenge is to link variation in ecosystem 

fluxes with changes in plant gene expression 

and soil microbial community dynamics to help 

researchers probe the fundamental mechanisms 

regulating seasonal and interannual changes in 

the carbon balance of terrestrial ecosystems. This 

challenge could be achieved by developing and 

deploying in situ sequencing technologies at several core AmeriFlux sites. New information about 

the changing composition of fungal and bacterial 

communities could enable significant advances in 

modeling ecosystem respiration as well as methane 

(CH4) and nitrous oxide (N2O) trace gas production. Concurrent information on gene expression 

in plants could revolutionize understanding of cold 

hardening (and recovery) processes in temperate 

and high-latitude ecosystems, time-evolving limits to photosynthesis, and production of different 

volatile organic compounds that create clouds and 

feedbacks via atmospheric chemistry. Another 
28 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

critical AmeriFlux frontier involves more quantitatively linking carbon and water fluxes to ecosystem 

demographics, landscape dynamics, land use, and 

land management. First, new investment in tree and 

shrub demography at core sites would considerably 

strengthen the use of AmeriFlux to study climate 

change impacts on vegetation dynamics and to test 

next-generation demography models such as the 

Functionally Assembled Terrestrial Ecosystem Simulator (FATES) model, which is being integrated 

into the E3SM. Second, systematic deployment 

of ecosystem-level lidar and remote-sensing measurements have considerable potential for mapping 

three-dimensional (3D) changes in forest composition before and after climate extremes and other 

processes that influence ecosystem structure and 

flux dynamics. Third, new tower clusters along land 

use and management gradients are essential in developing a quantitative understanding of how humans 

are modifying ecosystem fluxes at regional and continental scales.

Add ARM Capabilities for Understanding 

Aerosol-Cloud Interactions, Cloud Radiative 

Feedbacks, and Extreme Precipitation. New 

investment in the ARM Facility has potential for 

characterizing the coupling of aerosols and clouds 

with atmospheric turbulences and convective 

systems, which is currently lacking. Such information is essential for understanding aerosol-cloud 

interactions, cloud radiative feedback, and extreme 

precipitation. Needed to constrain models are measurements of 3D atmospheric motion fields from 

remote-sensing instruments and drones across a 

range of scales from a few hundred kilometers to a 

few meters, along with concurrent measurements of 

aerosols and clouds.

Develop New Remote-Sensing Technologies 

for Atmospheric, Land, and Urban Systems.

Another challenge for BER is the design and 

deployment of new systems to remotely monitor 

ecosystem properties and environmental variables 

by exploiting emerging technologies. Over the past 

decade, several remote-sensing techniques have 

transformed understanding of Earth system science. 

Paradigm-changing observing systems include 

the Argo float network in the oceans, AmeriFlux 

Network on land, ARM scanning cloud radars, and 

the National Aeronautics and Space Administration’s (NASA) constellation of Earth-observing 

satellites. The pace of development in the 

environmental-sensing field continues to accelerate 

and includes advances in drone, lidar, and subsurface imaging technologies as well as improvements 

in networking systems. An important science challenge for BER is to develop new systems that provide new environmental information across a range 

of temporal and spatial scales that are currently 

inaccessible by NASA or other federal agencies. 

Development of a strategic plan and the specific targeting of several achievable science objectives in the 

atmosphere, land, and urban domains are important 

near-term requirements for BER to maintain its 

commitment to excellence within this field. New 

technologies that enable mapping the 3D structure 

of trace gas, heat, and wind and water flows within 

cities, for example, may create new opportunities for 

the design of sustainable energy solutions.

Deploy IFLs, Conduct Regional-Scale Experiments Examining Biosphere–Climate System Feedbacks, and Design Expanded FACE 

Studies. Three major research needs are identified for future BER field experiments. The first 

is to develop IFLs that target biogeochemical, 

energy, and water flows between urban areas and 

surrounding ecosystems. Important gradients 

include connections between cities and nearby 

coastal ecosystems, mountain watersheds, arid 

lands, and croplands. IFLs should be located in 

rapidly changing regions or areas that are strategically important for energy security. They also 

need to exploit existing BER science capabilities 

across biological and Earth science disciplines 

(e.g., ARM, AmeriFlux, and DOE’s Joint Genome 

Institute and Environmental Molecular Sciences 

Laboratory), while simultaneously fostering new 

ones. The second research need is to create new 

experiments that capture feedbacks between the 

biosphere and physical climate system at regional 

scales. An important example is the analysis of 
November 2017 Biological and Environmental Research Advisory Committee 29

Chapter 3 — Earth and Environmental Systems Sciences

how different land uses modify surface fluxes and 

atmospheric composition that, in turn, modify 

regional climate and, ultimately, the sustainability of human enterprise within a region. Existing 

NGEEs and ARM facilities have not yet embraced 

these coupling and feedback challenges. Finally, 

BER’s past and current investments in FACE have 

yielded datasets that have proven essential for 

developing predictive models of the global carbon cycle. Yet escalating costs have limited the 

expansion of these experiments into new regions 

where uncertainties remain extremely high. Considering new breakthroughs in simulating air flows 

through forest canopies, control systems, and 

isotopic labeling approaches, BER should revisit 

whether new FACE designs could be developed 

that would enable a wider suite of deployments. 

The direct response of ecosystems to rising atmospheric CO2 remains one of the most important 

sources of uncertainty in ESMs, on par with uncertainties associated with aerosol direct and indirect effects and exceeding those associated with 

climate-carbon feedbacks.

Tightly Couple Models and Experiments to Test 

Predictions and Hypotheses and Inform Observational Strategies. Seamless model-experiment 

integration is critical for measurements to effectively inform model development and hypothesis 

testing. Models are increasingly capable of resolving the complexity of processes exhibited within 

observations and interpreting causal pathways. 

Combining inverse modeling, data assimilation 

strategies, and novel metrics can help design 

better observational systems to test relationships important to predictions. The past decade 

has seen tremendous growth in new capabilities 

such as machine learning and other data science 

approaches. Consequently, the time is right to 

invest in capabilities synthesizing different fields of 

expertise to create new process-level understanding 

of many important biological and physical phenomena (see also Chapter 6 on Data Analytics and 

Computing, p. 71).

Grand Challenge 3.3 

Advance basic knowledge and scale-aware simulation capability for Earth system feedbacks associated with aerosols and moist processes to better 

quantify aerosol forcing, precipitation changes, and 

extreme events with consequences for energy and 

water cycles, global distribution of nutrients, and 

human health.

Resolving Critical Uncertainties for 

Aerosols, Cloud-Climate Feedbacks, 

and Precipitation

Aerosols originate from both natural and anthropogenic sources. Atmospheric aerosols directly affect 

Earth’s energy balance and air quality. Depending 

on the physical and chemical properties of aerosols, 

direct radiative forcing on the Earth can be negative 

or positive due to scattering of solar radiation to 

space and aerosol absorption of solar and infrared 

radiation. Aerosols can modify the number density 

concentration of cloud particles and, thus, cloud 

particle sizes, which impact cloud optical properties. 

Cloud particle sizes, in turn, affect the precipitation 

efficiency and lifetime of clouds. These indirect 

effects of aerosols on Earth’s energy budget and 

precipitation still are not well understood, and their 

simulations are poorly constrained by observations, 

so aerosols represent one of the largest uncertainties 

in current ESMs (Boucher et al. 2013; Nazarenko 

et al. 2017).

Clouds strongly affect Earth’s energy budget by 

reflecting solar radiation and trapping infrared radiation. Their net radiative impact depends on cloud 

temperature, hydrometeor particle sizes, and mass of 

liquid and ice in the clouds. Clouds form as a result 

of supersaturation by the lifting of moist air in the 

presence of aerosols and atmospheric flows across 

a broad spectrum of temporal and spatial scales. 

How clouds respond to climate change determines 

whether they positively or negatively feed back to 

a climate forcing such as atmospheric greenhouse 

gases (GHGs; Geoffroy et al. 2017).
30 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Current ESMs require parameterizations of many 

atmospheric processes important to aerosols and 

clouds. The large uncertainties of current ESMs in 

simulating the indirect effects of aerosols on Earth’s 

energy budget, cloud-climate feedback processes, 

and intensity-frequency distribution of precipitation 

seriously limit the utility of these models in simulating energy and water cycles and projecting their 

future changes.

Resolving these areas of uncertainty will advance 

efforts to predictively understand the Earth system 

and its variability on a number of fronts. For example, deeper insights into aerosols and their indirect 

effects will improve simulations of aerosol forcing 

on the Earth system from natural and anthropogenic 

sources. Meanwhile, increased knowledge of clouds 

and their interactions with turbulence and the Earth 

surface would enable researchers to better quantify 

cloud-climate feedbacks and reduce uncertainties 

of future climate change projections. Better understanding and an improved ability to model mesoscale convective systems (MCSs) will lead to better 

simulations of the global water cycle and prediction 

of extreme weather events that affect the production, 

transport, and use of human energy systems. Finally, 

robust, scale-aware physical parameterizations will 

enable practical applications of ESMs to predict 

water, energy, and carbon cycles as well as their 

impacts on human energy systems.

Research Needs and Knowledge Gaps

Improve the Understanding of Biogenic Aerosol 

Emissions, New Particle Formation, and Aerosol Evolution in the Atmosphere. Knowledge of 

the chemical and physical properties of aerosols, 

including their mixing states, is needed to understand their growth and evolution. Aerosol effects 

on mixed-phase clouds are known to significantly 

affect cloud lifetime and precipitation, but this 

knowledge is incomplete and their simulations are 

highly uncertain. Also poorly understood is the 

nucleation of ice cloud particles by aerosols. Both 

laboratory experiments and field measurements 

are needed to test and improve the fundamental 

theory of new particle formation (see Fig. 3.1. 

New Particle Formation, p. 31), secondary organic 

aerosol (SOA) growth, and aerosol nucleation of 

ice particles. Process models should be developed 

based on first principles of physics and chemistry 

to connect measurements with models. Parameterizations should be developed on the basis of theoretical understanding and process models for use in 

coarse-resolution ESMs for long-term simulations.

Understand and Accurately Simulate Cloud 

Interactions with Atmospheric Turbulence and 

Earth’s Surface. Clouds primarily develop in turbulent atmospheric flows, and they, in turn, affect turbulence flows through their effects on radiation and 

latent heating. Accurately simulating turbulencecloud coupling is essential for simulating both the 

aerosol indirect effect and cloud-climate feedback. 

The ubiquitous low clouds over the eastern oceans in 

the subtropics exist due to atmospheric turbulence 

generated by cloud-top radiative and evaporative 

cooling. Surface heterogeneities, including terrain, 

vegetation, and soil temperature and moisture, influence atmospheric turbulence and cloud formation. 

Research is needed to develop a hierarchy of models 

to describe cloud processes—from single air parcels 

to single shallow cumulus clouds, to large eddies of 

several kilometers—to enable ESMs to explain and 

parameterize the coupling of atmospheric turbulence and clouds and the roles of surface heterogeneities and large-scale atmospheric conditions. New 

observations should be made to better quantify the 

interaction among turbulence, clouds, and surface 

fluxes and to constrain the models.

Characterize Mesoscale Convective Systems and 

Their Role in Extreme Weather Events. MCSs 

are responsible for some of the most damaging, 

high-impact weather events such as tornados, hail, 

and extreme precipitation. Recent findings suggest 

that the increased frequency of longer-lasting MCSs 

is responsible for increased extreme precipitation 

over the central United States in the past decades 

(Feng et al. 2016). MCSs self-organize to exhibit a 

variety of dynamical structures strongly modulated 

by large-scale atmospheric and surface conditions 
November 2017 Biological and Environmental Research Advisory Committee 31

Chapter 3 — Earth and Environmental Systems Sciences

(Houze et al. 2015). The initiation, propagation, 

and decay of MCSs and their structures of dynamics 

and thermodynamics still are not well understood 

and are poorly simulated in models. Research is 

needed to characterize MCSs with simultaneous 

measurements of cloud microphysics and precipitation, updrafts and downdrafts, cold pools, anvils, 

gravity waves, and environmental conditions of the 

atmosphere and surface. Research also is needed 

to improve theoretical understanding of the organization within MCSs, their propagation, and time 

evolution under different environmental conditions. 

A hierarchy of models can be used to describe their 

mechanisms, leading to their realistic simulation 

and prediction. Research is needed to understand 

the predictability limit of MCSs through data 

assimilation and hindcast and forecast experiments 

(see also Chapter 8 section, Planet Scale, p. 109).

Develop Scale-Aware Physical Parameterizations, Particularly for Deep Convection. The 

impact of subgrid-scale processes on resolved-scale 

dynamics and thermodynamics is represented by 

physical parameterizations in ESMs. By definition, 

the subgrid-scale processes change with model 

resolution, but physical parameterizations in current models are designed for resolutions limited 

by computational resources. As high-resolution 

and variable-resolution models become practical, the physical basis and formulation of some 

existing parameterizations are no longer valid. 

Research is needed to develop scale-aware physical 

Fig. 3.1. New Particle Formation. New particles form in the outflow region of earlier convective clouds, leading to high 

concentrations of small particles in the free troposphere. These small particles are injected into the boundary layer by 

the downward motions in the convective system, where they interact with volatile organic compounds (VOCs) and other 

gases emitted by vegetation from the Earth’s surface to grow and become cloud condensation nuclei. [Image courtesy Luiz 

Machado, National Institute for Space Research, and Jian Wang, Brookhaven National Laboratory]
32 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

parameterizations for models across resolutions. A 

particular challenge is the treatment of deep convection that barely can be resolved by global models but 

may occupy a large fraction of a model grid cell. As 

deep convection may organize to MCSs, the inability to resolve the former also limits representation of 

the latter in climate models. Research on new methodologies is needed to overcome this challenge.

Grand Challenge 3.4 

Advance modeling and understanding of important ecological, biological, and carbon cycle interactions and feedbacks in the climate system to 

identify potential tipping points and possible 

energy strategies.

Developing a Rigorous Understanding of 

Ecosystems and the Global Carbon Cycle

Biogeochemical feedbacks have the potential to 

considerably alter future trajectories of atmospheric 

composition and climate in the 21st century (see 

Fig. 3.2. Biogeochemical Feedbacks, this page). Multiple lines of evidence suggest that forest responses 

to rising atmospheric CO2 are responsible for a large 

component of the contemporary terrestrial carbon 

sink (Pan et al. 2011; Schimel et al. 2015). However, several massive terrestrial carbon reservoirs 

may be vulnerable to future change. They include 

carbon stored in permafrost soils within boreal and 

tundra biomes (Schuur et al. 2015), aboveground 

biomass in tropical forests (Brienen et al. 2015), 

carbon preserved under anoxic conditions in boreal 

and tropical peatlands (Turetsky et al. 2015), and 

organic matter associated with mineral soils that 

may be sensitive to changes in temperature or soil 

moisture (Pries et al. 2017). Within oceans, the 

net air-sea CO2 flux will respond to future changes 

in the solubility of dissolved inorganic carbon and 

ocean circulation. A weakening of ocean mixing and 

overturning could reduce the ocean’s ability to soak 

up anthropogenic CO2 that is emitted into the atmosphere, directly by reducing surface inorganic carbon flows into the interior (Schwinger et al. 2014) 

and indirectly by reducing the availability of surface 

Fig. 3.2. Biogeochemical 

Feedbacks. A synthesis 

of the magnitude of biogeochemical feedbacks on 

climate. Key: BVOC, biogenic 

volatile organic compounds; 

C, carbon; CH4, methane; 

CO2, carbon dioxide; DMS, 

dimethyl sulfide; K, Kelvin; 

m, meter; N, nitrogen; N2O, 

nitrous oxide; W, watt. [Image 

reprinted with permission 

from Ciais, P., et al. 2013. 

“Carbon and Other Biogeochemical Cycles.” In: Climate 

Change 2013: The Physical 

Science Basis. Contribution of 

Working Group I to the Fifth 

Assessment Report of the 

Intergovernmental Panel on 

Climate Change (Eds.: Stocker, 

T. F., et al.), Cambridge University Press, Cambridge, UK, and 

New York, NY, USA.]

Land C response to CO2

Ocean C response to CO2

including N-cycle

including N-cycle

Land C response to climate

Ocean C response to climate

 Permafrost CO2

 Climate CH4 lifetime

 BVOC on ozone

 Climate-aerosol

 Climate-dust

 Climate-DMS

 Climate-ozone

 Fire

 Climate on N2O

 Wetlands CH4

(W m–2 K–1)

–0.4 –0.2 0 0.2 0.4

(W m–2 K–1)

–1.5 –1 –0.5 0 0.5 1 1.5
November 2017 Biological and Environmental Research Advisory Committee 33

Chapter 3 — Earth and Environmental Systems Sciences

nutrients that regulate the strength of the biological 

pump (Galbraith and Martiny 2015). Combined, 

the impact and uncertainty associated with these 

biogeochemistry feedbacks are as large as those 

associated with clouds and ocean dynamics (Gregory et al. 2009). Thus, an improved understanding 

of carbon cycle processes is essential for developing 

predictive models of Earth system dynamics over 

time scales of years to centuries.

Apart from understanding Earth system feedbacks, 

fundamental knowledge of ecosystems is essential for 

understanding their resilience to changes in human 

and environmental drivers. Accurate predictions of 

water availability for energy and agriculture, for example, depend on a mechanistic representation of vegetation controls on evapotranspiration in upstream 

watersheds. Similarly, analysis of the factors that 

influence U.S. food security can benefit from a mechanistic representation of crops in ESMs, enabling 

yield predictions that respond simultaneously to 

changes in multiple environmental factors. In oceans, 

future changes in net primary production and interior 

oxygen availability may limit the range of important 

commercial fisheries (Deutsch et al. 2015). Understanding the mechanisms that regulate the carbon 

sequestration potential of ecosystems is important to 

systematically explore future land use scenarios that 

may limit carbon accumulation in the atmosphere, 

thus helping to stabilize the Earth system.

Achieving a rigorous understanding of the global 

carbon cycle and ecosystems is necessary for understanding long-term Earth system consequences 

such as floods, water availability, wildfire damage, 

and heat waves in different energy scenarios. Such 

knowledge also is critical for the design of energy 

solutions that optimize outcomes across different 

economic sectors and regions. Moreover, maintaining a robust biosphere is an essential foundation for 

national and economic growth as well as regional 

and global food security.

Research Needs and Knowledge Gaps

Predict the Changing Structure and Composition of Ecosystems Across the Global Land 

Surface. Although considerable progress has been 

made during the past decade in understanding 

controls on tree mortality (Anderegg et al. 2015), 

the mechanistic representation of underlying physiological processes in ESMs remains rudimentary, 

with advances expected from better simulation of 

plant-soil hydrologic coupling and stand dynamics 

and from the use of experimental testbeds for model 

development and evaluation. Environmental controls on seed dispersal, recruitment, migration, pest 

outbreaks, wildfires, invasive species, and changing 

land use patterns represent equally important controls on vegetation dynamics at region-to-continent 

scales. Recognizing this broader suite of processes 

and interactions will be necessary for making progress on this challenge. To create a new synthetic 

understanding of vegetation dynamics, new investments will need to target experimental manipulation, remote ecosystem monitoring, mechanisms 

coupling human and natural ecosystems, and simulation capacity to rapidly and systematically explore 

a wide range of future scenarios.

Improve Model Representation of the Terrestrial Carbon Sink and Responses of Key Systems 

(e.g., Arctic, Tropics, and Peatland) to Change.

As ESM simulation of the global carbon cycle 

is becoming more mature, critical uncertainties 

remain that limit the ability to predict feedbacks and 

future atmospheric composition with confidence. 

Perhaps the most significant uncertainty originates 

from the CO2 fertilization effect—specifically, the 

degree to which the terrestrial biosphere is taking 

up carbon in response to increasing atmospheric 

CO2. Model predictions of the magnitude of the 

contemporary terrestrial carbon sink vary by more 

than a factor of two in current ESMs. Contemporary 

and future responses of high-latitude and tropical 

ecosystems to different global change drivers remain 

the largest source of uncertainty in carbon cycle 

predictions. Thus, model-experiment integration 

targeting these regions is essential for making future 

progress. Other carbon cycle challenges will require 

investments in new areas. In Indonesia, decomposition and burning of peatlands during El Niño events 

have considerably accelerated annual carbon losses 
34 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

associated with land use change, while creating 

regional haze events that cause premature deaths 

(Koplitz et al. 2016). In northern tundra and boreal 

forest ecosystems, peatland also represents a very 

large and vulnerable carbon reservoir that has the 

potential to considerably modify future atmospheric 

composition (Turetsky et al. 2015). Thus, accurate 

peatland representation in ESMs represents an 

important need essential for future carbon cycle and 

air quality predictions.

Accurately Simulate Atmospheric CH4 and N2O.

Fully coupled Earth system simulation of biogeochemical cycles other than CO2 remains nascent, 

so another important challenge is to simulate the 

transient dynamics of atmospheric CH4 and N2O. 

The biogeochemical processes regulating these gases 

have undergone profound modification by human 

activities, and their future trajectories remain highly 

uncertain. These gases have the potential to considerably modify emissions scenarios. For CH4, new 

investment is needed in land use model development, 

complementing the existing capability to simulate 

CH4 production and oxidation in soils. A fully coupled nitrogen cycle will require similar investments in 

land use, along with explicit mass-conserving simulation of land-to-ocean nutrient flows and better representation of nitrogen and oxygen dynamics in the 

ocean. Although state-of-the-art ESMs now simulate 

the influence of nitrogen limitation on photosynthesis, the model’s ability to simulate preindustrial atmospheric N2O mole fractions and the transient rise of 

this trace gas over the past 200 years represents an 

important (and unrealized) mechanistic test of global 

nitrogen cycle knowledge.

Develop New Approaches to Couple and Simulate Land, Aquatic, and Ocean Biogeochemical Cycles. Coastal ecosystems are vulnerable to 

changes in human systems and environmental conditions such as extreme events. Developing realistic 

future scenarios of ocean ecosystem responses to 

global change requires new approaches for coupling 

land, aquatic, and ocean biogeochemical cycles, 

including the flow of freshwater, nutrients, organic 

carbon, and sediments from terrestrial ecosystems 

to the river and coastal ocean. Understanding and 

modeling plant, soil, and hydrologic processes 

across land, river, and coastal systems are important 

as these processes exert a fundamental control on 

biogeochemical interactions. Changes in both the 

frequency and magnitude of hydrologic extremes, as 

well as changes in land management, will have significant impacts on nutrient flows and their ecological 

impacts. A mechanistic understanding of this coupling also is required for predicting nutrient trapping 

in coastal sediments, and, ultimately, the delivery of 

phosphorus, nitrogen, and iron to the open ocean, as 

well as how extreme events such as tropical cyclones 

and associated storm surge and coastal inundation 

may influence biogeochemistry in the coupled landriver-ocean system. Anthropogenic modification of 

river inputs is likely to be considerable over the next 

several decades with expected increases in global 

agriculture. Sustained agricultural and urban inputs 

have the potential to modify the strength of the 

biological pump, as well as coastal and open ocean 

interior oxygen concentrations.

Develop a Quantitative, Multiscale Understanding of the Coupling Mechanisms Associated with 

Reactive Trace Gases and Aerosols. Over the 

past decade, recognition of reactive trace gases and 

aerosols as important drivers of land and ocean ecosystem dynamics has increased. Examples of coupling mechanisms include the effects of aerosols on 

diffuse light; the impact of ozone on photosynthesis 

and canopy conductance; and the redistribution of 

nutrients by dust storms, wildfires, and spores. A key 

challenge within this emerging field is the need to 

develop a quantitative understanding of the impact 

of the different coupling mechanisms across a range 

of spatial and temporal scales. These mechanisms 

often are obscured in the observational record as a 

consequence of extreme events modifying multiple 

aspects of atmospheric composition. For example, large volcanic eruptions can profoundly affect 

diffuse light, yet these events also influence tropospheric hydroxyl concentrations and iron deposition 

in remote ocean regions. Similarly, drought extremes 

in the tropics associated with El Niño trigger massive biomass burning events that simultaneously 
November 2017 Biological and Environmental Research Advisory Committee 35

Chapter 3 — Earth and Environmental Systems Sciences

modify surface energy fluxes, diffuse light, ozone, 

moisture availability, and nutrient inputs. Continued 

investment in field campaigns, such as BER’s Green 

Ocean Amazon campaign, and model analysis of 

biosphere-atmosphere interactions are essential for 

making further progress in this important frontier.

Grand Challenge 3.5 

Characterize, understand, and model the complex, multiscale water cycle processes in the Earth 

system including the subsurface to understand 

and predict water availability and human system 

response to extremes.

Advancing Water Cycle Research

Freshwater is essential to life and supports myriad 

human activities that in turn alter freshwater availability. Water cycles through the Earth system as 

water evaporates from the land and ocean surface to 

reside as water vapor and clouds in the atmosphere. 

Precipitation from clouds changes soil moisture, 

snow and ice, groundwater, and runoff over land, 

and precipitation and river runoff change ocean 

salinity and stratification and sea level. Terrestrial 

moisture status and ocean stratification influence 

evaporation from the land and ocean surface. 

Human activities such as water extraction, reservoir 

operation, irrigation and agricultural production, 

and industrial and municipal water use have significantly altered the water cycle (Postel et al. 1996; 

Wada et al. 2010; Richey et al. 2015). Processes that 

are integral to the water cycle vary over a considerable range of spatial and temporal scales, challenging both observation and modeling to understand, 

quantify, and predict the properties of these processes as well as their variability and change rates. 

Water cycle processes are major sources of uncertainty in modeling and quantifying climate sensitivity because these processes play a key role in water 

vapor, cloud, surface albedo, and carbon cycle feedbacks. Uncertainty in predicting water cycle changes 

also has direct implications for predicting freshwater 

availability, sea level rise, and extreme events such 

as floods, droughts, and storm surges, all restraining 

the ability to assess human system vulnerability to 

weather and climate extremes.

The highly coupled multiscale, multiphase nature of 

water cycle processes has long been a major challenge 

confounding modeling and prediction. Increasing 

grid resolution has yielded some improvements in 

modeling the spatial distribution of clouds, precipitation, soil moisture, and snowpack, but biases in simulating water cycle processes in ESMs remain large.

Despite increasing recognition of the role of subsurface water processes in modulating surface fluxes and 

plant functioning, growth, and mortality, there are 

gaps in understanding and modeling subsurface water 

processes, partly because of limited measurements 

and modeling across local-to-watershed and large 

river basin scales. Global estimates of groundwater 

storage are extremely uncertain, varying by an order 

of magnitude depending on the data and methods 

used (Famiglietti 2014; Gleeson et al. 2016). Human 

systems are beginning to be incorporated into terrestrial models, but their interactive role in the integrated water cycle of the human-Earth system is still 

largely unexplored.

Resolving these water cycle uncertainties cuts across 

all BER Earth system science research needs because 

water is integral to all Earth system processes through 

its indispensable role in connecting the water, energy, 

and carbon cycles. Quantifying the spatiotemporal 

distribution and predicting the changes in freshwater supply, as well as understanding the drivers of 

extreme events and their changes, are critical for supporting DOE’s energy mission as energy production 

and use are intimately linked to water supply and 

quality through the energy-water nexus. Therefore, 

an urgent need remains to characterize, understand, 

and model the complex, multiscale water cycle for 

understanding and predicting freshwater availability 

and assessing the vulnerability of human systems to 

extremes and prolonged climatic stress.

Research Needs and Knowledge Gaps

Leverage Advances in Global Cloud-Resolving 

Modeling. The multiscale modeling challenge 
36 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

in Earth system modeling is most acute for the 

water cycle because errors in modeling water cycle 

processes at small temporal and spatial scales 

can upscale through water cycle feedbacks and 

water-energy-carbon interactions to affect larger-scale 

and longer-term Earth system predictions. Strong 

evidence suggests that when atmospheric models 

resolve clouds and convection, significant improvements in model fidelity are possible, such as more 

realistic precipitation characteristics (Kendon et al. 

2012; Kooperman et al. 2016; Gao et al. 2017) and 

land-atmosphere feedbacks (Hohenegger et al. 2009; 

Leung and Gao 2016). Global cloud-resolving modeling with grid spacings of 4 km or less is becoming 

feasible with advances in computing resources for 

short-term simulations and weather forecasting. Routinely running cloud-resolving models coupled with 

land surface models in weather forecasting mode at 

continental and global scales can provide the foundational knowledge needed to improve the models 

for use in coupled ESMs. Such endeavors should 

be key activities of the computational user facilities 

discussed in Grand Challenge 3.1 on Earth system 

modeling, p. 24.

Develop Multimodel Ensembles of CloudResolving Forecasts. Collaborations with U.S. and 

international modeling centers on cloud-resolving 

weather forecasting is highly beneficial to enable 

comparison and shared expertise and data for rigorous forecast evaluation and diagnosis. Exploring 

forecast sensitivity to model formulations and 

parameterizations of unresolved processes can guide 

model development. Production of a multimodel 

ensemble of cloud-resolving forecasts of extreme 

events that are poorly characterized by observations can advance understanding of their large-scale 

drivers and predictability. With significant gaps 

in understanding the governing characteristics of 

extreme events, such as their intensity, duration, 

size, frequency, and co-occurrence, another research 

need is to develop dynamical and statistical theories 

about which factors control these characteristics. 

The multimodel ensemble forecasts will provide 

unprecedentedly high resolution data for knowledge 

discovery through machine learning and analysis of 

large datasets, combined with the development of 

theories and novel metrics and diagnostics.

Improve Convection and Circulation Simulations to Better Project Regional Precipitation 

Changes. Global precipitation changes in a warmer 

climate are constrained by the global energy budget 

to be roughly 2% per degree Celsius of warming 

(Held and Soden 2006). However, large uncertainty 

remains in projecting future changes in regional 

precipitation because precipitation changes can 

be dominated by poorly constrained circulation 

changes whose interactions with convection are 

not well understood or simulated by models (Bony 

et al. 2015). The substantial variability of large-scale 

circulation (Shepherd 2014) is only beginning 

to be characterized using large-ensemble simulations (Kay et al. 2015). Computational advances 

enabling convection-permitting simulations and 

large-ensemble simulations may facilitate the development of theories to constrain regional precipitation changes in a perturbed climate. Modeling 

hierarchies may be used to disentangle different 

physical and dynamical mechanisms and develop 

emergent constraints on hydrological cycle changes 

(Klein and Hall 2015).

Improve Subsurface Process Fidelity in Terrestrial Models. Terrestrial subsurface processes influence the partitioning of surface sensible and latent 

fluxes and, hence, land-atmosphere interactions and 

local water recycling. They also directly influence 

groundwater, an important source of freshwater 

supply. Measurements have provided evidence that 

plants can access moisture from diverse sources for 

transpiration (Thompson et al. 2011), and subsurface hydrology can be strongly influenced by geology and lithology, for example, when rock fractures 

(Salve et al. 2012) and soil micro- and macropores 

(Beven and Germann 2013) dominate the flow 

paths. However, land-surface models used in ESMs 

have limited representations of soil hydrology, 

plant-root-soil moisture interactions, surface water–

groundwater interactions, and terrestrial-aquatic 

interactions. These limitations hinder skillful simulation of the spatial and temporal variability of 

evapotranspiration and recharge to groundwater, 
November 2017 Biological and Environmental Research Advisory Committee 37

Chapter 3 — Earth and Environmental Systems Sciences

with implications for modeling flood generation, 

precipitation-soil moisture feedback, vegetation 

drought response, and land carbon sinks. Improving 

representation of such processes in terrestrial models is an ongoing need. Recent efforts to develop 

global hydrogeological datasets of soil permeability 

and porosity (Gleeson et al. 2014), soil depth (Pelletier et al. 2016), and lithology (Hartmann and 

Moosdorf 2012) provide a significant opportunity 

for global high-resolution modeling of subsurface 

processes (e.g., Gleeson et al. 2016).

Understand the Effects of Human-Earth System 

Interactions on the Water Cycle. Human systems 

such as dams and reservoirs, irrigation, and crop 

management have large footprints on the regional 

and global water cycle. However, the mechanistic 

nature of the footprints and how they may change 

in the future are not well understood. Efforts in 

modeling human systems generally focus on global 

modeling using conceptual representations or 

regional modeling with detailed, process-based 

representations that may not be transferable to the 

continental or global scale for lack of global input 

data. Human systems data and coupled models must 

be developed to explore the dynamics of humanEarth system interactions and their impacts on the 

water cycle. Combined with data assimilation and 

cloud-resolving modeling, high-resolution terrestrial 

models coupled with human systems models may 

provide much improved quantification of hydrological fluxes and storage to better characterize the water 

cycle and its historical changes and improve understanding of the drivers of water cycle changes.

Grand Challenge 3.6 

Understand the time-dependent processes and 

mechanisms associated with melting glaciers, 

ice caps, and ice sheets and their contributions to 

regional sea level rise.

Predicting Changes in Sea Level Rise

Over the past century, sea level has risen roughly 

20 centimeters (cm), largely due to increases in 

ocean heat content and, to a lesser extent, surface 

melt from the Greenland ice sheet and global mountain glaciers (Hay et al. 2015). By the end of this 

century, sea level will rise another 50 to 100 cm, and 

possibly more, from continued warming and melt 

(IPCC 2013). The larger concern involves potential 

changes in the force balances that currently limit 

rates of ice flow through marine outlets. For instance, 

changes in the delivery of heat to ice shelves could 

lead to ice shelf thinning, reducing the buttressing 

effect of confined outlets. This change could result 

in significant acceleration of ice flows, potentially 

activating ice streams deep into the interior of their 

catchments. Moreover, several catchments are 

known to reside in marine basins with reverse-slope 

bedrock geometry, a configuration that has been 

associated with a hypothesized marine ice sheet 

instability mechanism. The concern is that changes 

in ice flow dynamics could significantly increase the 

rate of sea level rise, potentially generating an additional meter per century or more.

The challenge for predicting ice flow changes is that 

many of the controlling processes are not directly 

observable. Around the Antarctic Ocean, for 

instance, heat delivery to ice shelves is mediated by 

upwelling of relatively warm, salty water masses onto 

the continental shelf and by ice-shelf boundary layer 

processes (see Fig. 3.3. Melting Ice Shelf, p. 38). 

These boundary conditions and processes are poorly 

observed and resolved in numerical models. Similar 

observational challenges affect understanding of subglacial hydrologic systems and fracture mechanics.

Changes in sea level potentially are a predictable 

and consequential outcome of human activity on 

the environment, with significant implications for 

the resilience of coastal and offshore energy infrastructures. BER can play a pivotal role in developing 

advanced modeling tools that can leverage a limited 

observational record to interpret the processes 

involved with these changes. However, developing 

a modeling capability is only part of the challenge. 

Longer prediction time scales and the limited observational record will make difficult the testing and 

evaluation of the contributions of new capabilities 

to improved performance. The development of 
38 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

modeling capabilities within an uncertainty quantification framework is therefore needed. Such a 

framework can play an important role in synthesizing the modeling and observational components of 

research endeavors.

Research Needs and Knowledge Gaps

Model Processes and Mechanisms Controlling 

Ice Melt and Dynamics. The ability to predict 

decade-to-century changes in global glacial ice 

inventories and their effect on regional sea level 

rise depends on understanding and representing 

within numerical models the time-dependent processes and mechanisms controlling ice melt and ice 

dynamics. Such predictions also require understanding and modeling the set of gravitational, wind, and 

ocean current forces that affect the global distribution of ocean mass. The highest-priority model 

development needs, among others, in this regard 

are ice-ocean interactions, subglacial hydrology, and 

ice-fracture mechanics and calving. Because observations of these processes are severely limited, the 

community will need to adopt a much tighter coupling among observation, theoretical, and modeling 

activities to advance current understanding and test 

model predictive capabilities. Polar ice caps integrate 

and respond to environmental change on all time 

scales, including decades to centuries. Needed is a 

better understanding of how ice sheets participate 

in this variability. Attributing recent changes to particular causes requires recognizing in observations 

the signature of forced change, relative to unforced 

variability, coming from ice-ocean interactions, 

changes in subglacial hydrology, surface melt, or 

even longer-term adjustments from states established during the Quaternary and Holocene that still 

reside within the polar ice caps.

Develop Data Assimilation and Inversion Tools 

to Deal with the Dearth of Observational Data. 

Many of the scientific aspects to these challenges 

stem from the limited observational record with 

which to test hypotheses and advance understanding. A key capacity for dealing with this constraint 

is to develop tools for data assimilation and data 

inversion, which can be used to establish initial 

conditions for near-term predictions of changes in 

ice mass, as well as plausible model interpretations 

of uncertain physical properties or uncertain forcings. Such tools need to be refined further to make 

optimal use of available observations and infer 

uncertainties. Currently feasible is focusing on a few 

well-instrumented catchment basins that can serve 

as testbeds for developing understanding, predictive 

modeling capability, and future tests to establish 

model credibility in predictions of sea level rise.

Assess Uncertainties in Data and Modeling 

Methods and Forge Interagency Collaborations.

Pursuing multiple strategies is needed to estimate 

the boundary conditions and physical processes 

that affect sea level rise. This endeavor may involve 

gaining a deeper understanding of the limitations 

in observational and modeling methods for representing plausible alternatives. In many cases, 

Fig. 3.3. Melting Ice Shelf. The Larsen B ice shelf in Antarctica in 1995 (red) superimposed with its complete collapse 

in just over a month (yellow, green, and blue). Image 

captured by the moderate-resolution imaging spectroradiometer (MODIS) on the National Aeronautics and Space 

Administration’s (NASA) Terra satellite between January 31 

and March 5, 2002. [Image courtesy NASA]

March 5, 2002

Feb. 17, 2002

Jan. 31, 2002

1995
November 2017 Biological and Environmental Research Advisory Committee 39

Chapter 3 — Earth and Environmental Systems Sciences

incorporating statistical methods may be useful for 

simulating such uncertainties so that their effects 

may be properly represented in model predictions. Expertise in the observations and products, 

including ice core and ocean sediment data, radar 

images of ice sheet stratigraphy, geothermal fluxes, 

inferences of ocean and ice sheet bed geometry, and 

subsurface fluid flow, is important to complement 

modeling capabilities. Given the importance and 

scarcity of observations, interagency collaborations, 

particularly with research activities aimed at modeldata synthesis and discovery, are highly beneficial.

Grand Challenge 3.7 

Quantify the interplay between internally generated climate variability and externally forced 

response involving anthropogenic and natural factors and their relative roles in the time evolution of 

regional variability to understand predictability of 

the Earth system.

Providing Decadal Predictions 

for Climate–Human System Interactions

Over the next 20 years, policymakers will be planning how to adapt to climate change and effectively 

mitigate further changes. Climate science will need 

to provide more accurate climate predictions and 

more detailed assessments of the overall impacts of 

changes to Earth and human systems. To achieve 

this goal, reliable and decision-relevant climate 

model products with uncertainty information are 

needed. These products typically are produced 

by running a model initializing in the mid-1800s. 

It progresses forward in time, adding observed, 

time-evolving external forcings such as volcanic 

eruptions, solar variability, visible air pollution, and 

increasing GHGs, while looking to the mid-21st

century and beyond, where different emissions scenarios provide the major source of uncertainty. For 

nearer-term time scales, “decadal climate prediction” 

is an emerging field in climate science that aspires 

to make near-term predictions for the next 10 to 20 

years (Meehl et al. 2014). Credible predictions of 

climate and human systems, from subseasonal to 10 

to 20 years in the future, would support BER in providing a foundational science capability for planning 

future energy and resource needs. These decadal climate predictions use ESMs that are initialized with a 

set of climate-system observations at a specific time. 

This model is run forward for about 10 years, with 

the objective of simulating both the internally generated variability and response to external forcings. 

Thus, the challenge for near-term prediction is to 

quantify the interplay between the internal variability and response to external forcings with the goal of 

providing climate information to assess impacts.

This scenario can be illustrated by viewing the 

observational record of 20th century global temperatures as a “rising staircase” (Kosaka and Xie 2016). 

Even though CO2 has continuously increased over 

the 20th and early 21st centuries, the temperature 

response has not been a continuous increase. Rather, 

there have been 10- to 20-year periods of accelerated 

and slower warming (see Fig. 3.4. Recent Slowdown 

in Global Warming Symptomatic of Decadal Climate Variability, p. 40). Time-evolving changes to 

external forcings combine with internally generated 

10- to 20-year decadal variability to modulate what 

otherwise would have been a continuous increase 

of global temperatures due to the steady increase 

of CO2. For near-term predictions, the internal 

variability or noise is a critical part of the system to 

be predicted, involving the interplay of the internal 

variability with other externally forced responses.

Research Needs and Knowledge Gaps

Use Hindcast and Multiple Ensemble 

Approaches to Assess Short-Term and Regional 

Predictions. For decadal climate prediction, sets 

of “hindcasts” typically are performed with ESMs 

to quantify the predictability and reliability of the 

predictions. These hindcasts involve starting the 

model at certain times with climate system conditions observed at that time. Application of multiple ensemble methodology has achieved success 

in NWP for periods of a week or so. Applied to 

decadal climate prediction, each start date for each 

hindcast should have multiple ensemble members 
40 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Fig. 3.4. Recent Slowdown in Global Warming Symptomatic of Decadal Climate Variability. Time series of annual mean 

globally averaged surface temperature anomalies (red dots) based on data from Karl et al. (2015). The black line shows a longterm linear trend, computed from 1950 to 2014, forced mainly by increasing greenhouse gases. Green lines are multidecadal 

linear trends for positive phases of the interdecadal Pacific oscillation (IPO), blue lines for negative phases of the IPO. 

Multidecadal trend lines associated with IPO phases generally follow the long-term change in global temperature but differ 

intermittently depending on the phase. [Image courtesy National Oceanic and Atmospheric Administration. From Meehl, G. 

A. 2015. “Decadal Climate Variability and the Early-2000s Hiatus,” Variations 13(3). U.S. Climate Variability and Predictability 

Program. usclivar.org/sites/default/files/documents/2015/Variations2015Summer.pdf ]

to assess the uncertainty of the predictions on 

regional scales, as well as for time scales of a season 

to a decade in advance. More work is needed to 

explore the ensemble space, especially to provide 

credible, time-evolving probabilistic regional climate change information.

Develop Best Practices for Initializing ESMs with 

Observational Data. To improve predictions, a 

number of actions are needed. When an ESM is initialized with observations at a certain start date, the 

model starts to drift away from the observed state as 

a consequence of model biases. Adjustment of these 

biases is necessary before evaluating the hindcasts 

and predictions. Improving ESMs would reduce 

the need for bias adjustments. Additionally, there is 

no best practice for how to initialize a fully coupled 

ESM; a more thorough exploration is needed to 

determine the best way to initialize an ESM with 

observations of individual or combined states of the 

Earth system components, with and without data 

assimilation. Much work to date has focused on predicting ocean temperatures on decadal time scales, 

since that is where most of the mechanisms thought 

to produce decadal climate variability reside. More 

assessments are needed, however, of predictions 

over land, such as precipitation and temperature, on 

regional to local spatial scales and from seasonal to 

20 years ahead in temporal scales.
November 2017 Biological and Environmental Research Advisory Committee 41

Chapter 3 — Earth and Environmental Systems Sciences

Improve Understanding of How the Climate 

System Works on Decadal Time Scales. Currently, 

several proposed modes of decadal climate variability exist in the different ocean basins. The fundamental premise of decadal climate prediction is that, 

if initialized properly, the processes and mechanisms 

associated with those modes of decadal variability 

could provide capabilities for near-term regional 

prediction. Thus, insights into such processes and 

mechanisms would be essential to understanding 

how internal climate system variability interacts with 

the response to external forcing in decadal climate 

predictions, depending on analyses of observations 

in conjunction with climate model simulations.

Test Decadal Predictions Using Weather Forecasting and Multimodel Ensembles. The atmospheric 

components of ESMs used for decadal climate predictions are very similar to those used in operational 

NWP. Atmospheric ESM components should be 

tested by using them to perform weather forecasts, 

as discussed in Grand Challenge 3.1 on Earth system 

modeling, p. 24. The coupled atmosphere-land-ocean 

models of the ESMs also can be tested in subseasonto-season (S2S) forecasting mode. Interagency collaborations provide an opportunity for comparison 

and analysis of a large multimodel ensemble, which 

may elucidate coupled model behaviors relevant to 

decadal predictability and prediction skill.

Grand Challenge 3.8 

Understand the long-term Earth system stability 

in response to possible future Earth system outcomes and address the level of confidence and 

identify emergent constraints for the range of 

model projections.

Predicting Potential High-Impact Earth 

System Thresholds and Feedbacks

Current assessments of climate change risks do not 

adequately account for the high-risk, low-probability 

outcomes associated with threshold behavior within 

vulnerable systems. Some examples of vulnerabilities include significant mass wasting of the polar ice 

caps, appreciable carbon releases from stores within 

marine sediments, and the possible prolonged 

droughts or regime shifts in tropical hydroclimates. 

The potential for threshold behavior within these 

systems is supported by a large diversity of observational evidence interpreted from paleoclimate 

archives. The mechanisms and forcings involved 

with triggering these events are still largely unexplained. Much remains to be understood about 

potential threshold behavior within terrestrial and 

marine ecosystems. Current model development 

practices are not ideally set up to identify such vulnerabilities, much less quantify their likelihood.

As the climate changes, humans will continually adapt their behavior, both consciously and 

unconsciously, in response. For instance, electrical 

demand will increase as warmer climate increases 

the need for air conditioning. While some behavior changes affect carbon emissions in a relatively 

straightforward manner, some human-climate 

interactions are far more complex. For example, in 

many low-stabilization scenarios, substantial bioenergy requirements necessitate that large portions of 

arable land be devoted to energy production at the 

possible expense of food production and without 

consideration of the water supply needed to support 

production of other crops or energy sources. The 

impacts on the global agricultural economy and 

water resources may be significant but are poorly 

quantified. Such stresses likely will trigger socioeconomic and technical changes that could alter population trends, energy demands, and other societal 

behaviors relevant to GHG emissions. These feedbacks also could trigger instabilities in the coupled 

human-Earth system, either through the feedbacks 

themselves or by pushing the climate system past 

a critical threshold. Many decision makers require 

information about high-risk, low-probability outcomes, but current scenario development is based 

on plausible pathways with little or no estimate of 

their likelihood. Outlier scenarios typically are outside those generally used to force model simulations 

of future climate. However, improved understanding of the potential for high-impact Earth system 

threshold behavior as well as human-Earth system 

feedbacks will lead to more realistic assessments 
42 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

of the feasibility of future scenarios and risks. This 

grand challenge will better inform development of 

appropriate U.S. energy policies intended to achieve 

specific targets. Just as insurers need to correctly 

price their policies for high-risk, low-probability 

events, cost-effective climate change adaptation policies also must be developed with the consideration 

of such outcomes.

Research Needs and Knowledge Gaps

Investigate Plausible High-Risk Scenarios 

Exploring Potential Earth System Threshold 

Behavior. Much of the community’s effort is 

directed toward developing optimal model configurations based on the observational record of the 

past few decades. This focus has limitations given 

the value of information about potential extremes 

within a risk assessment. More lateral thinking is 

needed concerning methodology to explore and 

test alternate hypotheses for the physics of climate. 

As an example, an “abrupt change early-warning 

system” can be used to prioritize observation, 

modeling, and theory toward critical Earth system 

components where the risk of threshold behavior could be imminent (NRC 2013). Analysis of 

high-risk, low-probability climate scenarios could 

employ advanced statistical techniques. While more 

can be done to generate quantitative information 

about uncertainties in climate model predictions 

using state-of-the-art ESMs, the limiting factor 

in making significant advances about low-probability, high-risk scenarios is the community’s 

scientific creativity aided by mathematical and 

computational wherewithal.

Improve Human-Earth System Modeling Capabilities. To discover how human-climate feedbacks 

could trigger instabilities, more work is needed 

to fully couple IAMs with ESMs (e.g., see www.

globalchange.umd.edu/iesm/) and use these tools to 

construct large ensembles of simulation databases 

to more fully explore possible interactions. However, more research is required to understand when 

offline IAMs versus coupled human-Earth system 

models are needed and to develop more robust, 

offline IAMs for interrogating datasets to look at 

human feedbacks. Higher resolution should be a 

target for IAM improvement, as well as comparison 

with observations and evaluation of parametric 

and structural model uncertainty using emulators. 

Also needed is representation of a broader range 

of interactions between human and Earth system 

processes. There are three essential areas of model 

development: (1) human-Earth interactions at fine 

scales such as processes in urban population centers 

and coastal environments are not well represented in 

the current generation of models; (2) more holistic 

representations of carbon-water interactions are 

important to account for human and ecosystem 

response to perturbations and their feedbacks to the 

Earth system; and (3) representing potential feedbacks involving migration, population, and labor 

productivity is important to explore their significance in projecting future changes.

Improving human-Earth system modeling capabilities will provide the tools needed to characterize 

how humans respond to climate change and how 

those responses affect further climate change, influencing the Earth system trajectory and the realization and stability of future scenarios. There are 

implications of long-term outcomes for near-term 

climate, particularly related to the need to understand negative emissions. More attention needs to 

be paid to reversibility and hysteresis on a variety 

of temporal and spatial scales. Lastly, closer programmatic coordination via common risk analyses 

should target areas of DOE interest, including risks 

to energy and water demands.
November 2017 Biological and Environmental Research Advisory Committee 43

Chapter 4 — Microbial to Earth System Pathways

Determining the Pathways that 

Link Microbial Activities to the 

Earth System Scale

Microbes affect atmospheric and climate 

change, energy production, and remediation of legacy waste. Moreover, they 

link the carbon, nitrogen, and water cycles by catalyzing ecological processes and biogeochemical 

transformations that are strongly mediated by water 

availability and that affect carbon and nutrient 

cycling and transfer between the biosphere and the 

atmosphere. Overall, this high-order grand challenge 

seeks to determine the pathways that link microbial activities to the Earth system scale, identifying 

(1) the strong interactions and interactors (i.e., 

the keystone taxa) that influence these pathways, 

(2) how microbial communities change in response 

to environmental changes (i.e., as responses and 

feedbacks), and (3) how these communities might 

be managed to elicit desired outcomes of Earth 

system functioning (see Fig. 4.1. Microbial to Earth 

System Pathways, p. 45).

The long-recognized linkages between Earth system 

science and microbiology are central to developing an integrative and practical understanding of 

how energy use affects the environment. Though 

individual microbial cells have negligible impact, 

when summed their influence is massive, often dominating elemental transformations at a hierarchy of 

spatial and temporal scales. These combined impacts 

range from the blooms of microbial activity around 

resource “hot spots” to processing and filtering 

of contaminants across landscapes, to influencing 

soil structure, to spreading disease, to determining trace gas fluxes that affect the composition of 

Earth’s atmosphere and the global climate. Microbes 

catalyze major transformations in every elemental 

cycle relevant to carbon, water, and energy. Not well 

known, however, is how these activities respond to 

changing edaphic drivers and how they shape the 

direction and pace of those changes.

The biological diversity of microorganisms is far 

deeper and broader than diversity among plants and 

animals, but knowledge of how this genetic potential 

is distributed across the globe is growing rapidly. 

The hackneyed “black box” is now at least partially 

translucent. Yet, understanding is still lacking 

regarding whether and where diversity matters for 

the processes through which microorganisms affect 

the Earth as an ecological system.

The Biological and Environmental Research Advisory Committee’s (BERAC) vision for microbial to 

Grand Challenges in Microbial 

to Earth System Pathways

Overarching Grand Challenge and 20-Year Vision

Define the levels of biological organization most relevant to scaling from single cells to 

ecosystems and global cycles; capture how that organization varies in time and space; and 

identify critical interactions that dictate rates of carbon, nutrient, and energy transformation in 

different environments.

4
44 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Earth system pathways is a more complete translation between microbial ecology and Earth system 

modeling for improved prediction and management. 

To achieve this vision, BERAC has identified four 

grand challenges (see Microbial to Earth System 

Pathways Summary of Grand Challenges and Action 

Items, this page). Successfully addressing these 

challenges revolves around (1) identifying microbial 

traits that can be used to organize microbial diversity and enable prediction of how microorganisms 

function in nature as cells, populations, and communities; (2) recognizing that the functional roles 

of microorganisms rely on the complex network of 

Microbial to Earth System Pathways

Summary of Grand Challenges and Action Items

Microbial communities influence soil and plant systems that affect regional and global environments. 

However, research at one scale does not always translate to other scales because of challenges in data formats, 

relevance at different scales, and fundamental understanding of the links among scales. To help determine the 

pathways that link microbial activities to the Earth system scale, BERAC has identified four grand challenges 

that are outlined here and then described in more detail in the Grand Challenge Research Recommendations 

section, p. 46:

4.1. Characterize the biogeochemical exchanges 

driven by food web and plant-microbe 

interactions and evaluate their process-level 

impacts, sensitivity to disturbances, and 

shifting resource availability under changing 

environmental regimes.

4.2. Define the sphere of influence and key elements 

of microbial communities in space and time 

relevant for predicting larger-scale ecosystem 

phenomena for Earth system understanding.

4.3. Integrate molecular and process data to improve 

the ability to define ecologically significant 

traits of individual taxa and communities and 

use trait-based models to develop predictive 

links between community dynamics and 

ecosystem processes.

4.4. Align and deepen connections among 

conceptual understanding, measurements, 

and models related to the roles of microbes in 

determining the rate of transformation, uptake, 

and loss of chemical elements from ecosystems.

Associated with the grand challenges are four action 

items recommended for initiating progress toward 

the challenges:

• Develop and deploy sensors suitable for use in 

the field that integrate the effects of microbial 

activity at the ecosystem scale.

• Promote integrated studies that explicitly test 

predicted microbial network interactions and 

attempt to assess membership and speciesspecific and collective functional capabilities 

within ecologically coherent microbial 

communities.

• Conduct experiments that help determine 

the influence of microbial processes at larger, 

aggregate scales.

• Promote research teams to integrate microbial 

community dynamics into Earth system models.

ecological interactions that shape their biogeochemical impact; and (3) integrating microbial attributes 

quantitatively and describing the functional significance of microbial biodiversity for Earth system 

processes and their representation in Earth system 

models (ESMs).

Building on Progress Related 

to the 2010 Grand Challenges

As noted previously, the concept that microbial 

processes influence the Earth system is not new. 

Although this focus was not explicit in BERAC’s 
November 2017 Biological and Environmental Research Advisory Committee 45

Chapter 4 — Microbial to Earth System Pathways

20-year visioning effort published in the 2010 Grand 

Challenges report (BERAC 2010), the intention is 

implicit in many of the challenges that were identified. The topic has been elevated for this current 

report because connecting the understanding of 

microbiome function across scales of biological 

complexity has become increasingly important.

Substantial progress has been made for several of 

the specific goals identified in the 2010 report. 

Under “enabling predictive biology,” for example, 

the challenge was to develop technologies and 

models for associating biological activities at the 

microscale (i.e., dimensions of individual cells) with 

processes at the macroscale, having the ultimate 

goal of achieving a more predictive biology. The 

realization of complete in situ, single-cell omics 

(i.e., for metabolites, transcripts, and proteins) is 

on the near-term horizon. Recently cultured, novel 

microbes can rapidly be made genetically tractable 

and their genomes functionally annotated using 

high-throughput technology. Advanced spectrometric instruments have greatly enhanced the sensitivity and detection of metabolites and signaling 

molecules associated with the transformation of 

carbon and energy in the laboratory and environmental systems. Significant progress has been 

achieved in assembling simple laboratory microbial 

communities that recapitulate fundamental biotic 

interactions and system-level processes. Still a challenge, however, is developing more complex model 

communities that more fully capture environmentally relevant interaction networks and incorporate 

the plasticity and resiliency of those networks. 

Those types of model systems will be critical for 

(1) identifying the interdependencies most sensitive to environmental change, as well as the general 

design principles of natural systems (a challenge 

specified in the 2010 report), and (2) modeling 

and testing the relevance of those design principles 

in open environmental systems. Also still urgently 

needed, however, is the ability to measure gross 

rates (fluxes) of materials passing between organisms and their environment. Such information will 

be critical for predicting and manipulating the types 

Fig. 4.1. Microbial to Earth System Pathways. This 

diagram illustrates the range of scales—from molecular to 

global—through which microorganisms influence the Earth 

system. [Image courtesy Victor Leshyk]
46 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

and rates of ecosystem processes and response feedbacks influenced by climate change.

Some of these earlier unachieved challenges require 

rescoping and reformulation to reflect new understanding of system complexities. Clearly, functional 

metagenomics alone will not enable mass balance 

closure for biogeochemical cycles or be sufficient to 

predict organismal fitness, but it must be more fully 

integrated with modeling and complementary analytical measurements. The more immediate decadal 

challenge is to improve functional gene annotation 

methods to achieve a better approximation of phenotypes inferred from genotypes. This chapter outlines an updated set of challenges for (1) defining 

the different levels of biological organization most 

relevant to scaling from single cells to ecosystems 

and global cycles; (2) capturing how that organization varies in time and space; and (3) identifying 

interactions most critical to controlling the rates of 

carbon, nutrient, and energy transformations in different environments.

Grand Challenge Research 

Recommendations

Grand Challenge 4.1 

Characterize the biogeochemical exchanges driven 

by food web and plant-microbe interactions and 

evaluate their process-level impacts, sensitivity 

to disturbances, and shifting resource availability 

under changing environmental regimes.

Microbial Interactions Structuring 

Earth System Processes

Microorganisms live in the most complex ecological communities on Earth, and their interactions 

with plants, insects, and animals, while intimate in 

scale, influence processes at much larger scales. The 

complexity of microbial communities in taxonomic 

diversity, biochemical versatility, and evolutionary 

breadth and depth is both awe-inspiring and an 

immense grand challenge for any kind of practical 

synthesis. Understanding the nature, consistency, 

and organizing principles underlying such interactions is just beginning, yet there is reason to expect 

that such interactions imprint on ecosystem biogeochemistry in consistent ways, enabling prediction.

Full knowledge of Earth system consequences of 

interactions in microbial communities will improve 

the understanding of energy and material flow in 

ecosystems, as well as the ecological significance 

of the full range of organisms in microbial communities—bacteria, fungi, protozoa, metazoan, and 

viruses, including individual taxa or strains that may 

play keystone roles in such interactions. Identifying 

these interactions, the environmental influences 

on them, and their ecosystem-scale consequences 

should improve prediction by illuminating key pathways to Earth system processes.

Research Needs and Knowledge Gaps

Quantify and Predict Complex Ecological Networks. Within a habitat, organisms interact with 

each other via flows of energy, matter, and information to form complex ecological networks, including 

(1) trophic interactions described in food webs 

(Montoya et al. 2006; Bascompte 2007); (2) signaling networks [e.g., those involved in biofilm 

formation, quorum sensing (DeAngelis 2016), or 

the development of symbioses (Neal et al. 2012)]; 

and (3) cascades of metabolites as communities use 

a variety of substrates, as well as the resulting waste 

products, necromass, and persistent soil organic 

matter (SOM; Kallenbach et al. 2016). These interactions cause nutrient elements and carbon to cycle 

both into and out of microbial biomass, affecting 

organic matter stabilization (Kindler et al. 2009; 

Schweigert et al. 2015), plant nutrient availability, 

and conversion of microbial cell carbon to carbon 

dioxide (CO2; Clarholm 1985; Griffiths 1994; Bonkowski 2004; Fox et al. 2006). The major players in 

these interactions include plants, bacteria, archaea, 

fungi, protozoa, metazoans, and likely many phages 

and viruses not yet discovered. Quantifying and predicting such networks in terms of their composition, 

structure, dynamics, and biogeochemical impact 
November 2017 Biological and Environmental Research Advisory Committee 47

Chapter 4 — Microbial to Earth System Pathways

remain extremely challenging, yet finding generalities in such networks could enable prediction.

Model Elemental Dynamics. Predictive food web 

models have been developed (Berlow et al. 2009), 

but the complexity and cryptic nature of soil food 

webs have hampered understanding (Brose and 

Scheu 2014). With the advent of more powerful 

omics and isotopic techniques, the ability to model 

elemental dynamics in complex ecosystems is 

improving (Digel et al. 2014), such as identifying 

unique structural features of soil food webs (Riede 

et al. 2010) and exploring the effects of temperature 

and moisture on soil food chains (Binzer et al. 2012; 

Lang et al. 2012; Lang et al. 2014). Next-generation 

sequencing approaches, including ever-growing reference sequence collections of mitochondrial and 

nuclear DNA sequences for higher eukaryote taxa, 

are making accurate predictions of species composition and richness more feasible for mixed environmental samples (Taberlet et al. 2012; Leray et al. 

2013). The next challenge is to identify components 

of interaction networks that are quantitative and 

repeatable, and thus predictive of energy and nutrient 

flow through microbial ecosystems (see also Chapter 8 section, Modeling and Engineering, p. 110).

Detect and Quantify Ecosystem Consequences 

of Plant-Microbe-Soil Interactions. Interactions 

between microorganisms and plants have a major 

influence on plant productivity and biogeochemical 

fluxes from the ecosystem to the global scale. Carbon 

flow from plant roots to soil is the primary source of 

stabilized soil carbon, provides much of the energy 

that powers soil microbial food webs, drives production and consumption of trace gases, and shapes the 

nutrient cycles that influence plant growth. Global 

networks that monitor ecosystem-atmosphere 

exchange of CO2 (i.e., eddy flux networks) point to 

root exudation as a quantitatively significant flux in 

the carbon cycle, but its effects on nutrient cycling, 

balance of SOM formation and decomposition 

(i.e., “priming”), and trace gas production are not 

quantitatively represented in models because the 

controls are not well parameterized. Unlike leaf-level 

processes, which can be scaled using physical and 

radiative transfer physics, belowground processes 

are highly variable owing to the soil environment’s 

heterogeneity and the diverse strategies that plants 

have evolved for resource acquisition (Reich 2014), 

both of which make linking belowground and aboveground processes challenging at the scale of individual plants, plots, or ecosystems (Kramer-Walter et al. 

2016). Details are emerging of how communication 

among microbes, and between microbes and plants, 

affects symbiotic associations (Lareen et al. 2016; 

Rubin et al. 2017) and biofilm formation (Flemming 

et al. 2016), but translating these quantitatively to 

ecosystem processes is not yet possible. Thus, new 

methods are needed for detecting and quantifying 

the ecosystem consequences of plant-microbe-soil 

interactions in situ (i.e., to minimize biases introduced by isolating plants and microbes in artificial 

media or under controlled laboratory or greenhouse 

conditions). Also needed are new conceptual frameworks that enable modeling of these processes at 

multiple spatial and temporal scales.

Meeting this grand challenge requires a systems 

approach to microbial to Earth system pathways, 

where microbial interactions with biogeochemical 

processes will be observed in situ and mechanistically linked to ecosystem-scale consequences. Synthetic systems and enrichment cultures will be used 

to better understand what is first observed in nature. 

BERAC recommends several intermediate steps for 

advancements toward Grand Challenge 4.1:

• Support research that applies stable isotope 

probing and other labeling techniques to 

quantitatively trace elemental flows and 

reveal structuring interactions in situ. Such a 

powerful suite of tools can physically couple 

elemental flow to biodiversity by measuring 

isotopes in cells or biomolecules that contain 

taxonomic information.

• Identify sentinel molecules that indicate (1) the 

presence, nature, and degree of ecological 

interactions in complex communities and 

(2) metabolic status of organisms involved, 

integrating their significance in ecosystem 
48 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

functioning. These molecules include compounds in both living cells and microbial 

“remains” [e.g., cell walls, extracellular polymeric substances (EPS), and exoenzymes] that 

capture the salient components of microbial 

community, ecology, interactions, and function. 

Such compounds would help to integrate 

microbial effects in a targeted way relevant 

to parameterizing models at higher scales. 

Examples include indicators of metabolic 

stresses from environmental change—stresses 

such as nutrient, moisture, and temperature 

(e.g., common genetic regulatory stressresponse systems, such as sigma factors, as 

potential integrator signaling molecules).

• Improve quantitative understanding of how 

root exudates drive specific microbial activities 

(particularly those related to priming, the 

deconstruction of exudate compounds versus 

lignocellulose) and microbial responses that 

affect plant growth.

• Incorporate local patch-scale dynamics of 

interkingdom interactions (e.g., plant, fungal, 

and bacterial) to reconstruct ecological 

interaction networks and to understand the 

impact of microbial interactions on SOM 

formation, mineralization, and stabilization.

• In collaboration with national laboratories and 

equipment facilities, develop and deploy sensors 

suitable for field use (i.e., operable without 

pumps and on solar power) that integrate the 

effects of microbial activity at the ecosystem 

scale [e.g., concentrations of hydrogen, nitrous 

oxide (N2O), CO2, methane (CH4), oxygen, 

and porewater dissolved organic carbon, as well 

as the isotopic composition of these analytes].

• Develop new targeted efforts to analyze 

consortia of interacting microorganisms to 

isolate and understand the strong interactions 

detected in nature, including multispecies 

interactions that span trophic groups such as 

bacteria, archaea, protists, and viruses.

• Request research proposals to (1) identify the 

molecular signatures of microbial interactions 

and physiology that drive and respond to 

biogeochemical and environmental conditions; 

(2) quantify in situ energy budgets, element 

fluxes, and population dynamics in microbial 

ecosystems, identifying the subset of biotic 

and abiotic interactions that contribute to soil 

carbon stabilization, pollutant degradation, and 

their sensitivity to a changing environment (e.g., 

moisture, temperature, and atmospheric CO2

concentrations); and (3) develop new tools to 

determine the roles of specific organisms in situ, 

including tools that enable targeted knockouts 

of individual organisms (e.g., phage therapy) or 

specific genes [clustered regularly interspaced 

short palindromic repeats (CRISPR)] to test 

how removal of a key taxon or gene affects 

broader community dynamics.

Grand Challenge 4.2 

Define the sphere of influence and key elements of 

microbial communities in space and time relevant 

for predicting larger-scale ecosystem phenomena 

for Earth system understanding.

Microbial Interactions and 

Scales in Space and Time

The collective activities of interacting microorganisms existing within communities have a significant 

impact on physicochemical processes in the environment, spanning from biogeochemical transformations 

of major and minor elements to physical alterations 

through processes such as biofilm formation, mineral precipitation, and weathering. Yet the ability to 

recognize the boundaries of a microbial community 

in space and time is limited. Common descriptions 

of microbial communities are derived primarily from 

an artificial construct of microorganisms recovered 

in a sample rather than the actual ecologically relevant interacting network of microorganisms within 

a community (see reviews by Konopka 2009 and 

Vos et al. 2013). Given the small spatial scales and 
November 2017 Biological and Environmental Research Advisory Committee 49

Chapter 4 — Microbial to Earth System Pathways

interaction spheres between co-occurring microorganisms, most environmental samples collected for 

molecular microbial diversity analysis (e.g., sediment, 

soil, groundwater, or rhizosphere) likely harbor multiple microbial communities and microenvironments. 

New strategies are essential for enabling more robust 

and meaningful identification of the true membership, spatial distribution, cellular activities, and interactions within a cohesive microbial community that, 

in turn, influence an ecosystem’s large-scale geochemical and physical processes.

Research Needs and Knowledge Gaps

Develop Reliable Metrics for Investigating Spatiotemporal Interactions. Reliable metrics are 

urgently needed to visualize and define the spatiotemporal boundaries, membership, and interactions 

among microbial communities (e.g., in soil). Development of these metrics will enable investigation of 

several key questions: (1) Are there interdependencies or synergisms among microbial assemblages on 

discrete soil or sediment particles, or does each particle constitute its own ecological community? (2) If 

interactions and emergent metabolic processes 

occur at the multiparticle scale, what is the spatial 

and temporal extent of these interactions? (3) Can 

the boundaries of ecologically coherent communities be experimentally tested and modeled?

Assess the Spatial Variation of Microbial Communities. To date, only a handful of well-replicated field 

investigations have attempted to assess the spatial 

variation of microbial communities (i.e., diversity) 

by sampling along well-defined transects spanning millimeters to meters in scale (Martiny et al. 

2011). Other studies have documented microscale 

heterogeneity in metabolic activity on particles 

[e.g., organic carbon respiration without the corresponding information about the associated microbial 

diversity (Gonod et al. 2003)]. To understand and 

define what constitutes a coherent microbial community, information regarding microscale diversity 

must be connected with data characterizing the variation in microbial spatial distribution, species-specific 

activities, and emergent community-wide influence 

on relevant biogeochemical processes.

Geostatistical approaches at the phylotype or gene 

level show some promise for assessing spatial relationships (e.g., Nunan et al. 2002; Grundmann and 

Debouzie 2000). Also promising are developments 

using network modeling of 16S ribosomal RNA 

(rRNA) amplicon operational taxonomic units (e.g., 

Barberán et al. 2012; Furhman and Steele 2008) and 

metagenomic datasets (Lima-Mendez et al. 2015) 

that offer large-scale assessments of central microbial 

interaction nodes based on co-occurrence analysis. 

With the exception of Lima-Mendez et al. (2015), 

the majority of network studies thus far have not 

been extended beyond in silico predictions. Statistical 

associations often are inferred from coarse-grained 

samples, which are collected at the plot or ecosystem 

scale and are not representative of interactions occurring at the scales of local communities or populations. 

The assembly of discrete microscale communities 

likely dictates the extent of interacting microbial 

assemblages within a microbial ecosystem and, in 

turn, how interactions among microbial assemblages 

shift in response to changing environmental conditions. Studying microbial communities in high replication at the local patch scale is necessary to identify 

ecological interaction networks and to model the 

functional impact of microbial interactions. To begin 

to define the boundaries and structure of ecologically 

coherent communities, the next phase of research 

should evaluate cross-species metabolite exchange 

potentials based on genome-informed prediction of 

nutrient dependencies and community metabolic 

network models. Experiments should be developed 

to generate and test network-based hypotheses, 

employing molecular and analytical visualization 

methods, stable isotope probing, microfluidic 

devices, and other complementary methodologies 

to visualize, analyze, and track interactions among 

species and assemblages in a spatially explicit manner, including computational statistical analyses that 

incorporate spatial and temporal dynamics.

Characterize Microbial Community Boundaries 

and Spatiotemporal Dynamics. Models of major 
50 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

microbial community activities and interactions 

often produce unreliable predictions of microbial 

community stability, resiliency, and emergent properties, especially under fluctuating conditions or 

in highly heterogeneous environments. Current 

modeling efforts are confounded by even simple 

attributes (i.e., defining the coherent network of 

interacting microorganisms that compose an ecologically relevant community in nature, or defining the 

taxa that are metabolically active versus those that 

are dormant or represented only by “relic DNA”). 

Developing new approaches to characterize the 

boundaries and spatiotemporal dynamics of microbial communities will assist with optimizing the 

forecasting models of community-level activity and 

response to environmental change.

BERAC recommends several intermediate steps for 

advancements toward Grand Challenge 4.2:

• Advance and incorporate methods and 

approaches that facilitate the study of microscale 

processes, enabling multiscale investigations 

that transition from the bulk analysis (i.e., 

grams to milligrams) of soil to the fine-scale 

(i.e., millimeters to microns) particle mapping 

of cellular activity, behavior, and interactions. 

Independent methodologies are needed to 

validate current approaches based on 16S rRNA 

sequence or gene-based network predictions of 

microbial interactions. Incorporation of singlecell omics techniques; advanced light, electron, 

X-ray, and ion microscopy; and further tracer 

development of microbial activity in situ (e.g., 

stable isotope probing or fluorescently labeled 

substrates) all represent promising directions 

that will continue to advance the study of 

microorganisms within the context of their 

community in complex natural environments.

• Develop new computational statistical tools 

to incorporate spatial and temporal dynamics 

and enable interpretation of data-driven 

microbiome experiments.

• Encourage integrated studies that explicitly test 

predicted microbial network interactions and 

attempt to assess membership, species-specific, 

and collective functional capabilities within 

ecologically coherent communities.

• Develop a new U.S. Department of Energy 

(DOE) laboratory service (in collaboration 

with the National Institute of Standards and 

Technology), focusing on the development of 

fluorescent probes and tracers for biomolecule 

detection and synthesis of isotopically labeled 

substrates. The availability of well-characterized, 

isotopically labeled substrates and new 

fluorescence-based tracers will relieve some of the 

current limitations of microscopy-based assays 

for direct quantification of microbial activities, 

food web interactions, syntrophic associations, 

and the net flux of carbon and energy within 

coherent microbial communities.

Grand Challenge 4.3 

Integrate molecular and process data to improve 

the ability to define ecologically significant traits 

of individual taxa and communities and use traitbased models to develop predictive links between 

community dynamics and ecosystem processes.

Trait-Based Approaches to Scaling Up

Considering the dynamic nature of microbial 

interactions may help identify when and where 

molecular-scale information is important and where 

minimal reductionist information is sufficient. A 

trait-based approach provides a tractable and reproducible framework for identifying the physiological 

characteristics that determine the contribution of 

microbial communities to a biogeochemical process. 

This framework, in turn, will enable connection of 

microbial mechanisms at multiple scales to quantify 

the integrative effects that govern net effects at the 

ecosystem scale. As modelers and empiricists collaborate on community phenotypes, empirical studies 

will generate more appropriate data, and models 

will improve aggregation of hyperdiverse microbial communities into tractable functional units. 

As more collaborative data are collected to define 

the relationship between microbial composition 
November 2017 Biological and Environmental Research Advisory Committee 51

Chapter 4 — Microbial to Earth System Pathways

Defining Traits

Traits broadly encompass the physiological, 

morphological, and behavioral characteristics of 

an organism, species group, or functional guild. 

A microorganism’s simplest traits are encoded 

by just one genetic locus; thus, an organism’s 

genotype matches the potential phenotype. 

Traits also relate to definitions of a population 

(i.e., genetically cohesive) and strain (i.e., genetic 

sequence variation).

and ecosystem functioning, incorporating microbial interactions into process models will become 

increasingly valuable.

Research Needs and Knowledge Gaps

Identify Ecologically Significant Traits of Microorganisms and Microbial Consortia. Microbial 

metabolism cycles elements within and through 

ecosystems. Representing this influence in predictive models makes sense, and doing so has reduced 

uncertainty (e.g., Wieder et al. 2013). Efforts are 

still rudimentary, however, and do not account for 

microbiome variation among systems or in systemspecific responses to changing environmental 

conditions. Incorporating the spectacular diversity 

of traits present in the microbiomes of Earth’s ecosystems remains challenging. An essential challenge 

in scaling from microbial (or genomic) diversity to 

ecosystem function stems from the need to define 

the ecologically significant traits of microorganisms 

and microbial consortia, quantify their variation 

in situ, and test whether useful simplifications 

could reasonably represent the vast diversity while 

remaining quantitatively tractable. In most cases, 

diverse soil microorganisms function as networks 

of interacting organisms, or consortia, rather than as 

physiologically autonomous cells. Identifying these 

functional modules, or community phenotypes, that 

can be mapped to system-level processes may offer 

a promising approach for enabling prediction of the 

emergent outcomes of community metabolism.

Isolated microbial representatives do not represent the full spectrum of traits found in nature (see 

sidebar, Defining Traits, this page). Conversely, 

within some microbial groups such as organisms 

that conduct oxygenic photosynthesis or methanogenesis, traits may exhibit coherence among natural 

taxonomic groups defined by phylogenetic relationships; they also can map broadly to Earth’s different 

ecosystems (Martiny et al. 2013). Examples include 

(1) the exclusive provenance of certain marine 

bacteria, such as the cyanobacterium Prochlorococcus and the heterotroph Pelagibacter, to the world’s 

oceans and (2) the preference of specific groups 

of nitrifying microorganisms for either marine or 

terrestrial systems. Those patterns can be associated 

with well-defined traits of biogeochemical importance. Photoautotrophy by cyanobacteria, the specialization to mineralization of organic carbon in 

low-nutrient environments by Pelagibacter, and the 

oxidation of ammonia by both archaea and bacteria 

are biological processes of fundamental importance 

to planetary carbon and nitrogen cycles (see Fig. 4.1, 

p. 45). However, variations in physiological traits 

influencing the biogeography of major functional 

guilds—such as adaptive differences among guild 

members to changing pH, temperature, light, or 

nutrient concentration—are not captured in these 

very broad correlations between phylogenetic affiliation and function. For example, different genetically 

distinct species groups of Prochlorococcus vary in 

basic adaptive traits, including variable resistance to 

virus attack and optimum conditions of light, temperature, and nitrogen required for growth (Martiny 

et al. 2009). In turn, these trait differences have 

been shown to govern oceanic distribution patterns, 

suggesting that understanding this precision of trait 

assignment is fundamental to predicting ecosystem 

response to environmental change.

Link Patterns of Microbial Diversity with Ecosystem Function. Advancing understanding of 

traits in an ecological context requires conceptual 

and mathematical models that move beyond genetic 

potential to characterizing the realized phenotypes 

(i.e., traits) of microbes and, where possible, the integrated traits of the communities in which they occur 
52 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

(Allison 2012). For example, DOE’s Office of Biological and Environmental Research (BER) supports 

Next-Generation Ecosystem Experiments (NGEE) 

in the tropics and the Arctic (see also Chapter 7 on 

User Facilities and Research Infrastructure, p. 85). 

These NGEE projects seek to develop process-rich 

ecosystem models, employing a trait-based modeling 

approach. Defining key traits is an essential foundation for understanding how traits interact within an 

organism in response to other organisms (e.g., competition and mutualism) and the environment (e.g., 

niche partitioning and substrate access) and is a critical objective for identifying and predicting realized 

phenotypes under field conditions. A trait-based 

approach could provide an essential framework for 

linking patterns of diversity with ecosystem function 

and predicting the response of those systems to environmental change.

Identify Dominant Microbial Interactions that 

Influence Biogeochemical Cycling. Genomic 

studies suggest that metabolic complementation 

regulates biogeochemical pathways in natural 

microbial communities (Zelezniak et al. 2015). 

Interactions through metabolic complementarities 

are common in nature and have potentially large 

impacts on the function of microbial consortia and, 

in turn, ecosystem microbiomes. The interactions 

of microbial consortia also can affect the spatial segregation of consortia among niches, as well as the 

community’s stability, by impacting the potential to 

buffer environmental change. Therefore, spatially 

explicit studies that identify the metabolic response 

of discrete consortia to the local nutritional and 

physicochemical environment present a tractable 

approach for identifying the dominant microbial 

interactions influencing biogeochemical cycling in a 

diverse range of ecosystems.

Incorporate Microbiome Variation into Earth 

System Models. Incorporating microbial processes 

into ESMs is known to improve uncertainty surrounding carbon and nutrient cycling (Wieder et 

al. 2013), but ESMs have incorporated few of the 

microbial parameters needed to account for microbiome variation among systems or system response to 

changing environmental conditions (see also Chapter 3 on Earth and Environmental Systems Sciences, 

p. 21). Doing so remains a significant challenge 

because the potential for generating new knowledge 

from metaomic data still is unfolding. Annotation 

also remains a key challenge, and sequencing data—

even with more robust annotation—represent only 

microbial potential, not realized phonotypes.

BERAC recommends several intermediate steps for 

advancements toward Grand Challenge 4.3:

• Solicit proposals that (1) develop models 

to identify microbial interaction scenarios 

and thus narrow the set of molecules for 

targeting in empirical studies; (2) identify 

chemical signatures (i.e., genes, transcriptomes, 

metabolites, and isotopic tracers) that link 

biotic responses to environmental changes with 

ecosystem feedbacks critical to the carbon and 

water cycles; (3) characterize the dynamics of 

microbial interactions and how their plasticity 

drives organism and ecosystem resiliency in 

responses to seasonal changes and extreme 

events; and (4) identify markers for community 

traits that may scale from cells to systems. To 

enable their incorporation into ESMs, traits 

must be considered in new ways—using cultureindependent strategies that include isotopes, 

remote sensing, and changing molecular profiles 

(e.g., genes and transcripts) across seasons, 

extreme events, and gradients to identify markers 

for environmentally significant biological 

properties. Trait dynamics must be understood, 

especially for biogeochemistry, capturing how 

hot spots and “hot moments” drive net effects 

on ecosystem processes. Identifying traits, both 

stable and dynamic, will define how plasticity 

drives organism, community, and ecosystem 

responses to changes.

• Facilitate cross-disciplinary teams that examine 

ecosystems across organisms (i.e., plants and 

microbes), identify important traits across 

scales (i.e., watershed, ecosystem, community, 

organism, and molecules), and bring together 
November 2017 Biological and Environmental Research Advisory Committee 53

Chapter 4 — Microbial to Earth System Pathways

scientists, engineers, and modelers. Significant 

gaps among scientific domains limit the ability 

for molecular scientists to generate data useful 

for microbially explicit models. Similarly, 

modelers are challenged with identifying the 

important properties and processes governing 

ecosystem- to watershed-scale biogeochemical 

processes, which necessitates new funding 

opportunities encouraging cross-domain investigations to enable studies that develop new 

and complementary empirical, analytical, and 

modeling approaches to foster understanding 

across scales. Enabling cross training and literacy 

among domains will enable large advances in 

integrating high-resolution, spatiotemporal data 

to inform ESMs.

Grand Challenge 4.4 

Align and deepen connections among conceptual 

understanding, measurements, and models related 

to the roles of microbes in determining the rate of 

transformation, uptake, and loss of chemical elements from ecosystems.

Microbial Processes in Earth System Models

The persistence and stability of organic carbon in soil 

was long thought to be dominated by its chemical 

“quality”—its elemental composition and the nature 

of chemical bonds affecting its use as a microbial substrate—modulated by factors like temperature and 

moisture. The environmental and edaphic context is 

indeed important, but a new view has emerged that 

emphasizes soil minerals and microbial ecology as 

important controls of the formation, stabilization, 

and decomposition of organic matter in soils (see 

sidebar, Soil Organic Matter, p. 54). This new paradigm has not been implemented in soil biogeochemical models that predict trace gas fluxes of CO2, CH4, 

and N2O or the persistence of contaminants in the 

environment. Developing this capacity is imperative, 

and new initiatives are needed to develop a mechanistic understanding of (1) how the physicochemical 

protection of SOM determines its long-term fate, 

compared to its molecular composition (e.g., chemical recalcitrance); (2) how microbial communities, 

especially those found in the rhizosphere, play a 

vital role in the formation, stabilization, and decomposition of SOM, including the role of microbial 

necromass and extracellular products; and (3) how 

interactions between microbes and minerals at the 

soil pore scale govern carbon and nutrient retention 

and loss in ecosystems, trace gas exchange between 

ecosystems and the atmosphere, and persistence and 

fate of pollutants in watersheds. Concurrently, special attention must be given to developing numerical 

tools that tractably transfer these insights to societally relevant scales.

Research Needs and Knowledge Gaps

Develop New Measurement Networks for Collecting Data on Multiple Spatial and Temporal 

Scales. New data collection and models are needed 

to address emerging soil paradigms, emphasizing 

linkages between soil minerals and microbes 

responsible for SOM stabilization, trace gas production, nutrient availability, and contaminant processing. Integrating these data and formalizing fresh 

insights into new model structures are critical, and 

they present simultaneous challenges in identifying 

the key data and knowledge gaps that can inform 

and parameterize models yet to be built. Such challenges can be met with new measurement networks 

that collect various data on multiple spatial and 

temporal scales. Advancing this research area also 

requires the development of tools that measure 

process rates in ways that can be paired with the 

massive volume of molecular data coming online. 

Special attention is needed for integrating these 

measurements to inform, parameterize, and validate 

models of terrestrial biogeochemistry at ecosystem 

to Earth system scales.

Determine How Fine-Scale Analyses Map 

onto Larger-Scale Processes. Advances in 

high-throughput nucleic acid sequencing have led 

to new understanding of the extent of biodiversity, 

its functional potential, and clues about its actual 

functioning in the environment. Meanwhile, omics 
54 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Soil Organic Matter

Soil organic matter (SOM) 

accumulates in terrestrial 

ecosystems through the 

combined activities of 

photosynthesis by plants, 

as well as biosynthesis and 

decomposition by complex 

mixtures of soil microorganisms 

(i.e., bacteria, archaea, and fungi). 

As SOM becomes stabilized 

through several mechanisms, 

some of the matter remains in the 

soil for up to thousands of years, 

whereas other SOM cycles back 

to atmospheric carbon dioxide 

in a matter of days. SOM’s role 

in storing carbon and thereby 

slowing climate change promotes 

fertility and crop growth in 

agriculture, retaining nutrients 

and contaminants and thus 

slowing their release to aquatic 

ecosystems. These processes 

contributing to SOM stabilization 

are key to understanding 

microbial to Earth system 

pathways. Contributing factors 

include microbial physiology (e.g., 

carbon use efficiency, growth 

rates, and growth efficiency), 

selective preservation, and 

mineral stabilization (see figure, 

Microbial Controls on Soil Organic 

Matter Stabilization, at right). 

Plants also play a major role by providing rootderived (i.e., labile) inputs to soil, which can promote 

stabilization and destabilization (e.g., the priming 

effect) in ways not fully understood.

Microbial Controls on Soil Organic Matter Stabilization. Three conceptual representations depict different ways models could represent 

microbes and fauna. Key: CUE, carbon use efficiency; MGE, microbial 

growth efficiency; SOM, soil organic matter. [Reprinted from Grandy, A. S., 

et al. 2016. “Beyond Microbes: Are Fauna the Next Frontier in Soil Biogeochemical Models?” Soil Biology and Biochemistry 102, 40–44, with permission from Elsevier. © 2016]

techniques are cheaper than ever, leading to a deluge 

of data from microbial systems. Yet, there still are 

no clear connections between these rich data and 

ESMs for several reasons: (1) relationships between 

complex microbial community structure and its 

ecological function have been difficult to establish, 

even under laboratory conditions, because of inadequate genome annotation; (2) techniques for 

quantifying gross process rates in situ have not kept 

pace with techniques for describing microbial communities or their putative functions; and (3) representation of soil pore- and plot-scale dynamics at 
November 2017 Biological and Environmental Research Advisory Committee 55

Chapter 4 — Microbial to Earth System Pathways

larger spatial and temporal scales remains elusive. 

Addressing these three knowledge gaps will require 

consideration of how fine-scale analyses map onto 

larger-scale processes in ways that better inform predictive models.

Historically, ecosystem theories and biogeochemical 

models were based on the assumption that environmental conditions change the rate of ecosystem 

processes, but that biotic responses are consistent 

across sites (Bradford and Fierer 2012). Emerging 

evidence, however, suggests that the activities of 

microbial communities in soils, aquifers, and sediments are shaped by particular local environmental 

conditions (Fierer et al. 2012; Evans and Wallenstein 2014; Talbot et al. 2015). Accordingly, physiological tradeoffs between acquiring resources versus 

withstanding environmental stress potentially dictate the activities of belowground communities and 

their functional response to environmental change.

Better understanding is needed of the extent to 

which local microbial processes and mechanisms 

affect projections at Earth system scales. The framework for quantifying these influences is not yet 

developed. Across scales, multiple processes and 

drivers regulate ecosystem processes (Bernhardt 

et al. 2017), but additional insight is needed to 

understand how these factors integrate to determine emergent Earth system behavior. In particular, theoretical frameworks need investigation and 

refinement to illuminate broad-scale properties and 

processes that cause divergent trajectories across 

environmental gradients and in response to environmental perturbations.

Understand How Factors Controlling Biogeochemical Processes Aggregate Across Scales.

Questions related to this fundamental challenge 

include: Can the mean abundance of a functional 

gene (or transcript) lead to increased predictive skill 

of models that operate at scales of square kilometers 

or larger? Similarly, how may this information transfer to deeper soil horizons or change through time? 

If new scientific insights are intended to improve 

predictive models, they also must inform the 

broad-scale processes and properties that delineate 

divergent trajectories or threshold behaviors that 

may not be predictable from current approaches. 

Predictive modeling necessarily projects ecosystem 

behavior beyond the domain over which the model 

has been informed. As in the physical climate system (Knutti and Sedláček 2013), greater fidelity in 

process representations should increase confidence 

in modeling terrestrial and aquatic biogeochemical 

systems (Bradford et al. 2016). Toward this end, 

knowledge must be developed across environmental 

gradients, edaphic conditions, and land use practices. 

Given the challenges and costs of exhaustively sampling diverse subsurface environments, modeling 

also serves as an important tool to generate hypotheses, refine theory, and highlight key uncertainties in 

belowground process understanding.

Recent work suggests that trait-environment relationships can be context dependent. For example, 

the functional form of microbial responses and 

sensitivities to soil moisture may be constrained by 

historical environmental conditions and community composition (Hawkes and Keitt 2015; Martiny 

et al. 2017), suggesting that strategic sampling 

across biotic and abiotic gradients may be required 

to define the key ecological boundaries and environmental thresholds. A combination of targeted 

experiments coupled with multiscale model development can fill key data and knowledge gaps related 

to prediction of microbial community processes and 

ecosystem function.

BERAC recommends several new research funding 

opportunities to fill knowledge gaps needed to link 

microbial physiology with mineral stabilization and 

destabilization of SOM and the production and 

consumption of greenhouse gases. Potential projects that could lead to advancements toward Grand 

Challenge 4.4 are:

• Identify patterns in microbial functional 

traits among ecosystems, across edaphic 

gradients, and in response to perturbations. 

The physiological traits of microbial communities hold some promise for ways to 

connect microbial ecology to ESMs (see 

Grand Challenge 4.3, p. 50). Boiling down 
56 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

data-rich omics information through 

a functional trait lens will facilitate the 

identification of key community structure–

ecosystem function relationships—namely, 

informing microbial kinetics, growth rates, 

growth efficiency, and biochemistry. Similarly, 

microbial traits may influence microbe-mineral 

interactions related to the physicochemical 

stabilization and destabilization of SOM (e.g., 

through cell chemistry, necromass production, 

and EPS). These opportunities should enable 

integration of information at the microbial scale 

in dimensions tractable for modeling.

• Determine when microbial processes matter 

and change aggregate behavior. To meaningfully 

fill knowledge gaps, measurements of biogeochemical process rates (e.g., trace gas fluxes, 

nutrient cycling, redox conditions, and contaminant fluxes) must be correlated with omics 

datasets to identify appropriate trait metrics. 

These insights are critical for understanding 

how shifts in microbial community composition 

may drive changes in biogeochemical cycling 

and, ultimately, increase predictive capacity. 

Linking process rates with microbial community 

information and models is critical for largerscale projections through the assimilation of 

increasingly autonomous (and diverse) data 

into models.

• Develop tools to scale microbe-mineral 

insights from the molecular to ecosystem and 

global scales. The fine spatial and temporal 

heterogeneity of the subsurface environment 

presents enormous challenges in representing 

microbe- and mineral-scale insights into 

ecosystem and Earth system models. Models 

typically average and aggregate over nonlinear 

processes, but this approach may not be effective 

at representing the emerging understanding in 

larger-scale (ecosystem to Earth system) models. 

Accordingly, new opportunities should focus 

on identifying the best scale to explicitly model 

processes and extract emergent properties that 

can be parameterized at larger-scale models (i.e., 

as relates to Grand Challenge 4.2, p. 48).

Additionally, BERAC recommends support for 

opportunities and workshops that increase training, 

collaborations, and communication across disciplinary boundaries:

• Organize small, recurrent (annual) workshops 

to bring together microbial ecologists and 

multiscale modelers. These workshops would 

implement an integrated modeling-experiment 

framework called MODEX, with postdoctoral 

support (housed at a national laboratory) to 

coordinate group activities.

• Hold workshops at DOE’s Joint Genome 

Institute and Environmental Molecular 

Sciences Laboratory. Participants would design 

experiments exploring microbial community 

structure-function relationships, specifically 

focusing on rhizosphere priming and plantmicrobe interactions, interactions between 

microbial biomass and mineralogy, nitrification 

and denitrification, and methanogenesis and 

methanotrophy.

• Issue new proposal calls to incorporate microbial 

processes into ESMs. One new program would 

support the development and deployment of 

observational networks to challenge models. 

Large-scale and long-term experiments have been 

a hallmark of BER research. A second program 

should focus on building new knowledge and 

models that bridge BER’s Biological Systems 

Science Division with its Climate and Environmental Sciences Division, capitalizing on synergistic crossover and new frontiers in scaling from 

molecules to the Earth system.
November 2017 Biological and Environmental Research Advisory Committee 57

Chapter 5 — Energy Sustainability 

Grand Challenges in 

Energy Sustainability

Overarching Grand Challenge and 20-Year Vision

Provide a scientific foundation for implementing resilient energy strategies that promote 

prosperity, security, and human and environmental health by quantifying fundamental 

interactions between energy and natural system processes over a range of relevant geographic 

and temporal scales.

5

Envisioning 20-Year, Resilient 

Energy Strategies

The need for sustainable energy systems has 

never been greater. Providing the energy 

necessary for economic growth and prosperity in the face of a changing planet requires 

approaches to energy production and delivery that 

afford both economic and environmental security. 

This sustainability challenge is formidable, particularly because the energy system is and will remain 

completely entangled with the use of air, water, and 

land resources. On the leading edge of Earth system 

research, the U.S. Department of Energy’s (DOE) 

Office of Biological and Environmental Research 

(BER) is uniquely positioned to advance and integrate the basic science needed to revolutionize a 

transition to resilient energy strategies (i.e., strategies 

developed from knowledge of the multiple feedbacks that occur between energy and natural systems 

as well as system responses to various stressors).

For example, water is essential for growing bioenergy feedstocks, producing hydropower, and cooling thermoelectric and nuclear power generation 

facilities, but its availability is in part a function 

of hydroclimate variability and how watersheds 

respond to these variabilities and are managed. 

Likewise, the growth potential for wind and solar 

energy sources is dependent on how the climate 

system operates on regional scales, land availability, 

and the technological evolution of the grid itself. 

Underlying all these factors is the need for sophisticated understanding of the critical infrastructure 

used for water, energy, transportation, and communication, as well as infrastructure sensitivity to a 

variety of environmental stresses.

Developing strategies to improve energy system 

sustainability, therefore, depends on a much more 

complete understanding of how stressors to land, air, 

and water resources influence energy strategies and 

the roles that humans play in these interactions. A 

systems approach is required to discover fundamental interdependencies, including trigger points and 

thresholds, and to identify the inevitable tradeoffs 

that will need to be managed.

Approaches to energy sustainability also must 

be scale aware, considering interdependencies 

occurring within the energy strategy’s geographic 

footprint, which could range from local managed 

ecosystems to regional landscapes to continents (see 

Fig. 5.1. Multiscalar Energy-Land-Water Interactions, p. 58). Moreover, the details of how national 

goals intersect with regional and local realities are 

complicated. Wind power, for example, is growing 

rapidly within the national grid, but the location of 
58 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

adequate resources is a function of regional climate. 

Bioenergy’s potential expansion depends not only 

on climatic conditions that support bioenergy crops, 

but also on the simultaneous availability of healthy 

soil and water resources at the farm scale, economics 

of energy demand and supply, and availability of 

appropriate infrastructure to support this very different energy source. The feedbacks resulting from 

changes in the current energy portfolio to a much 

wider dependence on renewables and bioenergy 

also have the potential to interact with local and 

regional weather and climate patterns through both 

biogeochemical and physical processes.

Data-driven approaches, new observations, and 

modeling innovations will be needed to seek 

energy pathways that provide optimal economic 

and environmental outcomes and promote human 

well-being.

Building on Progress Related 

to the 2010 Grand Challenges

The 20-year vision of the Biological and Environmental Research Advisory Committee (BERAC) is 

to enable strategies that guide the implementation 

of a range of resilient energy systems promoting 

prosperity, security, and human and environmental 

health. Such a vision requires important investments 

in the fundamental sciences central to BER’s mission.

BER is uniquely positioned to foster this vision. 

Assets that support fundamental research regarding 

basic biology, watershed, climate, and integrated 

assessment provide a foundation for (1) understanding how very different systems couple to one 

other and (2) identifying which feedbacks are quantitatively important. BER has a history of addressing 

complex systems coupling in Earth system sciences 

Fig. 5.1. Multiscalar Energy-Land-Water Interactions. Energy sustainability is multiscalar, involving energy-land-water 

interactions at scales ranging from cellular to global. Interactions within and among scales largely will define the resiliency of 

different energy sustainability strategies; thus, questions must be scale aware and answers appropriately scalable.
November 2017 Biological and Environmental Research Advisory Committee 59

Chapter 5 — Energy Sustainability 

underpinned by fundamental BER system science 

and models. Some of the individual and interactive 

impacts could be large enough to significantly feed 

back to the evolution of the Earth system as a whole 

in ways not yet included in ESMs.

Demonstrated Targeted Progress Toward Characterizing the Spatial and Temporal Variabilities 

of Specific Watersheds and Ecosystems. The 

Arctic is one of these important ecosystems, where 

there is now better understanding of ecosystem 

sensitivity to warming and permafrost thaw, particularly, the responses of microbes, vegetation, and 

greenhouse gases (GHGs) to climate change. New 

insights also are emerging about the responses of 

critical western U.S. river watersheds, such as the 

Colorado and Columbia rivers, to a range of stressors as well as their associated impact on downgradient water and nutrient delivery with relevance to 

energy strategies. Very early insights are starting to 

emerge about other key systems that are especially 

sensitive to potential state changes, such as tropical 

forests and human-dominated systems. Next-generation observation platforms—some focused on 

coincident and autonomous monitoring of aboveand belowground interactions (e.g., Dafflon et al. 

2017) and integration of multiscale observations 

through remote-sensing datasets (e.g., Wainwright 

et al. 2016)—are being tested at key sites (see also 

Chapter 7 on User Facilities and Research Infrastructure, p. 85). In most cases, researchers are using 

the observations and process insights to challenge 

developing multiscale models.

Developed Unifying Models and Frameworks 

Capable of Testing and Evaluating the Significance of Potential Global Change Drivers, 

Including Climate, Energy, Land Use, and Water.

Programs in high-performance modeling and scaling 

are unmatched and have contributed substantially 

to the successful development of integrated assessment models (IAMs), ESMs [e.g., Energy Exascale 

Earth System Model (E3SM)], and integrated 

ESMs (iESMs). Other significant progress has been 

made in developing and applying lifecycle analysis (LCA) and full-cost accounting for ecosystem 

[e.g., Earth system models (ESMs)], watershed science (e.g., hydrobiogeochemistry), and bioenergy 

(i.e., from genes to landscapes). This experience is 

part of what will be needed to implement an encompassing vision for energy sustainability. BER additionally brings scientific rigor to functional studies 

of ecological systems, biogeochemical cycling, 

and the water cycle, as well as the physical climate 

system—from model development to uncertainty 

quantification, to new strategies for computational 

sciences and data analysis.

Considerable progress has been made over the past 

6 years to meet the energy sustainability challenges 

identified in the 2010 Grand Challenges report 

(BERAC 2010), with many noteworthy examples of 

success that fall into the report’s three broad energy 

sustainability challenge areas. Descriptions of several successes follow.

Advanced a Fundamental Understanding of the 

Impacts and Tradeoffs of Alternative Bioenergy 

Feedstocks and Land Uses on Energy, Climate, 

and Ecosystem Functioning. BER has positioned 

itself to meet most of the challenges identified in 

the 2010 Grand Challenges report through its Bioenergy Research Centers and Genomic Science 

sustainability programs. Although most of BER’s 

early focus was on energy conversion (i.e., biomass 

to biofuels), currently receiving more attention are 

environmental considerations (i.e., climate, biodiversity, and ecosystem functioning including healthy 

soils), and progress is steady. Breakthroughs are 

needed to make bioenergy and related bioproducts 

price competitive and to fully reap environmental 

benefits through an integrated understanding of the 

entire field to fuel enterprise.

Improved Understanding of Global Change 

Effects on Energy and Environmental Systems.

BER also is making substantial progress in helping 

to identify and quantify the impact of energy-foodenvironment tradeoffs via an understanding of the 

interactions among individual global change impact 

sectors, especially those related to energy and water. 

A recent BER water-energy testbed solicitation is a 

positive step in the direction of energy sustainability, 
60 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

services for bioenergy technologies, especially in 

the biogeochemistry and water arenas. Progress has 

been slower, however, for incorporating biodiversity 

responses and social and policy elements into sustainability research (see also Chapter 3 on Earth and 

Environmental Systems Sciences, p. 21).

Notably, a concerted push has been made toward 

more integration of IAMs with ecosystem models, 

especially through the energy-land-water nexus, 

where tight linkages can constrain energy efforts. 

This integration has led to better representation 

of climate change impacts within IAMs and has 

highlighted the value of portfolio approaches that 

effectively link IAMs, ecosystem models, and LCAs. 

Such linkages likely are to be crucially important 

for evaluating the complex multiscale effects of 

Energy Sustainability

Summary of Grand Challenges and Action Items

Energy production and use are inherently connected to land, air, and water resources. Comprehensively 

understanding these interactions is therefore important for guiding current and future energy production that 

will optimize energy availability and environmental quality. In recent years, research on bioenergy conversion 

and associated environmental considerations has progressed substantially, accompanied by an increased 

understanding of energy-food-environment tradeoffs and improved characterization of spatial and temporal 

variabilities of targeted ecosystems. Other significant advances include further development of integrated 

assessment models, climate models, integrated Earth system models, and the coupling of these models where 

appropriate to fully address sustainability science questions. Moving forward, four grand challenges will take 

this research into the next decade and help resolve important questions. These challenges are outlined here and 

then described in more detail in the Grand Challenge Research Recommendations section, p. 61.

5.1. Further develop the science of coupling energy, 

water, and land use across different spatial and 

temporal scales to understand environmental 

impacts and changing climate and to better 

predict net biogeochemical fluxes.

5.2. Use observational, experimental, and modelbased approaches to explore the sustainability 

of alternative energy systems, incorporating 

stability and resilience analysis, uncertainty, 

transition paths from current infrastructures, and 

the use of appropriate common metrics.

5.3. Understand how variability and change in natural 

systems affect energy system structure and 

function and determine how best to build this 

knowledge into models.

5.4. Create new data streams and more effectively 

use existing observations to ensure the availability of scale-appropriate data, particularly 

related to high-resolution land use, landscape 

infrastructure, demographic change, and energyland-water research.

Associated with these grand challenges are two 

action items (see Discussion of Action Items section, 

p. 67, for more details) that represent recommended 

first steps toward addressing the challenges:

• Establish a strategically distributed network of 

energy sustainability testbeds for addressing 

crucial research questions associated with specific 

energy strategies and air-water-land feedbacks 

at multiple scales. The combined testbeds will 

address several grand challenges while leading to 

a predictive understanding of couplings between 

energy systems and natural systems.

• Create an energy sustainability modeling 

and synthesis center for multidisciplinary 

teams to address key energy-water-land 

research challenges. The center will organize 

multidisciplinary teams to understand impacts 

(e.g., energy-water-land influences on energy 

infrastructure), sustainability working groups to 

resolve short-term research questions, and also 

facilitate and house integrated data products at 

resolutions needed for sustainability analyses.
November 2017 Biological and Environmental Research Advisory Committee 61

Chapter 5 — Energy Sustainability 

potential geoengineering solutions for climate 

change mitigation such as biomass carbon capture 

and storage.

Though progress since the 2010 Grand Challenges 

report has been steady and, in places, noteworthy, 

many challenges remain. Researchers have gleaned 

an initial understanding of how to produce enough 

energy to support people at a higher standard of 

living, but continuing breakthroughs will be needed 

to better understand and improve the sustainability 

of different energy-land-water configurations. Fulfilling these challenges will be essential to achieving 

BERAC’s vision for energy sustainability. Four grand 

challenges central to achieving this vision are outlined (see Energy Sustainability Summary of Grand 

Challenges and Action Items, p. 60) and discussed 

further in the following sections.

Grand Challenge Research 

Recommendations

Grand Challenge 5.1 

Further develop the science of coupling energy, 

water, and land use across different spatial and 

temporal scales to understand environmental 

impacts and changing climate and to better predict 

net biogeochemical fluxes.

The past and projected future responses of natural 

and human systems in three key sectors—energy, 

water, and land—to economic development and 

changing weather patterns are being studied in 

more depth than was possible with the data and scientific understanding that existed in 2010, but significant challenges remain. These challenges involve 

developing a better understanding of critical relationships and data requirements within these key 

sectors, including a better understanding of important phenomena and drivers at nanometer to global 

scales. Not all nanoscale processes will be important at higher levels of aggregation for all purposes, 

but some research is necessary to understand where 

processes at one scale will have important effects 

on processes and outcomes at higher aggregation 

levels. Even as progress is being made in these areas 

for individual sectors and regions, important interactions and feedbacks among these three sectors 

and with the Earth system are recognized increasingly as areas experiencing the worst impacts from 

energy, water, and land development now and likely 

into the future.

The impacts of changes in climate such as higher 

temperatures and varying precipitation patterns, 

as well as those in human systems such as water 

infrastructure and markets, will have important and 

interacting consequences for energy, water, and 

land use systems and markets. Better understanding 

of these energy-water-land feedbacks and interactions is crucial to assess their integrated impacts on 

people, infrastructure, and natural systems and to 

explore tradeoffs among alternative mitigation and 

adaptation strategies at appropriate spatial and temporal scales.

After farming, electric power generation is the 

largest user of water. In addition to requiring water 

for hydropower electricity generation and cooling, 

thermoelectric plants release heated water into the 

environment. As in-stream temperatures rise with 

climate change, the release of heated water can 

be problematic for aquatic ecologies, as individual power plants can have a significant impact on 

local in-stream temperatures. In addition, higher 

temperatures reduce the efficiency of thermoelectric power generation by increasing turbine outlet 

temperatures. The nation’s largest consumptive use 

of water is irrigated agriculture, but the essential 

delivery of this water to crops depends on electricity, thereby providing another set of links in the 

energy-water-land chain. In addition, some electric 

utilities have been turning to biomass co-firing as a 

near-term option for generating cleaner power. The 

transportation sector also has increased its demand 

for biofuels based on bioenergy crops, placing further demands on water systems and land. There are 

many other dimensions to the energy-water-land 

nexus, but these particular components are tightly 

interconnected and likely to be especially vulnerable 

to changing climate, while also offering significant 
62 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

opportunities for adaptation. Since the nature of 

these interactions varies greatly by location, infrastructure, and biophysical constraints, gridded 

modeling of these nexus components is potentially 

quite valuable.

Weather variability and climate change are critical 

drivers of future developments in the energy-waterland system. These impacts need to be studied with 

future precipitation and temperature projections 

at the appropriate geographical scale to capture 

relevant physical linkages, especially for extreme 

events, which likely will put pressure on existing 

infrastructure. The link between precipitation and 

the hydrological system is obvious, but evolving relationships among water use for urban development, 

power generation, cooling, and agriculture are more 

complex, particularly when considering the interactions among runoff, reservoir storage, groundwater 

accumulation and depletion, and in-stream flows. 

Electricity production is very sensitive to extreme 

heat, which boosts the demand for air conditioning. 

Unfortunately, such demand surges also are likely 

to be closely related to surges in irrigation demand. 

Recent statistical studies have found that temperature extremes are crucial predictors of yields for 

corn, soybeans, cotton, rice, and wheat; high temperatures increase the plant’s water demand and, at 

the same time, diminish water availability through 

evapotranspiration. A fine-grid analysis is necessary 

to detect these thresholds insofar as a partial or temporal averaging can make detecting them impossible.

Finally, this discussion demonstrates that energy, 

land, and water models are highly interdependent, 

with the inputs to each often being some of the 

outputs from others. Furthermore, these models are 

linked through both physical relationships and economic market relationships, the latter through trade 

and transfer agreements.

Energy-land-water research is well-aligned with BER 

objectives for improvements in three important 

areas: (1) model representation of energy system 

transition impacts on society and changes in other 

sectors, as well as impacts of the whole Earth system on energy system transitions; (2) the research 

community’s ability to include interactions and feedbacks among the energy, water, and land systems and 

feedbacks in these assessments through integrated 

impact analyses or IAMs; and (3) the ability of 

ESMs or IAMs to project the feedbacks from sector 

changes to the whole Earth system and then back on 

the energy system (see also Chapter 3 on Earth and 

Environmental Systems Sciences, p. 21).

Grand Challenge 5.2 

Use observational, experimental, and model-based 

approaches to explore the sustainability of alternative energy systems, incorporating stability and 

resilience analysis, uncertainty, transition paths 

from current infrastructures, and the use of appropriate common metrics.

The multiscale, interconnected, and nonlinear nature 

of energy, water, and land systems, coupled with 

their vulnerability to multiple stressors, represents 

a significant challenge for examining the risks and 

tradeoffs among alternative energy strategies. Transitioning to a future energy grid that is in perpetual, 

sustainable balance with the natural system’s evolution demands several new advances. Identifying 

pathways to energy system resiliency requires new 

experimental and observational approaches specifically designed to quantify key interactions influencing coupled energy–natural system behavior. Also 

required is a new generation of computationally 

efficient, data-modeling-analysis frameworks that 

can support scenario analytics, uncertainty quantifications, and well-defined metrics to evaluate a range 

of outcomes. Development of these new approaches 

will benefit from integration of a range of developing 

technologies, including exascale computing, deepdata analytics, networked drones, and in situ microbial sensing.

An important challenge for Earth system modeling 

is to understand the ecological, biological, and carbon cycle interactions and feedbacks in the climate 

system to identify potential tipping points and mitigation strategies. Energy systems—whether thermoelectrically, hydrologically, wind, solar, or biologically 
November 2017 Biological and Environmental Research Advisory Committee 63

Chapter 5 — Energy Sustainability 

based—provide acute points of interaction that may 

magnify or attenuate key interactions and feedbacks 

that affect land use and vegetation dynamics. A 

follow-on modeling challenge is to stress-test ESMs 

with energy-related drivers, fully exploring potentials 

for particular energy systems to enhance rather than 

diminish the services integral to ecosystem and landscape functioning at different scales.

The new approaches will vary greatly depending 

on the energy strategy’s particular type and scale, 

as well as assumptions about stressors that prohibit 

energy sustainability (see Fig. 5.1, p. 58). For example, enabling a sustainable billion-ton bioenergy 

feedstock supply requires the production of “more 

with less,” or increasing the quality and volume of 

crop yields on marginal lands challenged by poor 

soil health, water availability, and nutrient supply. At 

the scale of individual plants, robust approaches are 

urgently needed to measure and mechanistically predict plant growth, form, function, and interactions 

with the surrounding biotic and abiotic environment. New soil and plant sensors and proxies could 

be developed to identify (1) key controls on tolerance to environmental stresses such as drought, heat, 

and low soil fertility; (2) how plant resources are 

partitioned to diverse processes and organs, including respiration, roots, and aboveground biomass; 

(3) how root and root-microbe processes control the 

acquisition of water and nutrients; and (4) how the 

plant microbiome, including rhizosphere communities, mycorrhizal symbioses, and endophytes, contributes to plant performance. Such quantification 

could, for example, lead to new insights about plant 

enzymatic activities under water stress or the soil 

microbial community’s ability to access atmospheric 

nitrogen and soil phosphorous, elements which are 

abundant but not necessarily bioavailable and could, 

in turn, contribute to bioenergy sustainability.

At the basin scale, hydropower, power plant cooling, and agriculture rely on watersheds to sustainably deliver water and nutrients downgradient. 

Watersheds experience a range of stressors having 

unknown ramifications on energy systems. Examples of stressors include floods, droughts, fires, 

early snowmelt, beetle infestation, and land use 

change. Developing a predictive understanding 

of watershed behavior and response to increasingly frequent stressors is challenging mainly 

for three reasons. First, there is a wide range of 

hydrological-biogeochemical interactions in a watershed that occur among plants, microorganisms, 

organic matter, minerals, dissolved constituents, and 

migrating fluids that influence water, nutrient, carbon, and other key elemental cycles. These interactions often vary significantly, both laterally across a 

watershed and vertically between its many compartments (e.g., bedrock, groundwater, capillary fringe, 

vadose zone, soils, land surface, and vegetation).

Second, accurately predicting the cumulative watershed response to a perturbation is particularly challenging because such a prediction must consider 

the spatial variability of nonlinear responses and 

their aggregation. Traditional watershed analysis 

approaches have been developed based on relatively simple watersheds, often with an assumption 

that historical hydrological trends (e.g., precipitation) are representative of future trends. Recent 

studies highlight the need for innovative research 

paradigms that analyze and predict complex and 

dynamic watershed behavior under future conditions, their impact on energy systems, and other 

societal benefits. New paradigms will require new 

networked sensing systems that can jointly (i.e., in 

many cases, remotely and autonomously) monitor 

above- and belowground responses to perturbations 

across scales. Also needed for this challenge are 

physics-based, scale-aware surface-subsurface watershed models that can accurately predict aggregated 

hydrological and biogeochemical watershed exports 

under a range of stressors, as needed, to assess the 

sustainability of downgradient hydropower and 

agricultural systems.

Third, the equally complex and understudied 

domain at the energy-land-water nexus is the 

energy-intensive urban landscape. BER research 

historically has focused on the carbon, water, 

and energy cycles of natural ecosystems and how 

these processes influence the climate system. 
64 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

More recently, BER has started to explore water 

and water-energy systems, but few efforts are 

focused on quantifying intensively coupled 

natural-human-energy system interactions, such 

as those occurring within urban and urbanizing 

systems. Urban areas, which often rely on a mixture of energy strategies, consume a disproportionately larger percentage of the planet’s energy 

and freshwater, representing over 70% of fossil 

fuel carbon dioxide (CO2) emissions, which is 

nearly triple the net carbon uptake from terrestrial or oceanic carbon sinks (Churkina 2016). 

Yet, lacking are (1) the fundamental knowledge 

of hydrological-biogeochemical interactions that 

occur in urban landscapes and, therefore, (2) sufficient understanding of the drivers, controls, and 

feedbacks between human and Earth systems to 

inform the design of more sustainable energy outcomes. Bringing BER resources to bear on these 

questions will enable forecasting and mitigation 

against unsustainable outcomes.

Examining the tradeoffs associated with various 

energy strategies or their mixes—in the context of 

land-water systems, responding to a range of stressors, and with consideration of the metrics discussed 

previously—will require new cyberinfrastructure 

and software tools. These tools must be able to perform scenario analytics and quantify uncertainty 

while handling diverse models and data associated 

with a range of strategies, observational datasets, 

and dynamic models. Scale-adaptive simulation 

capabilities, which use adaptive mesh refinement 

methods to “telescope” into subsystems that require 

finer resolution or different physics, may provide 

a flexible approach. Both scale-adaptive as well as 

reduced-order models are expected to be critical for 

scenario exploration and decision analysis, uncertainty quantification, and visualization of outcomes 

and tradeoffs (see also Chapter 6 on Data Analytics 

and Computing, p. 71).

BER is uniquely positioned for assessing, understanding, and predicting interactions among a range 

of energy and natural system processes needed to 

underpin sustainable bioenergy, wind, hydropower, 

solar, thermoelectric, and other strategies. First are 

BER’s existing programs and investments to understand subsurface microbes, plant, and plant-microbe 

interactions. Key to rapid advances are in situ

sensing, high-throughput genome analyses, and 

next-generation visualization technologies (see 

also Chapter 2 on Biological Systems Science, p. 3). 

Second are long-standing programs in hydrology 

and biogeochemistry that have rapidly advanced the 

understanding of watershed processes across a variety of scales and Earth system compartments, from 

the subsurface to the troposphere. This expertise 

could be extended to include interactions between 

natural and human-dominated landscapes (see also 

Chapter 3 on Earth and Environmental Systems 

Sciences, p. 21). Third is BER’s extensive data and 

simulation capacity that enables effective integration 

of diverse and multiscale processes into watershed, 

regional, and global contexts.

Grand Challenge 5.3 

Understand how variability and change in natural 

systems affect energy system structure and function and determine how best to build this knowledge into models.

Numerous studies have looked at ways in which 

different energy systems and technologies ultimately affect the physical environment through their 

implications for GHG emissions. Considerably less 

literature is available on the real and potential vulnerabilities of energy systems to change and variability 

in physical systems, even though concrete demonstrations are beginning to show that these influences 

are real and, in some cases, substantial. Important to 

pursue and include in models as critical feedbacks 

are both empirical assessments of current risk and 

modeling studies to investigate the potential for 

future risks to energy systems and infrastructure.

The rationale for incorporating the effects of physical system change and variability on energy system structure and function in the BER portfolio 

is straightforward. The effects of these first two 

vulnerabilities on the delivery of energy services 
November 2017 Biological and Environmental Research Advisory Committee 65

Chapter 5 — Energy Sustainability 

are a function of many factors, among them the 

development and implementation of both current 

and new technologies. However, when either individual weather events or long-term systemic change 

in the physical climate system have the potential to 

interrupt the delivery of energy systems, they reveal 

a new set of feedbacks that operate from the atmosphere to the built environment, influencing the 

relationships among land, water, and energy systems.

The most obvious examples for understanding how 

physical systems influence energy system structure 

and function are from the repercussions of storm 

events on energy infrastructure. Well known, for 

example, is that Hurricane Katrina disrupted the 

supply of natural gas coming into the port of New 

Orleans for several months because of the storm’s 

impact on shallow-water oil and gas rigs in the Gulf 

of Mexico. Not only the storm’s intensity, but also 

the locations of the rigs themselves, established the 

vulnerability of that energy system component to 

major events such as hurricanes. Similar phenomena resulted from Superstorm Sandy’s impacts on 

electricity generation and transmission in the New 

Jersey and New York area. In this case, however, the 

actual distribution of sensitive infrastructure had 

scarcely changed since being established roughly a 

century ago, demonstrating that the spatial distribution of critical infrastructure can “lock in” climate 

vulnerabilities for a very long time. This effect, not 

previously sufficiently well understood, has important implications for societal resilience, demonstrating that rebuilding systems with their same degree 

of vulnerability would not be wise.

More recent examples center around longer-lasting 

events, especially heat waves and drought that 

affect the availability of water for cooling thermoelectric and nuclear power plants. The severity of 

this effect during recent heat wave and drought 

events in Texas and the southeastern United 

States required power plants to be derated (i.e., 

their available power generation capacity was 

decreased), affecting their ability to provide 

regional electricity. A third type of vulnerability 

is implicit in the short-term response of energy 

demand to climate and weather events. A germane 

example is the well-known response of electricity 

demand to periods of high temperature and heat 

waves. A fourth type of vulnerability to physical 

systems can develop over a longer-term period, as 

energy technologies themselves begin to change. 

In particular, if bioenergy crops penetrate regional 

energy markets to a great extent, with or without 

carbon sequestration, their supply of material 

obviously is dependent on climate conditions and 

water availability that are favorable to the plants 

themselves. Thus, longerterm patterns of variability 

and change in the physical climate system, similar 

to those already observed for natural systems and 

phenological change, become important considerations in any assessment of future energy infrastructure and its projected functioning.

Current energy models or IAMs that seek to understand how energy systems change over time do not 

fully represent energy system feedbacks with the 

physical climate system. Thus, these models do not 

represent the potential for either short-term, eventdriven effects of the physical climate system on 

energy or the longer-term effect of systemic changes 

and variability on energy infrastructure siting and 

performance. Because the sensitivities are known 

to exist, however, they must be understood much 

better from a quantitative perspective to be incorporated into models.

Relevance for BER of this limited feedback in models is twofold. First, from a climate impact perspective, these effects are poorly understood in terms 

of their frequency and intensity. Efforts like DOE’s 

E3SM development should be able to represent 

the distribution of climate-related weather events 

in such a way that they can contribute to a growing 

body of impact studies that go beyond ecosystems 

and water resources. Second, from a more comprehensive Earth system perspective, these effects are 

all examples of feedbacks from the climate system 

to systems with a human dimension, and thus they 

have direct consequences for the evolution of those 

human systems over time, as well as implications for 
66 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

how these systems might be designed to minimize 

their vulnerability.

Grand Challenge 5.4 

Create new data streams and more effectively 

use existing observations to ensure the availability of scale-appropriate data, particularly 

related to high-resolution land use, landscape 

infrastructure, demographic change, and 

energy-land-water research.

The integration of land use information into ESMs 

has yielded new insights about contemporary and 

future changes in the terrestrial carbon budget, forest dynamics, and crop productivity by more realistically constraining model inputs and responses. Key 

datasets used to develop land use models include 

time series of cropland area and yield, pasture area, 

and rates of wood harvesting. Future projections 

often rely on historical relationships between these 

variables and regionally aggregated estimates of 

population, economic wealth, and energy policy. To 

facilitate the development of next-generation land 

use models, a new suite of high-resolution datasets 

describing human and energy systems is a priority. 

Investment in this area has the potential to be transformative, enabling new insight about the drivers 

and responses of the global Earth system to changing patterns of energy and land use.

At least five different data classes are needed to create more mechanistic models and to stimulate the 

development of new conceptual approaches in the 

field of energy sustainability. These classes include 

human demography, land use intensification, transportation, economic wealth, and energy systems. 

For each data class, efforts are under way to develop 

regional- and global-scale products. However, many 

of the datasets have limited or no time series information, which is crucial for understanding how 

human systems (and their impacts) are evolving 

through time. Nor has there been coordination to 

create a suite of products that are internally consistent across different modeling domains. The 

Mauna Loa atmospheric CO2 time series is perhaps 

the archetypal example of the critical importance 

of long-term Earth system monitoring. For other 

Earth system variables, the National Aeronautics 

and Space Administration (NASA) and other space 

agencies have recognized the importance of sustained funding for developing Earth system data 

records that span different satellite instruments and 

measurement techniques. Similarly, within BER, 

long-term funding for the AmeriFlux Network of 

sites measuring ecosystem CO2, water, and energy 

fluxes has enabled the scientific community to 

understand how terrestrial ecosystems are responding to short-term and secular changes in atmospheric composition and climate, with the value 

of these time series increasing exponentially with 

their longevity (see also Chapter 7 on User Facilities 

and Research Infrastructure, p. 85). In contrast, for 

human and energy systems, there is less recognition of the importance of long-term data records; 

as a consequence, data paucity has created a critical 

bottleneck limiting the growth of the energy sustainability field as a whole.

For human demography, extremely high spatial 

resolution population information is available from 

LandScan, which BER already supports. LandScan 

provides state-of-the-art contemporary global population estimates that are valuable for many applications. However, to connect population to observed 

changes in land cover, hydrology, and environmental 

processes such as wildfire dynamics, quantitative 

time series of changing human populations are 

needed that possess an internally consistent methodology and for which quantitative uncertainties are 

available. This type of data is essential for time series 

analysis and trend detection. More broadly, ancillary 

data on age structure, wealth (i.e., gross domestic 

product), and housing density would considerably 

amplify the value of these data for determining 

impacts, using the Kaya identity or other conceptual 

approaches for expressing total CO2 emissions levels.

Indicators of land use extensification (i.e., clearing new land for human use) may be more easily 

estimated from remote-sensing observations than 
November 2017 Biological and Environmental Research Advisory Committee 67

Chapter 5 — Energy Sustainability 

indicators of intensification (i.e., higher yields on the 

same area of land). As a result, arguably some of the 

greatest achievements in the understanding of land 

cover and land use change over the last 2 decades 

have come from the use of high-resolution satellite 

imagery to quantify patterns of forest cover change, 

deforestation, and cropland use. However, land use 

intensification can have equally important impacts 

on global biogeochemical cycles and disturbance 

dynamics, and this information is essential for 

improving many aspects of ESMs. For example, all 

the prognostic fire models participating in the Fire 

Model Intercomparison Project (FireMIP) underestimate the declining incidence of fire in grasslands 

and savannas worldwide over the past 2 decades. A 

likely contributing factor is that none of the models 

simulate the density of cattle or other livestock on the 

landscape. In many areas, cattle density has increased 

severalfold, reducing fuel amount and continuity that, 

in turn, limit fire size. Livestock also have profound 

impacts on the global methane cycle, making these 

data essential for accurately simulating atmospheric 

concentrations of this important GHG. Apart from 

livestock density, other closely related indicators of 

land use intensification include fertilizer use and 

water transfers for irrigation. Reliable estimates of 

nitrogen fertilizer use and irrigation water withdrawals do not even exist for the midwestern United 

States, yet both are crucial drivers of GHG fluxes, 

hydrologic flows, and primary productivity in agricultural landscapes, with concomitant effects on energy, 

water, and carbon interactions.

Transportation databases lack sufficiently resolved 

time series information for both surface and air 

travel. Particularly needed are spatially explicit information on road density and the number of cars and 

trucks for at least the past few decades. For air travel, 

information about passenger transfers among cities, 

possibly at a monthly resolution, is critically needed. 

Such data are important not only for energy use and 

emissions, but they also can inform models for the 

spread of invasive species and disease.

Likewise, there are crucial needs for better 

resolved economic and energy system databases. 

High-resolution data products extending from at 

least 1990 forward are needed to sufficiently underpin energy sustainability modeling efforts.

Discussion of Action Items

Achieving BERAC’s 20-year energy sustainability 

vision requires advances and integration of many 

components. Two key components, described in 

this section, include (1) establishing energy sustainability testbeds for addressing crucial research 

questions at multiple scales and (2) creating an 

energy sustainability modeling and synthesis 

center for multidisciplinary teams to address key 

energy-water-land research challenges.

Establish a Network of Energy 

Sustainability Testbeds

BERAC recommends developing a Network of 

Energy Sustainability Testbeds (NEST) at strategically distributed study sites. A world-unique 

NEST would comprise a suite of testbeds spanning 

a range of scales, where each scale is relevant for a 

particular energy strategy and associated air-waterland forcing or vulnerability. Each testbed in this 

network would be used to address a specific suite of 

questions and challenges appropriate for that strategy, but synthesis across the testbeds could offer an 

unprecedented opportunity to advance fundamental knowledge and tools needed to develop a range 

of resilient and interconnected energy strategies 

(see Fig. 5.2. Proposed Network of Energy Sustainability Testbeds, p. 68).

Several characteristics will be important for the 

success of NEST. Envisioned is a network of about 

five energy sustainability testbeds, which together 

would be funded at a level similar to that of a DOE 

Energy Innovation Hub. The choice of testbed 

locations should be carefully considered and meet 

several criteria:

• Distributed testbeds must cover a range of 

scales matched to the footprint associated with 

interactions between energy strategies and 

associated natural system processes. Example 
68 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Fig. 5.2. Proposed Network of Energy Sustainability Testbeds. These testbeds comprise a suite of strategically distributed 

study sites chosen to span a range of scales, each relevant to a particular energy strategy and associated air-water-land forcing. Each testbed will be used for experiments, observations, and modeling to address a unique set of questions; synthesis 

across the testbeds will offer an unprecedented opportunity for advancing the fundamental knowledge and tools needed 

to develop a range of resilient and interconnected energy strategies. [Bottom left image: Soil aggregate illustration to right 

of black arrow modified from Jastrow, J. D., and R. M. Miller. 1998. “Soil Aggregate Stabilization and Carbon Sequestration: 

Feedbacks Through Organomineral Associations.” In Soil Processes and the Carbon Cycle. 207–23. Eds. Lal, R., et al. CRC Press 

LLC, Boca Raton, Florida. Farm field courtesy U.S. Department of Energy’s Great Lakes Bioenergy Research Center. Urban 

landscape courtesy iStockphoto. California map on left courtesy California Energy Commission. California map on right

courtesy California Department of Water Resources. U.S. national power grid map courtesy Federal Emergency Management Agency]
November 2017 Biological and Environmental Research Advisory Committee 69

Chapter 5 — Energy Sustainability 

scales that could be considered include a farm, 

metropolitan area, river basin, coastal system, 

multistate region, or even a continent.

• Each testbed should address several of the 

grand challenges in energy sustainability, 

leading to a predictive understanding of 

couplings between energy systems and natural 

systems (i.e., land, air, and water) relevant 

to the particular testbed. As illustrated in 

Fig. 5.2, p. 68, the data, models, energy mix, 

and variabilities, as well as energy-waterland vulnerabilities, approaches, and science 

questions, will vary with scale and testbed.

• Network testbeds should be chosen to 

optimize opportunities for advancing the 

understanding of how energy-air-water-land 

systems couple across scales and strategies 

and how they respond to different stressors. 

If carefully chosen, synthesis of knowledge, 

models, and data across the network will offer 

significant, sum-is-greater-than-the-parts 

advances toward BERAC’s 20-year vision.

• Community acceptance of key testbed 

characteristics will be a critical component of 

success. Envisioned are community workshops 

that involve scientists as well as stakeholders 

to further refine the network construct and 

identify priority criteria.

Synthesis across the testbeds will offer an unprecedented opportunity to advance fundamental knowledge needed for developing resilient energy strategies.

Create an Energy Sustainability 

Modeling and Synthesis Center

A Center for Energy Sustainability Modeling and 

Synthesis (CESMS) would provide a physical location for teams to tackle complex interdisciplinary 

problems related to energy sustainability challenges 

and house a location for the assembly of key databases that would be available both to teams and the 

larger scientific energy sustainability community.

Energy sustainability research, appropriately integrated, is highly interdisciplinary. Addressing any 

one of its challenges requires the pursuit of multiple lines of inquiry. Doing this efficiently (i.e., 

taking advantage of potential synergies and better 

ensuring integration) will require interdisciplinary 

teams working in concert to target a specific challenge. Envisioned are two types of teams working 

at two different temporal scales, funded under the 

CESMS umbrella:

• Energy Sustainability Process Teams. Multidisciplinary teams working across institutional 

boundaries on long-term problems could be 

a powerful means to quickly advance energy 

sustainability science. For example, the formation of a team to understand the impacts of 

changing climate extremes on energy-waterland interactions and subsequently on the U.S. 

energy infrastructure might draw on a mix of 

national laboratory and university scientists, 

together with industry stakeholders. The team 

would design observations and experiments, 

assemble data products, and create the mix 

of models required to provide an important 

predictive capacity. The complexity of the 

problems tackled would require multiyear 

commitments by all parties.

• Ad Hoc Energy Sustainability Working 

Groups. These teams would work on problems 

of more limited scope that could, with CESMS 

support, be reasonably expected to resolve a 

problem over the course of a year or less via 

multiple working group meetings. Ad hoc

proposals would be solicited from multidisciplinary groups that would gather at CESMS 

several times to discuss and work on problems 

best addressed in a center setting with its cyberinfrastructure and logistical support. Working 

groups also might be assigned a center-based 

postdoctoral scientist to facilitate access to 

databases and other resources to best synthesize 

existing data and models related to a particular 

energy sustainability question.
70 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

CESMS also will provide a home for assembling 

integrated data products at appropriate resolutions 

for energy-land-water resources as elaborated on 

in the previous discussion on energy sustainability 

testbeds. Primarily, this includes long-term data 

for key human and energy system variables not 

now available in assembled form from any source. 

BERAC proposes an early workshop to identify 

community priorities, followed by a funding model 

[e.g., perhaps similar to NASA’s Making Earth 

Science Data Records for Use in Research Environments (MEaSUREs) Program], which provides the 

necessary data continuity in an internally consistent 

way over a period of at least several decades. One 

path might be to fund one proposal in each of the 

five variable classes identified previously: human 

demography, land use intensification, transportation, economic wealth and trade, and energy use.

Overall, CESMS would provide a means for connecting scientists with data in a physical environment designed to facilitate the multidisciplinary 

approaches crucial for addressing key energy sustainability questions, thereby advancing energy sustainability science quickly and efficiently.
November 2017 Biological and Environmental Research Advisory Committee 71

Chapter 6 — Data Analytics and Computing

Grand Challenges in Data 

Analytics and Computing

Overarching Grand Challenge and 20-Year Vision

Develop the approaches and computational capabilities to collect, store, and analyze large-scale 

data across temporal and spatial scales.

6

Ensuring that Multiscale Scientific 

Data Support Scientific Rigor, 

from Collection to Analysis

Research efforts in data analytics and computing continue to focus on processing ever 

larger amounts of data in a more timely 

manner. Additional efforts aim to combine and analyze complex data at different spatial and temporal 

scales to increase understanding and predictability 

of natural systems across scales. In further developing these capabilities, several objectives are crucial:

• Ensure that scientific data, at whatever scale, are 

readily accessible to researchers and the research 

community; well documented and maintained, 

with identifiable provenance; discoverable; and 

referenceable in a rapidly evolving commercial 

technology environment and in the presence of 

new data collection technologies.

• Ensure that software system design and 

engineering for data analysis and modeling are 

focused not only on scalability for today and 

tomorrow, but also are readily adaptable to 

emerging computer architectures.

• Develop a cross-disciplinary framework, 

including the underpinning mathematical, 

statistical, and computational tools, to facilitate 

model integration across multiple scales with 

requisite scientific rigor.

• Build capacity to include “human-in-the-loop” 

in computational analyses of multimodal 

streaming data, which will require a continual 

process of incorporating new ways to combine, 

visualize, and adjust real-time data streams, along 

with mechanisms for enabling experimental 

parameters to be changed on the fly.

In its 2010 Grand Challenges report (BERAC 

2010), the Biological and Environmental Research 

Advisory Committee (BERAC) identified handling 

and interpreting the explosion of scientific data 

as a primary challenge. Not surprisingly, six of the 

seven 2010 recommendations in the chapter, Grand 

Challenges in Computing for Biological and Environmental Research, were focused on data-related 

issues, from promoting more standardization in 

data capture and analysis methods to developing 

new computing paradigms to harness extreme-scale 

parallelism for data-intensive analysis applications 

(see sidebar, Summary of 2010 BERAC Research 

Recommendations for Computing, p. 72). These 

challenges have only increased in magnitude since 

the 2010 report and are continually escalating with 

the development of new instruments and sensors 

that stream data in real time.
72 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Also, unsurprisingly, BERAC’s 2017 Grand Challenges workshop confirmed and maintained that 

data are a primary focus of the computational challenges for the U.S. Department of Energy’s (DOE) 

Biological and Environmental Research program 

(BER). However, since the 2010 report, these challenges have been more clearly defined, moving more 

toward specific challenges posed by (1) the extreme 

volume, velocity, and diversity of available scientific 

data; (2) the need to develop effective means to 

store, analyze, and interpret extremely large, distributed, and heterogeneous data collections; (3) the 

rapidly changing computing architectures; and (4) 

the need for scientific researchers to more immediately visualize and interact with experimental data to 

steer experiments in the most fruitful directions.

These challenges are not specific to BER; they 

are well-recognized challenges in the broader 

scientific community and are active areas of 

research and community engagement across many 

scientific disciplines.

Examples of broad research community focus on 

these issues include:

• A 2017 workshop by the international Human 

Frontier Science Program (HFSP), which 

gathered senior data stewards and funding agency 

representatives to work toward a unified vision 

and plan for a coordinated international effort 

to better ensure long-term sustainability and 

appropriate alignment of funding with scientific 

impact. The results include a publication that 

articulates the challenge and also supplies 

consensus definitions of core principles and goals 

(www.hfsp.org; Anderson et al. In review).

• In the biomedical research community, a 

National Institutes of Health (NIH) framework 

for supporting a “Findable, Accessible, 

Interoperable, and Reusable” (FAIR) data 

standard, and establishing a “commons” for 

tracking and documenting data collected in the 

course of research (Miller 2016).

• Within the DOE national laboratory system, 

the Analysis in Motion (AIM) Initiative at 

Pacific Northwest National Laboratory (PNNL) 

that was established to address the challenges 

of streaming data and more fully integrating 

human-in-the-loop (see Fig. 6.1. AIM Initiative, 

p. 73). AIM is considered a major laboratory 

initiative at PNNL.

Summary of 2010 

BERAC Research 

Recommendations 

for Computing

• Establish a new data management paradigm for 

data-intensive science with ontologies as a basis 

for semantic data representations; standards 

for experimental protocols and data exchange; 

and an open-access, open-development data 

management infrastructure.

• Create a new publishing paradigm that credits 

and rewards researchers for publishing peerreviewed datasets or analytical methods 

in addition to conventional peer-reviewed 

journal articles.

• Develop new computing paradigms capable of 

meeting the enormous parallel processing and 

data-intensive analysis needs now emerging for 

biological, climate, and environmental data.

• Standardize experimental and computational 

protocols and methods to increase data 

integration, data usability, and system 

interoperability to improve research productivity.

• Improve data usability and model accuracy 

by ensuring that appropriate data quality 

standards are created and stored with the 

accompanying data.

• Design and build software solutions that provide 

researchers with better access to increasingly 

large, complex, and interrelated datasets.

• Develop virtual laboratories and tools 

to more fully engage human cognitive 

faculties and provide richer opportunities for 

scientific collaborations.
November 2017 Biological and Environmental Research Advisory Committee 73

Chapter 6 — Data Analytics and Computing

• Globus cloud research data management 

services (Chard et al. 2016) that are being 

applied to data publication and sharing within 

DOE’s Energy Exascale Earth System Model 

(E3SM), DOE light sources, National Institute 

of Standards and Technology Materials Data 

Facility (Blaiszik et al. 2016), NIH Big Data to 

Knowledge (BD2K) projects, and elsewhere, 

enabling secure, reliable access to many 

petabytes of data.

The computer and data science challenges in these 

areas clearly are not merely stand-alone goals 

written by and for computer and data scientists; 

rather, they are driven by the key scientific challenges that researchers face across BER biological, 

environmental, and climate science studies. Examples include: (1) predictive biology for microbial 

communities; (2) scaling from microbes to Earth 

systems; (3) elucidating the coupled Earth system, 

which includes climate patterns, the oceans, and 

plants, as well as human impacts that encompass 

the influence of urban environments and effects of 

industrial processes; and (4) transforming biology 

into a quantitative and predictive science. Addressing these scientific challenges requires new types 

of experiments—with high-resolution imaging, 

high-throughput data collection, and long-term 

observations of whole ecosystems (both above and 

below ground)—while leveraging newly available 

and ubiquitous observational and sensor systems 

and the benefits of streaming data from instrumentation. These core BER science areas each are 

developing new modeling approaches, including 

integrated models that cross traditional disciplinary 

boundaries and scales. Addressing these challenges 

also requires the support of data and computational 

science advances that are responsive to and keep 

pace with scientific needs and directions (see sidebar, U.S. Department of Energy User Facility Perspective: Joint Genome Institute, p. 74).

New Sensor Technologies. The 2010 Grand Challenges report (BERAC 2010) anticipated new sensor 

technologies. The rapid commercialization of these 

technologies, with tremendous cost decreases, provides great opportunity for broader data collection, 

but presents the challenge of ensuring that data are 

Fig. 6.1. Analysis in Motion (AIM) Initiative. AIM is developing a new analysis paradigm to provide continuous, automated 

synthesis of new knowledge and enable measurement systems to be steered in response to emerging knowledge, rebalancing the effort between humans and machine (Dasgupta et al. 2017; Crouser et al. 2017). [Image courtesy Mark Greaves, Pacific 

Northwest National Laboratory]
74 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

collected in a scientifically meaningful form. Raw 

sensor measurements will serve science well only 

if they are meaningfully annotated with metadata. 

Further, such measurements will be most meaningful if they are collected with a predefined scientific 

methodology that specifically guides which data 

are collected. Scientists still may make unexpected, 

serendipitous discoveries by poring through all data 

collected, but BER investigators should evaluate 

these new data sources carefully and design controlled experiments to capture the data and metadata 

needed to produce valid scientific results over time.

Disruptive Technologies. Community participants in the 2017 BERAC Grand Challenges workshop expressed their expectation to see continued 

exponential growth in data volumes over the next 

2 decades due to a cycle of continuous technological advances, with an ever-growing gap between 

large-scale data needs and current capabilities to 

address those needs. Importantly, workshop participants also identified the emergence of potentially highly disruptive technologies as a major 

challenge. Some emerging technologies likely will 

lead to increased growth rates in data volumes and 

velocity (e.g., whole-genome and very long read 

sequencing). Others, such as ubiquitous sensors, 

will result in computational challenges due to their 

geographic distribution and potentially remote 

or difficult-to-reach locations where they can be 

placed (e.g., underground). Advances in computing infrastructures also are likely to lead to disruptive technologies. With the end of Moore’s law in 

sight, a wide variety of novel computing architectures (e.g., exascale, quantum, and neuromorphic 

computing, as well as supercomputers specialized 

for deep learning) already are emerging. Effectively 

assessing and harnessing these new technologies 

for scientific discovery will create additional challenges for BER science (see Data Analytics and 

Computing Summary of Grand Challenges and 

Action Items, p. 75).

Building on Progress Related 

to the 2010 Grand Challenges

The BER research community has made significant 

progress in harnessing leading-edge computing technologies [e.g., those provided by DOE’s Office of 

Advanced Scientific Computing Research (ASCR) 

Leadership Computing Facilities] to advance climate and biological science. In close collaboration 

with the computer science and applied mathematics research communities, new scalable numerical simulation methods and libraries have been 

developed and deployed. Climate, environmental, 

and biological science projects are represented in 

the recently launched DOE Exascale Computing 

Project (exascaleproject.org) and are continuing to 

drive the development of next-generation modeling 

systems. The general high-performance computing 

(HPC) skill level among BER researchers is such 

that while the community faces future hurdles in the 

U.S. Department of Energy 

User Facility Perspective: 

Joint Genome Institute

The DOE Joint Genome Institute’s most pressing 

data and computing challenges, as indicated in 

2017, include:

• Inferring function and gene ontology from 

large datasets.

• Bringing compute cycles to data rather 

than data to compute cycles for managing 

exascale data.

• Leveraging high-performance computing 

resources in the exascale era.

• Conducting large-scale calculations across 

large datasets that span both space and time 

to infer microbial community composition and 

interactions.

• Ensuring affordable memory through 

distributed systems to analyze exascale 

datasets efficiently.

• Combining and building “systems-level” 

understanding from orthogonal datasets.

• Reducing data to distill biological signals 

from noise.
November 2017 Biological and Environmental Research Advisory Committee 75

Chapter 6 — Data Analytics and Computing

required extreme scalability of algorithms, performance, and portability, researchers are well placed 

to continue making advances if provided with the 

needed support.

Several recent workshops were dedicated to the data 

and computing challenges raised in the 2010 Grand 

Challenges report (BERAC 2010):

• In April 2013, ASCR organized a Data 

Crosscutting Requirements Review (ASCR 

2013a), which highlighted the fact that 

experimental facilities, especially those across 

BER and the DOE Offices of Basic Energy 

Sciences (BES) and High Energy Physics 

(HEP), are expected to see much more 

rapid growth in data volumes, velocity, and 

complexity than previously expected.

• A July 2013 ASCR workshop on Accelerating 

Scientific Knowledge Discovery, with BER, 

Data Analytics and Computing

Summary of Grand Challenges and Action Items

Data challenges in BER research programs have increased by orders of magnitude over the past few years. 

New techniques and services are required to leverage the wealth of research results and transform them into 

world-leading scientific discoveries. Although the data differ in format, common approaches can be employed 

among BER programs for archiving, accessing, processing, and generating enhanced data products.

Over the past decade, further progress has been made in developing archives, developing software to handle 

large amounts of program-specific data, and linking BER researchers with high-performance computing 

capabilities. However, the growth in data size, complexity, and heterogeneity has far outpaced these efforts, 

and the challenges are continually escalating with the development of new instruments and sensors that 

stream data in real time. As a result, BERAC has identified five grand challenges outlined here and then 

described in more detail in the Grand Challenge Research Recommendations section, p. 77:

6.1. Develop robust approaches for large-scale 

data collection, curation, annotation, and 

maintenance.

6.2. Develop computing and software infrastructure 

to enable large-scale data (i.e., Big Data) storage 

and analysis.

6.3. Conduct research to develop suitable 

algorithms and programming models 

that can harness current and future 

computer architectures to effectively model 

complex coupled systems and analyze 

extreme-scale data.

6.4. Engineer advanced computational modeling 

combined with data integration across 

temporal and spatial scales.

6.5. Conduct research and develop activities that 

support human understanding of large-scale, 

multimodal data streams, including the ability 

to steer experiments in real time.

Associated with these grand challenges are four 

action items recommended for initiating progress 

toward the challenges (see Discussion of Action 

Items section, p. 80, for more details):

• Prioritize building and maintaining an 

integrated facility for long-term data storage, 

archiving, and data analysis.

• Evolve current and develop new ontologies 

and metadata standards, as well as strengthen 

compliance in funding announcements and 

reporting standards.

• Facilitate and encourage increased interactions across disciplines (e.g., computer 

scientists, statisticians, data analysts, and 

experimentalists).

• Foster coordination and collaboration within 

DOE and among other agencies.
76 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

BES, and HEP participation, focused on the 

challenges of enabling effective scientific 

discovery in an era of extreme-scale data volume, 

velocity, and diversity. The resulting report 

(ASCR 2013b) supported research into scalable 

data storage, sharing, and fusion capabilities.

• In August 2015, BER’s Climate and Environmental Sciences Division (CESD) commissioned 

a workshop of its virtual data working group. The 

workshop organizers conducted a survey of the 

broader CESD research community’s computing 

requirements. The top two community needs 

identified by this survey are (1) an easy way to 

publish and archive data and (2) the linking and 

comparison of heterogeneous data types (U.S. 

DOE 2016).

• In September 2015, an ASCR workshop on the 

Management, Visualization, and Analysis of 

Experimental and Observational Data (ASCR 

2015), with participation from BER, BES, and 

HEP, put emerging community challenges into 

an even starker perspective:

– All experimental and observational 

science (EOS) projects represented at this 

workshop struggle to keep up with the 

demands and opportunities posed by the 

increasing data flood.

– Meeting the challenges of data explosion 

from EOS projects requires computational platforms, networking, and storage 

of greater capacity and lower latency, 

along with software infrastructure suited 

to project needs. However, existing 

HPC platforms and their software tools 

are designed and provisioned for highconcurrency, HPC workloads; singleproject data products; and comparatively 

simpler data needs. The result is a signif­icant gap between EOS project needs 

and the current state of the art in computational and software capabilities and 

resources.

• In March 2016, ASCR and BER organized an 

Exascale Requirements Review. The resulting 

report (ASCR 2016) again predominantly 

highlights the communities’ data needs for 

addressing data issues at extreme scales: 

scalable data processing, data representation 

and visualization, data transport, and more 

effective fusion of model simulations with 

observational data. At this workshop, representatives from BER, BES, HEP, and DOE’s 

Office of Nuclear Physics (NP) again highlighted their critical need for a large-scale data 

management and analysis capability (both 

in hardware and software) to meet their core 

requirements in pursuit of scientific discoveries.

• As mentioned previously, the 2017 international 

HFSP workshop and its resulting publication 

(Anderson et al. In review) also highlights the 

computational challenges faced by the scientific

community.

As this broad community engagement demonstrates, BER is not alone in its urgent need for more 

effective data management and analysis facilities 

and applications for extreme-scale data. BES, HEP, 

and NP face similar challenges, and there is great 

potential for addressing these needs jointly. The 

BER Virtual Laboratory: Innovative Framework for 

Biological and Environmental Grand Challenges report 

(BERAC 2013) clearly identified the need for a 

computational infrastructure to integrate disparate 

and multiscale measurements, theory, and process 

understanding into predictive models, ultimately 

creating new knowledge for developing energy and 

environmental solutions. This report also highlighted the need to build upon and integrate BER’s 

existing knowledge-discovery infrastructure, such 

as DOE’s Systems Biology Knowledgebase (KBase; 

kbase.us). In addition, the report recommended 

close collaboration with ASCR to discover, develop, 

and deploy computational and networking capabilities for analyzing, modeling, simulating, and predicting complex phenomena important to DOE. This 

request for a joint data management and analysis 
November 2017 Biological and Environmental Research Advisory Committee 77

Chapter 6 — Data Analytics and Computing

capability was renewed and strengthened during the 

2016 crosscutting ASCR-BER Exascale Requirements Review (ASCR 2016). Any such solution will 

certainly build on the experiences and capabilities 

developed by existing BER infrastructure services 

such as KBase and the Earth System Grid Federation (ESGF; esgf.llnl.gov).

Grand Challenge Research 

Recommendations

Grand Challenge 6.1 

Develop robust approaches for large-scale data collection, curation, annotation, and maintenance.

As noted previously, BERAC’s 2010 recommendation to establish a new data management paradigm 

for data-intensive science is not unique to BER or 

DOE, but rather it is experienced across disciplines 

and federal agencies. This challenge also has not 

been solved. Closely related to maintaining curated 

data annotations and augmenting any automated 

annotation (e.g., genome annotation) are (1) ongoing concern for data preservation (i.e., mirroring 

data to prevent accidental loss), (2) security (i.e., to 

prevent intentional or unintentional corruption of 

data), and (3) data provenance and curation (i.e., 

whereby updates and corrections to data collections are made in a transparent and traceable way to 

establish when and why datasets might be altered 

and corrected). NIH, which has a long history of 

working toward secure and robust data processes, 

is moving in this direction by actively working to 

establish a data commons (see Fig. 6.2. DataMed 

Ecosystem, p. 78).

Grand Challenge 6.2 

Develop computing and software infrastructure 

to enable large-scale data (i.e., Big Data) storage 

and analysis.

BER science is highly reliant on access to largescale computational resources currently provided 

by ASCR to carry out extreme-scale production 

runs, as well as midrange computing capabilities for 

model development and testing. Continued access 

to these resources remains essential for BER science.

BER science increasingly integrates processes 

across scales and disciplines, from molecules to the 

global-system level. One challenge is to integrate 

data throughout biological lifecycles and across 

scales, from experimental, observational, and computational sources, spanning domains as diverse as 

molecular biology, plants, and environmental systems that include land, water, and energy. Making 

this endeavor difficult is (1) distribution of data 

across many different heterogeneous archives and 

databases, (2) creation of data analysis systems 

that enable scientists and scientific communities 

to analyze large-scale data and integrate data collections, and (3) provision and ongoing maintenance and development of a suitable set of scalable 

data analysis tools. Scientists currently spend an 

inordinate amount of time on discovering, downloading, and integrating data for their analyses. 

Furthermore, they often cannot use all the available 

data because they lack the appropriate scalable 

analysis tools, a challenge that serves as the basis of 

this recommendation.

At a wide range of recent workshops, BER researchers expressed the need for an integrated data analysis 

infrastructure [including the 2017 BERAC Grand 

Challenges workshop and recent ASCR-BER Exascale Requirements Review (ASCR 2016)]. Such an 

infrastructure should enable easy and unified access 

to all data, along with the computational capacity 

and analytical tools to analyze those data.

When designing such a data warehousing and access 

interface, and the computing and software infrastructure to support it, much can be learned from 

several existing BER data infrastructure projects. 

BER’s KBase provides a single-analysis environment in which scientists can integrate, analyze, and 

share data. This system, still in relatively nascent 

form, provides advanced, scalable analytical tools 

that users can leverage for their work. However, the 

current implementation is limited in the variety of 
78 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

available data and overall scale of data that can be 

analyzed. In a similar vein, ESGF represents a worldwide network of data archives for climate modeling 

results, with a single search interface that is neutral 

to the physical location of data resources, and can 

deliver data from multiple sources to one target destination. However, ESGF does not provide access 

to data analysis facilities and tools and currently is 

focused only on climate data. Evaluating solutions 

from outside BER also would be useful. Examples 

include the Globus system (globus.org) used in 

KBase, ESGF, and many other national and international data projects. One of these projects, funded 

by HEP, ASCR, and the Large Hadron Collider, is 

the Production and Distributed Analysis (PanDA; 

news.pandawms.org) project, which manages data, 

Fig. 6.2. DataMed Ecosystem. Multiple stakeholders interact using components and tools that may or may not yet exist. 

[Modified with permission from Miller, K. 2016. “Data’s Identity Crisis: The Struggle to Name It, Describe It, Find It, and Publish 

It,” Biomedical Computation Review (Spring 2016), 22–27.]
November 2017 Biological and Environmental Research Advisory Committee 79

Chapter 6 — Data Analytics and Computing

application, and computing access for a worldwide 

community. Noteworthy is that PanDA helped to 

analyze 1.5 exabytes of data in 2016.

HPC, and data analysis infrastructure access even 

more so, is pervasive in today’s BER science. Therefore, scientific progress and new discoveries depend 

on community access not just to “adequate,” but to 

“state-of-the-art” computational and data resources.

Grand Challenge 6.3 

Conduct research to develop suitable algorithms 

and programming models that can harness current 

and future computer architectures to effectively 

model complex coupled systems and analyze 

extreme-scale data.

An important part of the infrastructure needed 

includes advanced analytics in support of knowledge discovery. Measurements of biological systems 

using technologies such as high-throughput molecular sequencing (Mardis 2008) and imaging (Kherlopian et al. 2008) routinely generate terabytes of data 

per experiment. Similarly, high-resolution climate 

modeling affords spatial resolutions down to the 

25-kilometer level while generating hundreds of 

terabytes of data (Wehner et al. 2014). Collectively, 

raw data generated by biological and Earth system 

studies in public databases are quickly approaching 

exabyte (1018) scales. Integrated analyses of the various data types will afford deeper understanding of 

system processes and enable knowledge discovery, 

but they also will require the development and use 

of advanced algorithms and machine-learning techniques to process the data scale and noise inherent 

in these measurements.

The large data volumes being generated necessitate 

the development of indexes and data structures 

to enable efficient querying of these datasets. The 

computer science community has developed relevant approaches for indexing large corpora of text 

(Navarro and Mäkinen 2007). Connecting with the 

computer science community is vital to both learn 

from the disciplinary advances of its members and 

engage them in biological research design, so that 

each study design is well defined in the statistical, 

numerical, and computational requirements necessary for experimental rigor and success.

Biological and Earth systems datasets are often 

multidimensional (e.g., corresponding to genes, 

genomes, metabolites, organisms, and climate 

model grids). The quantity of the variable definitions alone (i.e., even before data collection) can 

be daunting, with hundreds or even thousands of 

variables to measure. However, not all variables 

may be of equal importance when constructing models of the system being studied. Thus, 

advanced and automated analytic efforts should 

include the development of methods for data 

reduction and variable feature selection (Guyon 

and Elisseeff 2003) to identify important characteristic dataset features.

Crucial to the accurate study of these systems is the 

availability of high-quality annotations. In biological datasets, for example, these annotations include 

those for gene and protein function. The approach 

to this challenge must couple sensitive and accurate 

computational methods for homology detection 

with novel, high-throughput experimental methods 

for characterizing gene function.

Taken together, advanced analytic approaches 

remain challenging but are necessary for developing improved predictive and more computationally efficient models at all levels of the system 

being studied.

Grand Challege 6.4 

Engineer advanced computational modeling combined with data integration across temporal and 

spatial scales.

Advances in the various omics technologies have 

enabled increasingly expansive studies of biological 

systems, including in-depth sampling of constituent 

genomes, transcripts, proteins, and metabolites. 

Together with the environmental and phenotype 

metadata collected during the experiments, these 
80 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

omics data types open the door to building multiscale models of the biological system, ranging 

from gene interaction networks in an organism to 

multiorganism organization in a community with 

environmental interactions (see also Chapter 2 

on Biological Systems Science, p. 3). Progress has 

been made on modeling higher-order interactions 

between organisms in microbial communities 

using techniques like metabolic flux analysis (Harcombe et al. 2014; Latendresse et al. 2012; Orth 

et al. 2010), but a substantial gap in continuity still 

exists between microbial system models and Earth 

system models, including climate prediction (see 

also Chapter 3 on Earth and Environmental Systems Sciences and Chapter 4 on Microbial to Earth 

System Pathways, p. 21 and p. 43, respectively). Relevant to bridging this gap is the need for novel computational techniques, modeling frameworks, and 

workflows that can effectively handle large volumes 

of heterogeneous data types.

Grand Challenge 6.5 

Conduct research and develop activities that support human understanding of large-scale, multimodal data streams, including the ability to steer 

experiments in real time.

These activities include visualization of streaming 

data from various sources and the ability to capture 

snapshots of experiments in progress, as well as 

change the direction of these experiments based on 

immediately available data visible to the researcher 

or to intelligent algorithms (built to recognize when 

parameters need to be changed). These capabilities 

are vital to accelerate progress and reduce wasteful data collection, giving scientists the ability to 

rapidly tweak experiments while under way, rather 

than wasting resources to complete and then reset 

the experiment from scratch. This new paradigm is 

actively being explored through initiatives such as 

AIM at PNNL (see Fig. 6.1, p. 73).

Discussion of Action Items

Prioritize Building and Maintaining an 

Integrated Facility for Long-Term Data 

Storage, Archiving, and Data Analysis

A new priority should be established, along with the 

necessary long-term support, to build and maintain 

an integrated facility for long-term data storage 

and archival of analytic results. This facility should 

be secure, mirrored to prevent accidental loss, and 

usable across current platforms, as well as extensible 

to technology advances over time.

Evolve Current and Develop New 

Ontologies and Metadata Standards, as 

Well as Strengthen Compliance in Funding 

Announcements and Reporting Standards

A recurring topic at the 2017 BERAC Grand Challenges workshop was the need to make data at all 

levels available in one common thematic database, 

from which community users can access the data 

and to which they can apply their own, or standard, 

analysis techniques. For example, (1) all climate and 

climate modeling data should be made accessible in 

one database (i.e., perhaps similar to KBase), (2) all 

biological data should be accessible in one database, and (3) linkages between the data resources 

should be clear and readily accessible. In geosciences 

research, the EarthCube project (earthcube.org; Gil 

et al. 2014) consists of a diverse set of scientists to 

“influence” data collection, analysis, and sharing. The 

Materials Data Facility (materialsdatafacility.org; 

Blaiszik et al. 2016) integrates data across dozens 

of materials datasets, enabling researchers to find, 

access, and integrate various types of data from different locations. To make data accessible in common 

thematic databases, the ontologies (see sidebar, 

What Are Ontologies? p. 81) associated with each 

theme must be fully developed and compliance 

enforced. In the genomics realm, the Genomic 

Standards Consortium (gensc.org) establishes 

international standards to enable the development 

of genomic databases, which, in turn, support comparative whole-genomics analyses. In the Earth sciences area, Climate and Forecast (CF) conventions 
November 2017 Biological and Environmental Research Advisory Committee 81

Chapter 6 — Data Analytics and Computing

and metadata (cfconventions.org) are developing 

metadata standards and best practices. To properly 

integrate and compare data across biology, climate, 

and environmental systems, each system must have 

established and enforced ontologies. Workshop participants unanimously expressed the need for developing standards and ontologies at all BER levels 

(i.e., climate, environmental systems, and biology), 

as well as developing an atmosphere that supports 

and encourages compliance with these standards 

and ontologies.

Current efforts include the (1) Coupled Model 

Intercomparison Project (CMIP; World Climate 

Research Programme; cmip-pcmdi.llnl.gov), a 

widely used system to provide standard experimental 

and validation protocols to study Atmosphere-Ocean 

General Circulation Models (AOGCMs), as well as 

documentation and data access; (2) Observations 

for Model Intercomparison Projects (Obs4MIPs; 

www.earthsystemcog.org/projects/obs4mips/), a 

repository of well-documented datasets organized by 

the CMIP5 model output requirements to facilitate 

model data comparisons; and (3) KBase (kbase.us), 

a data and software platform for predicting and 

designing biological function.

As stated in the 2010 Grand Challenges report 

(BERAC 2010): “Initial focus for standards development should be on the largest and most significant experimental results having the greatest impact 

on the research community.” The same is true for 

current needs; without the development of strict 

ontologies and compliance in all components of 

BER data (i.e., microbiome, systems biology, and 

environmental systems), research teams will be 

unable to integrate, analyze, compare, or break down 

barriers between and across systems. Additionally, 

metadata and laboratory methods must be clearly 

documented and available for any publicly deposited 

experimental data. Without such documentation, 

consistency within and across laboratories will not 

be attainable, thus creating insurmountable barricades in integrating multiscalar data across systems.

Facilitate and Encourage Increased 

Interactions Across Disciplines 

(e.g., Computer Scientists, Statisticians, 

Data Analysts, and Experimentalists)

Intra- and interagency collaborations will leverage 

efforts of ontology development, data deposition, 

and data integration for specific, targeted system levels, thereby facilitating data exchange and comparisons across these different systems. For example, the 

National Microbiome Initiative, which was launched 

in 2016, is a collaborative effort among the White 

House Office of Science and Technology Policy 

(OSTP), several federal agencies, and private-sector 

stakeholders to support the study of microbiomes 

across different ecosystems (OSTP 2016). Among 

the primary goals are the facilitation of interdisciplinary research and development of standard technologies, both of which are completely in line with 

BERAC’s recommendations. Similarly, much can be 

learned from the KBase initiative for biology; this 

system likely also could be applied to climate and 

What Are Ontologies?

Ontologies are formal definitions and names of 

entities that exist in a domain or field of study. 

Additionally, ontologies are standard definitions 

that are accepted across the domain’s community and enable clear, precise sharing of data 

and results within and across domains. A good 

introduction can be found in the “Ontology 

Development 101” guide from the protégé project at Stanford University (Noy and McGuinness 

2001). Examples of existing ontologies that have 

been widely adopted are (1) Minimal Information About a Microarray Experiment (MIAME; 

Functional Genomics Data Society; fged.org/

projects/miame/); (2) Minimum Information 

About a Proteomics Experiment (MIAPE; Proteomics Standards Initiative; psidev.info); (3) Gene 

Ontology Consortium (geneontology.org); (4) Climate Ontology (National Aeronautics and Space 

Administration; cds.nccs.nasa.gov/tools-services/

ontology/); and (5) Climate and Forecast ontology (W3C Semantic Sensor Network Incubator 

Group; www.w3.org/2005/Incubator/ssn/ssnx/cf/

cf-property).
82 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

environmental systems, as well as enable easier data 

exchange, leveraging that was called for at the 2017 

BERAC Grand Challenges workshop.

The integration of specific databases developed 

at other federal agencies [e.g., the Sequence Read 

Archive at NIH’s National Center for Biotechnology Information (NCBI), or the National Science 

Foundation’s (NSF) Biological Databases Initiative] 

would greatly facilitate integration and comparisons 

of multiscalar and cross-systems data. Current effort 

and resources could be reduced notably by more 

deliberate efforts to coordinate and collaborate 

among agencies. To conquer the challenge of public 

data collection ranging from the Earth sciences to 

microbiome levels, data and tool exchange must be 

made continuous and seamless across researchers, 

irrespective of association to federal agency or other 

funding source. In other words, an agency-agnostic 

knowledge discovery effort should be implemented.

Such opportunities include, for example:

• NSF’s Advances in Biological Informatics 

program (www.nsf.gov/funding/pgm_summ.

jsp?pims_id=5444), which focuses on the 

development of new informatics tools for 

biological data and dissemination of biological 

knowledge. This program actively supports 

whole-omics annotation efforts, wholeomics informatics tool development, and 

microbiome research.

• NSF’s iPlant collaborative (now called CyVerse; 

cyverse.org), which is a community-driven 

infrastructure to study ecosystems and their 

interactions, providing HPC services, cloud 

computing, data storage, and an interactive 

analytical platform for research in the 

life sciences.

• Globus research data management services 

(globus.org), which are deployed extensively 

across DOE, NSF, NIH, and other centers, to 

enable secure, reliable, high-speed access to data 

regardless of location and also to support data 

sharing, publication, and discovery.

• NSF’s Directorate for Biological Sciences, 

which offers a host of programs to support 

microbial research (e.g., Center for Microbial 

Oceanography Research and Education), as well 

as the National Institute for Mathematical and 

Biological Synthesis (NIMBioS). NIMBioS is 

especially important as its focus is to bridge the 

mathematics-biology interface through broader 

impacts and training.

• NIH’s omics annotation guides, such as the 

Eukaryotic Genome Annotation Pipeline 

(www.ncbi.nlm.nih.gov/books/NBK169439/), 

which are widely accessible. Most standard 

whole-omics databases are housed at NCBI and 

are widely accessible.

Foster Coordination and Collaboration 

Within DOE and Among Other Agencies

New experimental technologies are continuously 

being developed, enabling a wealth of new scientific 

discoveries by probing deeper, with more detail, 

into scientific processes. These new experimental 

facilities, from light source–based instruments to 

full-biome observations, will be increasingly complex in their makeup and operation and are poised 

to create extremely large volumes of data (see also 

Chapter 7 on User Facilities and Research Infrastructure, p. 85). To fully leverage their scientific 

potential, instruments and experiments will need 

to be designed with foresight. Scientists must be 

able to steer these complex experiments to optimize 

their scientific outcome, based on rigorously analyzed results, while the data are still being collected 

through a continual cycle of streaming analysis 

and steering. Sometimes researchers are not able 

to orchestrate every step of the experiment due to 

its speed (i.e., high throughput) or duration (e.g., 

multiyear biome observations). At these points, 

autonomous, optimized experimental steering in 

software may need to take over, while still being 

guided by the experiment’s scientific objectives and 

investigator oversight. Furthermore, many sensors 

often are available for use during experiments or 

observations; however, during the experimental 
November 2017 Biological and Environmental Research Advisory Committee 83

Chapter 6 — Data Analytics and Computing

design process, ensuring that statistically relevant 

measurements are obtained is important, so that 

findings reflect a robust and rigorous scientific 

discovery process. Currently, however, instrument 

and experiment design, as well as experimental data 

analyses, are treated as distinct and separate activities, often with no overlap in personnel, thus leading 

to many deficiencies in the experimental process.

An initial workshop would be beneficial to provide 

an overview of experimental techniques, analytical 

methods, and experimental planning capabilities, while also providing opportunities for BER 

researchers, experimentalists, computer scientists, 

and applied mathematicians and statisticians to 

share their experiences of sensor selection planning 

and outcomes, including lessons learned. Further 

workshops could build on this initial overview, 

focusing on the design and operation of specific 

large-scale experiments and providing an opportunity to create an integrated instrument and experimental design process.

Such mechanisms foster stronger multidisciplinary 

teams, which, in turn, can design better experiments, leading to more rapid discoveries while 

simultaneously making more efficient use of existing 

instruments. Over time, the hope is that not only 

large-scale and complex experiments, but all experiments, will be more optimally designed to support 

scientific discoveries.
84 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision
November 2017 Biological and Environmental Research Advisory Committee 85

Chapter 7 — User Facilities and Research Infrastructure

Providing Resources for BER Science

The fundamental biological, Earth, and environmental systems science supported by the 

U.S. Department of Energy’s (DOE) Office 

of Biological and Environmental Research (BER) is 

enabled by user facilities and research infrastructure. 

Across the DOE complex, national user facilities 

foster a more interdisciplinary research approach 

by providing a wide range of technologies, methodologies, and instruments critically important for 

accelerating the science necessary for BER missions 

in energy and environment. These capabilities are 

complex and expensive, requiring dedicated staff 

for technology maintenance, operation, and broad 

dissemination to individual researchers and institutions. The constantly evolving user facilities will continue to enable scientific breakthroughs in genomics, 

imaging, structural characterization of organisms 

and molecules, and atmospheric measurements only 

if their technologies remain at the scientific forefront. Improving existing technologies is therefore 

essential, as is advancing newly developed computational, imaging, remote-sensing, and other characterization capabilities deployed in existing or proposed 

user facilities (see User Facilities and Research 

Infrastructure Summary of Key Recommendations, 

p. 86). These resources will underpin critical experiments and generate the data needed to understand 

important biological and environmental processes.

User Facilities for Genome Sequencing 

and Molecular and Atmospheric Sciences

BER is the primary sponsor of three DOE scientific user facilities: Joint Genome Institute ( JGI), 

User Facilities and 7 Research Infrastructure

Environmental Molecular Sciences Laboratory 

(EMSL), and Atmospheric Radiation Measurement (ARM) Research Facility. JGI (jgi.doe.gov) 

was founded in 1998 as a high-throughput genome 

sequencing center tasked with completing the three 

chromosomes allotted to DOE for the Human 

Genome Project. After becoming a user facility in 

2004, the center opened its unique sequencing capabilities to the energy and environmental research 

communities. EMSL (www.emsl.pnl.gov), founded 

in 1997, brings together theoreticians experienced in 

modeling molecular processes with researchers from 

the physical and life sciences. Applying advanced 

experimental and computational resources, they 

undertake fundamental research on physical, 

chemical, and biological processes that underpin 

environmental and energy challenges facing DOE 

and the nation. The multiplatform ARM Research 

Facility (www.arm.gov) was founded in 1990 to 

provide detailed, accurate descriptions of Earth’s 

atmosphere in diverse climate regimes—knowledge 

needed to resolve uncertainties in climate and Earth 

system models (ESMs). The ARM Research Facility 

comprises a network of fixed-location observation 

sites and mobile observation facilities extensively 

equipped with instruments to measure atmospheric 

radiation, aerosols, clouds, other atmospheric properties, and surface fluxes of energy and water. The 

ARM Facility has a long history of data collection, 

processing, management, and distribution. In 2017, 

its archive surpassed 1 petabyte (PB) of data, and 

the facility recently expanded its computational 

capabilities to enable the coupling of high-resolution 

models with ARM observational data.
86 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Large-Scale Ecosystem Observations, 

Experiments, and Modeling

In addition to user facilities, BER supports research, 

computing, and data infrastructures. Large-scale 

field research projects include the Spruce and 

Peatland Responses Under Changing Environments (SPRUCE) study (mnspruce.ornl.gov); 

Next-Generation Ecosystem Experiments (NGEE) 

in the Arctic (ngee-arctic.ornl.gov) and tropics 

(ngee-tropics.lbl.gov); and the AmeriFlux Network 

(ameriflux.lbl.gov) for measuring carbon, water, and 

energy fluxes. The SPRUCE experiment assesses 

User Facilities and Research Infrastructure

Summary of Key Recommendations

Eight key recommendations for enhancing BER facilities and infrastructure over the next 20 years have 

emerged based on an assessment of the developments in these areas since 2010 and discussions 

about maintaining and advancing needed facility and infrastructure capabilities into the future. These 

recommendations are combined here with action items from other chapters that may be appropriate for 

inclusion and integration with user facilities or research infrastructure.

7.1. Foster a spirit of collaboration to enable 

integrative capabilities among BER and Office 

of Science user facilities, as well as other federal 

research facilities and infrastructure, thereby 

promoting a fully interdisciplinary approach to 

BER-relevant science.

7.2. Solicit input from the BER research community 

regarding researchers’ needs and train them 

in new experimental, observational, and 

modeling approaches, thus propagating the 

knowledge and skills for generating high-impact 

scientific results.

7.3. Develop innovative enabling technologies 

and construct and acquire state-of-the-art 

instruments that exploit the world-leading 

characteristics of each user facility. This will 

boost capabilities for basic research in biological 

systems and Earth and environmental systems 

science, thereby providing DOE and the nation 

with leading-edge capabilities for biological and 

environmental science.

7.4. Develop multimodal imaging and remotesensing capabilities at user facilities for interrogating length scales ranging from atomic 

to mesoscale and time scales ranging from 

nanoseconds to days.

7.5. Build upon existing investments and capabilities 

at the DOE Office of Science light and neutron 

science user facilities, continuing to align them 

with BER missions.

7.6. Further develop the necessary infrastructure 

at user facilities to study organisms in their 

natural habitats.

7.7. Develop and adopt technologies to convert 

genome sequence data into functional 

understanding at appropriate BER user facilities.

ecological responses to environmental change across 

multiple spatial scales, including microbial communities, moss populations, various higher-plant types, 

and some insect groups. SPRUCE provides stateof-the-art infrastructure to manipulate atmospheric 

carbon dioxide levels and air and belowground 

temperatures for evaluating critical ecosystem and 

biological processes and properties. NGEE projects 

seek to improve the representation of critical environmental processes in ESMs by studying systems 

that are globally important, climatically sensitive, 

and understudied or inadequately represented in 

models. NGEE–Arctic research examines how 
November 2017 Biological and Environmental Research Advisory Committee 87

Chapter 7 — User Facilities and Research Infrastructure

surface and subsurface processes and properties 

are interconnected across permafrost-dominated 

tundra ecosystems, while NGEE–Tropics activities 

focus on predicting the future of the tropical forest 

carbon sink and associated energy and water fluxes. 

The AmeriFlux Network research platform spans 

the full spectrum of ecosystems and climates, from 

Amazonian rainforests to Alaska’s North Slope. 

The scientific community uses AmeriFlux measurements to assess responses and feedbacks of 

terrestrial ecosystems to the environment, including 

changes in climate, land use, and extreme events 

such as droughts, storms, or wildfire. In 2012, DOE 

established the AmeriFlux Management Project to 

support the broad AmeriFlux community and the 

AmeriFlux sites.

Other DOE User Facilities 

Enabling BER Research

The BER research portfolio encompasses a wide 

range of biological and environmental systems 

whose cellular ultrastructure and physiology, bioprocesses and bioproducts, and environmental 

and atmospheric transformations demand understanding. These systems function on femtosecond 

to month time scales and on Ångström to global 

length scales. Bridging gaps between these scales is 

one of the greatest challenges in BER research. To 

understand complex biological and environmental systems across scales, researchers can leverage 

world-class light and neutron sources and the 

Nanoscale Science Research Centers (NSRCs), all 

constructed and operated by DOE’s Office of Basic 

Energy Sciences (BES). Supporting investigations 

into the basic structure of matter, BES facilities 

include the Advanced Light Source at Lawrence 

Berkeley National Laboratory, Advanced Photon 

Source at Argonne National Laboratory (ANL), 

National Synchrotron Light Source II (NSLS-II) 

at Brookhaven National Laboratory, Stanford Synchrotron Radiation Lightsource at SLAC National 

Accelerator Laboratory (SLAC), and Spallation 

Neutron Source (SNS) and High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. The 

research capabilities offered by these state-of-the-art 

national user facilities can be brought to bear in 

many areas of science and technology, including the 

biological and environmental sciences. For example, BER and the National Institutes of Health have 

made long-term, coordinated investments in constructing and operating beamlines and instruments 

for biology applications at the light and neutron 

sources. This collaborative support has led to significant scientific breakthroughs, discoveries, and 

growth in broad areas of the biosciences. Now is a 

well-timed and excellent opportunity to advance 

BER research by expanding existing DOE investments, aligning BER light and neutron source capabilities, and developing and integrating NSRC and 

electron source capabilities for biological and environmental research. This approach would enrich and 

expand BER-funded programs already supported at 

the facilities and open up new science possibilities 

relevant to BER’s mission.

Data and Computational Capabilities

Data produced by facilities and research projects 

are not particularly useful without capabilities for 

data management, analysis, and computation. BER 

researchers have dedicated access to advanced computational resources through EMSL’s Molecular Science Computing capabilities and the DOE Office of 

Advanced Scientific Computing Research’s (ASCR) 

National Energy Research Scientific Computing 

Center (NERSC; www.nersc.gov). Not only does 

NERSC provide critical storage space for archiving 

BER data, it supplies the high-performance computing capabilities essential for the complex, highvolume calculations required to process and analyze 

the wealth of data emanating from BER user facilities. Further enabling practical use of the data is 

the DOE Systems Biology Knowledgebase (KBase; 

kbase.us), an openly available informatics resource 

for collaborative computational analysis and modeling of plants, microbes, and their communities.

KBase integrates data and tools in a unified graphical 

interface providing users easy access to sophisticated systems biology workflows. Another BER data 

asset is the Earth System Grid Federation (ESGF), 

one of the largest-ever collaborative data efforts in 
88 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Earth system science (esgf.llnl.gov). Led by BER, 

this international multiagency federation develops, deploys, and maintains software to facilitate 

advancements in geophysical science. ESGF disseminates model simulation, observational, and reanalysis data for research assessments and climate model 

validation. Currently serving more than 25,000 

users, the ESGF archive supports 700,000 datasets 

from laboratories and universities around the world 

and contains over 5 PB of Earth system science data 

from more than 25 research projects and 70 model 

intercomparison projects (see also Chapter 6 on 

Data Analytics and Computing, p. 71).

Building on Progress Related 

to the 2010 Grand Challenges

The key role of user facilities in BER science over the 

years is expected to continue in the coming decades 

as facilities adapt to the user community’s changing needs. Although BER-supported user facilities 

have diverse technologies, science drivers, and user 

communities, they share the same overarching goal 

of providing access to unique technologies that are 

otherwise out of reach for individual investigators. 

Ideally, researchers can come to the user facilities 

with a question rather than just a technique request, 

relying on the scientific expertise of facility staff to 

navigate the technological options. In addition to 

traditional facilities where users physically conduct 

experiments, increasingly common virtual facilities 

are transforming how users interact with instrumentation and resources by enabling scientists to work 

remotely. Already common for some capabilities, 

this model potentially could expand to other areas of 

biological and environmental research.

Expanded EMSL’s Suite of Tools for Molecular to Mesoscale Investigations. Since the 2010 

Grand Challenges report (BERAC 2010), molecular science facilities have added a number of new 

capabilities and resources. EMSL, for example, 

has significantly evolved with the needs of the scientific community, providing a wide range of new 

molecular to mesoscale experimental capabilities 

that push the frontiers of instrument resolution and 

sensitivity. Users can leverage these capabilities to 

investigate biological and environmental processes 

under dynamic and native conditions. Additional 

advancements in EMSL’s computational hardware, 

calibrated codes, and data analytics and storage 

capabilities enable modeling of biological and environmental processes both at discrete scales and 

at the systems level across multiple temporal and 

spatial scales. Also possible now are more comprehensive characterizations of proteins, metabolites, 

and other biomolecules from natural and engineered 

biological systems using new mass spectrometry and 

microfluidic approaches. These technologies include 

a premier 21 Tesla Fourier transform ion cyclotron 

resonance mass spectrometer with unmatched resolution for identifying and quantifying molecular 

compounds from cells and sample mixtures such as 

soils, biofluids, and aerosols. EMSL also has developed or acquired unique capabilities for dynamic 

multimodal imaging of cells and environmental 

materials with submicron to subnanometer resolution using fluorescence, electron, cryo-electron, and 

ion-based imaging technologies. EMSL users are 

leveraging molecular ecology tools, including stable 

isotope probing, to study soil diagenesis and metabolism. Facility capabilities for data and image analytics enable users to identify and interpret chemical 

signals within and among cells and to study environmental interactions among microbes, microbial 

communities, plant roots, soil minerals, and aerosol 

precursor molecules. Together, these new technology platforms enable EMSL scientists and users to 

test new hypotheses about the functional responses 

of natural systems and to examine the details of 

those interactions with increasing specificity.

Increased JGI’s Sequencing Output and Range 

of Analysis Tools. Since 2010, JGI has increased 

its sequencing capacity by more than an order of 

magnitude, offering a greatly expanded range of 

capabilities for functional genomics, epigenomics, 

and metabolomics in concert with more sensitive 

and accurate methods for de novo shotgun sequencing. These methods have dramatically improved 

knowledge of genome evolution, stress responses, 

and interactions among plants, fungi, microbes, and 
November 2017 Biological and Environmental Research Advisory Committee 89

Chapter 7 — User Facilities and Research Infrastructure

microbial communities. Added as a core capability, 

DNA synthesis is enabling major scientific breakthroughs in molecular science with implications for 

bioenergy production and nutrient cycling. When 

combined with existing metabolomic, genetic, and 

functional characterization, these capabilities will 

help scientists understand biological systems on the 

functional level in their native environments.

Launched New ARM Mobile Facilities and Sites 

While Expanding Radar, Modeling, and Other 

ARM Capabilities. As part of its significantly 

increased capabilities, the ARM Research Facility 

opened (1) a new observational site in the Azores 

with a focus on marine clouds and aerosol-cloud 

interactions; (2) a marine-deployable mobile facility; and (3) a third mobile facility currently studying 

high-latitude aerosol and cloud processes. ARM 

researchers have made important progress in understanding the complex micro- and macrophysical 

processes controlling mixed-phase Arctic clouds. 

Advances in radar technology at the ARM Facility 

also have enabled unprecedented three-dimensional 

(3D) views of cloud and precipitation morphology, 

updraft dynamics, and microphysical properties. 

Scanning lidar technology (i.e., light detection and 

ranging), combined with other ARM sensors, has 

provided in-depth knowledge of the turbulent structure of the atmospheric boundary layer. Advanced 

aerosol observations, coupled with laboratory 

studies, have produced fundamental new insights 

into formation of new aerosol particles and the formation and evolution of secondary organic aerosol 

in the atmosphere. ARM observations also have 

been central to several new radiative transfer, aerosol, 

and cloud parameterizations developed for ESMs, 

and the facility recently developed capabilities in 

large eddy simulation modeling to better couple 

ARM observations with high-resolution modeling.

Leveraged Advanced Technologies at DOE Light 

and Neutron Sources for Unprecedented Characterization of Biological Systems. In addition to 

advancements in BER user facilities, new technologies 

and capabilities at BES-funded light and neutron 

sources are enabling deeper characterization of plants, 

microbes, and their environments. Very small beams 

at these user facilities enable in situ and even in cellulo

data collection. In many cases, researchers can access 

a facility’s experimental resources for structural biology remotely, leveraging the best instruments from 

their own laboratories. The spatial and temporal resolutions available from photon and neutron beams 

enable unprecedented characterization and imaging 

of plant-microbe-environment interactions at scales 

ranging from subnanometers to millimeters in length 

and from femtoseconds to seconds in time. This femtosecond resolution is made possible by extremely 

bright X-ray free-electron laser (XFEL) sources that 

are empowering new and dynamic structural biology 

experiments on the ultrafast time scale. The world’s 

first XFEL, the Linac Coherent Light Source (LCLS) 

at SLAC, has created a new paradigm for imaging biological samples by developing “diffract-before-destroy” 

methods for sample probing. Efforts are under way to 

align these and other technologies at the light and neutron resources and NSRCs as a part of an integrated 

capabilities portfolio for BER missions.

Applying User Facilities 

to BER Grand Challenges

There are opportunities for continuing and further 

expanding the important role of user facilities and 

research infrastructure in supporting advanced BER 

research, particularly the grand challenges identified in this report (see Table 7.1. Capability Assesment of DOE Office of Science User Facilities and 

Research Infrastructure for Addressing BER Grand 

Challenges and Key Recommendations, p. 90). 

Although existing facilities and infrastructure are 

well positioned to address research needs in some 

areas (e.g., JGI for grand challenges in biological systems), needed capabilities are lacking in others (e.g., 

resources to conduct energy sustainability research). 

In some cases, an expansion or refocusing of existing 

technical experience is necessary for addressing new 

research issues (e.g., leveraging KBase experience 

for data analytics and computing grand challenges). 

Working across DOE Office of Science user facilities, as well as community research infrastructure 
90 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Table 7.1. Capability Assessment of DOE Office of Science User Facilities and 

Research Infrastructure for Addressing BER Grand Challenges and Key Recommendations*

* Refer to text in respective chapters for descriptions of individual grand challenges or key recommendations. Existing capabilities and partial capabilities that could be leveraged for the grand challenges and key recommendations are denoted by

l and m, respectively.
November 2017 Biological and Environmental Research Advisory Committee 91

Chapter 7 — User Facilities and Research Infrastructure

and programs, will help strengthen and further 

develop advanced science capabilities within BER.

Maintaining and Advancing Facility 

and Infrastructure Capabilities

Collaborative Research Among Scientists 

and Facilities

The BER research community studies increasingly 

diverse, complex, and challenging biological and 

Earth and environmental systems science problems 

whose solutions require a spirit of collaboration 

among both researchers and facilities and a combination of cutting-edge capabilities leading to 

impactful discoveries. Collaborations are important 

to enabling integrative technologies that underpin 

a fully interdisciplinary approach to BER-relevant 

science at user facilities. Scientists accustomed to 

working as individual principal investigators (PIs) 

may need incentives to build interdisciplinary collaborations and access a range of capabilities to 

pursue their research questions. The BER Scientific 

Focus Areas (SFAs) offer one model of effective 

interaction among researchers.

Achieving these objectives also requires a mechanism allowing scientists to submit a single proposal 

for access to all relevant capabilities at BER-funded 

facilities. This process has been started by the Facilities Integrating Collaborations for User Science 

(FICUS) program, which aims to encourage and 

enable scientists to more easily incorporate into 

their research the expertise and capabilities of multiple DOE user facilities. As part of FICUS, user 

facilities collaboratively develop calls for proposals 

and coordinate their reviews. So far, JGI and EMSL 

have issued a joint call, as have EMSL and ARM, 

and JGI and NERSC. Similar ongoing collaborative 

initiatives among BES facilities aim to deliver innovative science while expanding the reach and impact 

of the user facilities. These initiatives include pilot 

partnerships among SNS, HFIR, and NSLS-II and 

among JGI, EMSL, and ANL. Another collaborative opportunity to consider is a pilot program that 

integrates capabilities from JGI, EMSL, light and 

neutron sources, and NSRCs.

In July 2016, JGI sponsored a FICUS workshop 

where participants proposed establishing a core 

steering committee to coordinate facility access 

efforts. Suggestions for the committee’s charge and 

configuration include:

• Frequent communication (at least monthly) 

via telecom.

• One representative member from each facility.

• Additional scientists to act as “points of contact” 

for each technique.

• Creation of a BER science user group to provide 

advice to the steering committee.

• An outreach process connecting FICUS with 

major BER programs, such as new Bioenergy 

Research Centers and SFAs.

• Formulation of steps for developing a web 

portal to provide information on BER user 

facility capabilities and access to them.

Training and Outreach

Expanding and educating the BER research community are important for advancing BER science. 

These efforts start with effectively disseminating 

information about the capabilities and methodologies available at user facilities. Each facility provides 

descriptions of its capabilities on its website, which 

is linked to DOE’s site. However, a common web 

portal with coordinated information about capabilities and user access across the facilities could 

help scientists navigate the options. Such efforts 

are beginning to take shape with the recent launch 

of a web portal describing all BER-supported techniques and facilities for structural biology research 

ranging from the atomic to cellular scales (www.

berstructuralbioportal.org).

Reaching out to members of the BER research 

community, especially BER Early Career Research 
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Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Program grantees, to evaluate their needs and train 

them in new experimental approaches could pave 

the way to high-impact scientific results. Workshops 

and hands-on training would be valuable in this 

regard, particularly if scheduled in conjunction with 

BER PI meetings and national conferences focusing 

on BER-related science and technologies. Facility 

user meetings should not be overlooked as opportunities to strengthen ties with existing users, engage 

prospective new users, and highlight BER-relevant 

research. Leveraging recent developments in remote 

access and automation could prove useful for training purposes, as well.

The DOE Office of Science Graduate Fellowship 

program is underused as a means to facilitate 

cross-technique and cross-facility training. Postdoctoral and visiting fellow programs also could enable 

PIs to take advantage of unique facility resources 

and develop expertise for use in their own laboratories, helping to create a self-sustaining user community in BER-related research.

New Methods, Complementary 

Technologies, and Instruments

Key to future impact and innovation are continued 

development and pursuit of technologies that build 

on unique user facility resources. Maintaining high 

standards for staff expertise in technology development and user support will be critical as well. 

User facilities will boost capabilities for basic biological and environmental research, thus providing 

leading-edge science for DOE in these areas.

Other important frontiers to pursue are the development of new instruments enabling multimodal 

imaging capabilities over length scales from subnanometers, millimeters, and beyond and the 

integration of existing complementary techniques. 

The research community can further expand and 

optimize new LCLS resources by developing 

and coupling light source technologies that feed 

into XFEL resources and vice versa. Also needed 

are research and development efforts and capital 

investment in characterization capabilities for 

single-particle cryo-electron microscopy (cryoEM) 

and cryo-electron tomography (cryoET). These 

techniques, which can bridge the gap between 

atomic and cellular scales, potentially could revolutionize future protein structural biology and 

cellular imaging but require significant capital and 

human investment out of reach for many research 

institutions. BER researchers need access to 

cryoEM user facilities for structural characterization of plant and microbial systems. Co-locating 

cryoEM and cryoET facilities at neutron and light 

sources or NSRCs would be practical, capitalizing 

on established open-access user programs and 

the expertise in operating advanced instrumentation. Additional opportunities exist to leverage 

and enhance BER-supported facility capabilities 

through interactions with the NSRCs in imaging 

and nanofabrication (see also Chapter 8 on Emerging Technologies, p. 95).

In addition to cutting-edge imaging techniques, 

advanced sequencing technologies and approaches 

are needed. Such capabilities will be critical to 

improving genome data quality, gene annotations, 

and protein structural and functional predictions, 

ultimately enabling the use of sequence data for 

functional understanding. As with sequencing technologies, metabolomic and proteomic instrumentation is evolving rapidly, requiring frequent updating 

to achieve the high spatial resolution goals laid out 

in this report. Although still nascent, deployable 

technologies for these applications are potentially 

transformative. 

A central task for BER is to provide research opportunities and scientific user facilities that advance the 

understanding of complex biological, Earth, and 

environmental systems important to DOE missions. 

User facilities provide a wide range of capabilities 

and technologies that drive research in these fields. 

Computing resources play a critical role in supporting these facilities and addressing the complexity 

inherent in BER systems of interest. New capabilities 

are needed, along with increased coordination and 

integration among user facilities, to fully examine the 

different spatial and temporal characteristics of these 

systems, model them at larger scales, and understand 
November 2017 Biological and Environmental Research Advisory Committee 93

Chapter 7 — User Facilities and Research Infrastructure

their interactions within and across spatial and temporal scales. This is captured particularly well by the 

proposed testbeds and synthesis center for energy 

sustainability research (see Discussion of Action 

Items section in Chapter 5, p. 67).

Computing and Data Management 

and Analysis

User facilities are supported by revolutionary changes 

in advanced computational and artificial intelligence 

approaches and in capabilities for handling large 

datasets. Adequate computational capacity is critical for (1) supporting spatial and temporal imaging 

approaches that improve mechanistic understanding 

of molecular to mesoscale biogeochemical processes, 

(2) analyzing advanced radar remote-sensing datasets to better understand microphysical cloud and 

precipitation processes, and (3) handling the large 

volumes of sequence and spectral data produced by 

genomic techniques. Another essential component 

of facility computing activities is the development 

of 3D models and high-performance computational 

methods for disordered systems and spectroscopic 

characterization. As the complexity of systems studied by BER researchers increases, so too will the need 

to develop computational tools enabling analysis of 

datasets from disparate sources over multiple length 

and time scales. Researcher access to the national 

laboratories’ cutting-edge computing resources for 

advanced data analysis and modeling could have 

an important role in developing next-generation 

capabilities. In all these areas, collaboration and integration among different facilities and techniques are 

essential for maintaining leadership in energy and 

environmental science (see also Chapter 6 on Data 

Analytics and Computing, p. 71).
94 Biological and Environmental Research Advisory Committee November 2017

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November 2017 Biological and Environmental Research Advisory Committee 95

Chapter 8 — Emerging Technologies

Developing Capabilities to 

Characterize and Represent 

Biological and Environmental 

Processes

The U.S. Department of Energy’s (DOE) Office 

of Biological and Environmental Research 

(BER) aims to understand fundamental biological, biogeochemical, and physical principles to be 

able to predict processes occurring at scales ranging 

from the molecular- and genomics-controlled smallest 

scales to environmental and ecological processes at the 

scale of planet Earth. Accomplishing this goal requires 

a range of capabilities to characterize and represent 

biological and environmental processes. Some of 

these capabilities already exist; others require further 

development and integration to enable complete 

analytical, imaging, and observational investigations. 

Further, when integrated at DOE user facilities, these 

capabilities can be combined and leveraged to foster 

an interdisciplinary approach to BER-relevant science. 

Many scientific breakthroughs are precipitated or 

enabled by technological developments as evidenced 

by advances in BER science over the past several years. 

These advances are intertwined with technological 

breakthroughs in omics, imaging, structural characterization, and atmospheric monitoring.

The technologies, tools, and methods described in 

this chapter are critical for advancing new, in-depth 

understanding of the complex, multiscale, multicomponent systems that are central to BER’s energy 

and environment missions (see Emerging Technologies Summary of Key Recommendations, p. 96). 

As new technologies, tools, instruments, platforms, 

algorithms, and approaches are developed, however, they must be made readily accessible to the 

researchers who need them to provide maximum 

value. Researchers must be able to either import and 

use these new technologies in their own laboratories 

or have access to them via user facilities with appropriate advanced expertise and support.

Building on Progress Related 

to the 2010 Grand Challenges

Many of the grand challenges presented in the 2010 

report (BERAC 2010) involved the development 

and application of new technologies, and significant 

progress has been made in many BER research areas 

described below. 

Dramatically Increased the Quantity and Quality of Genome and Metagenome Data. This 

increase is the result of enhanced experimental and 

computational methods and resources, including 

less expensive, higher-throughput, and longer-read 

sequencing technologies; improved methods for 

nucleic acid extraction and sequencing library 

preparation; sophisticated sequence analysis software; and enhanced databases and interfaces such 

as the Integrated Microbial Genomes and Microbiomes (IMG/M) system at DOE’s Joint Genome 

Institute (Chen et al. 2017) and the DOE Systems 

Biology Knowledgebase (KBase).

Improved Prediction, Expression, and Characterization of Secondary Metabolite Biosynthetic Pathways Involved in Interorganismal 

Interactions. These improvements include 

enhanced technologies to define the transcript, 

protein, and metabolite profiles of any particular 

organism with high sensitivity and selectivity, as 

well as mass spectrometry (MS)–based imaging 

to enable spatially resolved measurement and 

Emerging Technologies 8
96 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

visualization of metabolite mobility, flow, transport, and accumulation between cells (Musat et al. 

2016). Such methods enable the identification of 

specific pathways and, ultimately, the modeling of 

interorganism interactions.

Advanced Methods for Single-Cell Analysis. Only 

a few single cell–derived genomes were published in 

2010 (Podar et al. 2007; Woyke et al. 2009; Woyke 

et al. 2010). Since then, hundreds of such genomes 

have been produced, revolutionizing understanding 

of the evolutionary tree of life (Rinke et al. 2013). 

Emerging Technologies

Summary of Key Recommendations

In light of BER- and DOE-mediated technological advancements since 2010 and discussions about how 

emerging technologies will continue to develop into the future, the workshop produced several key 

recommendations for how BER could facilitate and capitalize on emerging technologies over the next 

20 years:

8.1. Characterize the genotype and phenotype 

of individual cells, including genomics, 

transcriptomics, proteomics, and metabolomics, 

to enable high-resolution predictive biology.

8.2. Increase throughput and integration of 

genomics, transcriptomics, proteomics, and 

metabolomics to enable improved translation 

from the molecular to cellular realm.

8.3. Characterize key processes of aerosols, clouds, 

turbulence, atmospheric convective systems, 

and their interactions to enable better modeling 

of water, carbon, and energy cycles of the Earth 

system and predictions of future climate change 

and extremes in atmospheric, terrestrial, and 

watershed events.

8.4. Integrate data covering broad time and 

length scales—from seconds to years and 

from Ångströms to the Earth scale—to enable 

multiscale comprehension and simulation.

8.5. Develop integrative and interpretive 

computational approaches that can handle 

large, disparate data types from multiple and 

heterogeneous sources using advanced and 

exascale computing.

8.6. Explore the development and application of 

nanoscience and nanotechnology to further 

understand biological systems at nanoscales or 

larger scales (e.g., nanosensors).

8.7. Explore the development and application of 

microsatellites and unmanned aerial systems as 

mechanisms for observation of atmospheric and 

terrestrial variables.

These technological efforts will provide the basis for addressing many of the challenges faced in biological 

systems science, Earth and environmental systems sciences, microbial to Earth system pathways, and 

energy sustainability.

Single-cell transcriptomics has grown significantly, 

with numerous approaches being applied to capture, tag, and analyze messenger RNA (mRNA) 

from individual cells (Kolodziejczyk et al. 2015). A 

variety of creative methods are extending single-cell 

methods to proteins, metabolites, and epigenetics, 

suggesting that multiomics analysis of individual 

cells may not be far off (Bock et al. 2016; Tsioris 

et al. 2014; McGlynn et al. 2015; Rosenthal et al. 

2013). At the single-cell level, MS-based imaging 

is emerging as a viable technology for visualizing 
November 2017 Biological and Environmental Research Advisory Committee 97

Chapter 8 — Emerging Technologies

a single cell and mapping the image to metabolite 

information (Pett-Ridge and Weber 2012).

Advanced the Field of Genome Editing. Genome 

editing is growing at an exceptionally rapid pace and 

shows astonishing promise in advancing genomic 

science (Damian and Porteus 2013). A system 

known as CRISPR (i.e., clustered regularly interspaced short palindromic repeats) is a powerful 

genome editing tool for inactivating genes, correcting mutated sequences, or inserting intact genes 

(Cong et al. 2013; Qi et al. 2013) in both prokaryotic and eukaryotic systems. This new genome engineering method is derived from an adaptive immune 

system that bacteria and archaea use to protect 

themselves against foreign invasive elements.

Advanced "Pump-Probe" Methods. These 

advances in structural biology include molecular 

dynamics pump-probe methods with free-electron 

lasers (FELs) and serial crystallography (Glownia 

et al. 2010; Johansson et al. 2013; White et al. 

2016). Both X-ray FELs (XFELs) and light sources 

can perform pump-probe dynamic experiments 

on the ultrafast time scale from three-dimensional 

(3D) crystals to enable the study of transient states 

(Levantino et al. 2015; Young et al. 2016).

Developed Cryo-Electron Microscopy (CryoEM).

This major new technology has advanced to the 

point where high-resolution atomic models can be 

obtained (without the need for crystals) for ~100 

kilodalton (kDa) proteins, viruses, membrane proteins, filaments, and molecular machines (Egelman 

2016; Allegretti et al. 2015; Callaway 2015; Li et al. 

2013; Wang et al. 2014; Glaeser 2016).

Advanced Imaging and Microscopy Techniques.

These advances, including transmission X-ray 

microscopy, neutron imaging, and confocal and epifluorescence microscopy, are enabling interrogation 

of difficult samples such as whole organisms, biofilms, and soils, at dramatically improved spatial and 

temporal resolution (Leis et al. 2009; Larabell and 

Nugent 2010).

Employed Ultrahigh-Resolution MS for Molecular Characterization. This technique has enabled 

molecular characterization of the thousands of soil 

organic molecules previously described in terms 

of bulk soil carbon:nitrogen:phosphorus ratios 

(Musat et al. 2016). With this new understanding, 

refined models of carbon pool diversity can be evaluated across ecosystems, and evidence of potential 

metabolite pathways can provide much needed context for integrative multiomic approaches.

Advanced Nanoscience and Nanotechnology.

New tools have been developed for mapping and 

characterizing connections at the nanoscale and 

increasing resolution of imaging technologies 

(Biteen et al. 2016). Coordinated investments 

were made through the interagency National 

Nanoscience Initiative (www.nano.gov), including the establishment of five Nanoscale Science 

Research Centers (NSRCs), which are national scientific user facilities supported by DOE’s Office of 

Basic Energy Sciences. The five NSRCs are (1) the 

Center for Functional Nanomaterials at Brookhaven 

National Laboratory (BNL), (2) Center for Integrated Nanotechnologies at Los Alamos National 

Laboratory and Sandia National Laboratories, 

(3) Center for Nanoscale Materials at Argonne 

National Laboratory (ANL), (4) Center for Nanophase Materials Sciences at Oak Ridge National 

Laboratory, and (5) Molecular Foundry at Lawrence 

Berkeley National Laboratory (LBNL).

Deployed New Instruments for Advancing 

Atmospheric Science. These instruments are 

measuring clouds and aerosols at fixed and mobile 

sites supported by DOE’s Atmospheric Radiation 

Measurement (ARM) Research Facility. They 

include X-band, W-band, and Ka-band scanning 

cloud radars; C-band polarimetric scanning radar; 

zenith pointing Doppler lidar; and unmanned aerial 

systems (UAS) and tethered balloons to increase 

the sampling frequency of both temporal and spatial 

cloud measurements.

Enabled Chemical Change Measurements in 

Live Microbial Specimens. Synchrotron-based 

broadband Fourier transform infrared (sFTIR) 

spectroscopy or spectromicroscopy now permits 

live cell analysis and observation of chemical events 
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in the same cell or sample by avoiding the damage 

caused by other methods (Holman et al. 2002). 

As a result, sFTIR spectromicroscopy (Holman 

et al. 2010; Probst et al. 2013) and time-resolved 

microfluidic sFTIR spectromicroscopy (Holman 

et al. 2009; Holman et al. 2010; Loutherback et al. 

2016) are now matured techniques for measuring 

chemical changes in live microbial specimens with 

diffraction-limited spatial resolution of 2 to 10 

microns (μm) and temporal resolution of several to 

tens of seconds.

Developed Scalable Computational Models for 

Molecular Transformations. These models were 

developed in NWChem, high-performance computational chemistry software, to describe molecular 

transformations in complex environments. Improvements include (1) new, enhanced capabilities to 

support experiments [nuclear magnetic resonance 

(NMR), electron paramagnetic resonance (EPR), 

photoelectron spectroscopy (PES), electron 

energy-loss spectroscopy (EELS), and ultraviolet-visible (UV-Vis) spectroscopy] at DOE’s Environmental Molecular Sciences Laboratory (EMSL); 

(2) methods to interpret and support state-of-theart X-ray light source experiments at user facilities 

housed at LBNL, SLAC National Accelerator Laboratory (SLAC), ANL, and BNL; (3) optimization 

methods to study reaction mechanisms; (4) relativistic approaches to support heavy-element chemistry; and (5) new electronic structure methods and 

multiscale formulations. Significant progress also 

has been achieved in the development of novel algorithms for emerging multicore architectures [e.g., 

graphics processing unit (GPU) and Intel Xeon 

Phi] and expert and machine-learning systems (e.g., 

EMSL Arrows).

Emerging Technologies and the 

Importance of Integration 

Across Scales

Emerging technologies play a key role in state-ofthe-art research. Experimental and modeling needs 

can drive technology or software development. 

Conversely, existing technologies can be brought 

together in many unique ways to expand their capabilities or explore new research questions that are 

essential for complete process understanding at the 

multiscale perspective.

A central theme at the Biological and Environmental 

Research Advisory Committee’s 2017 Grand Challenges workshop was the concept of scaling, both 

in terms of the inherent scales of measurements and 

the importance of cross-scale understanding. At 

larger scales, observations can be made of emergent 

phenomena that arise from the interactions of complex and collective processes whose manifestations 

are not apparent at reduced scales. At smaller scales, 

and at reduced complexity, mechanistic understanding can be obtained of dominant processes and the 

underlying mechanisms that give rise to the full 

phenomenological expression. Nevertheless, these 

top-down and bottom-up perspectives must be integrated in order to develop quantitative, predictive 

models. There are various approaches to the development of multiscale models, including hierarchical 

models where most process understanding is contained at the smallest scales, reduced-order models 

developed for specific processes at larger scales, and 

system models of multiple interacting processes. All 

these models should continue to be pursued as strategies to combine data and knowledge across scales.

The rest of this chapter groups the grand challenges 

in technology based on the scale at which they apply. 

While such classifications are imprecise, they provide a context in which potential cross-technology 

links are more apparent.

Molecular Scale

Structural Biology and Imaging. Many different 

biophysical measurements can provide spatial and 

temporal information, but structural imaging offers 

the most direct means for characterizing a biosystem’s organization. Atomic and molecular structure 

provides the foundation for functional models that 

bridge larger-scale biological processes to underlying chemistry. Current intuitive understanding 

of biomolecules at nanoscales remains insufficient 

to connect the exponential increases in genomic 
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Chapter 8 — Emerging Technologies

sequence information to form, function, and mechanism. New technological developments have transformed the ability to probe biological phenomena 

at the nanoscale and relevant temporal scales. These 

developments include cryoEM and cryo-electron 

tomography (cryoET), NMR, neutron macromolecular crystallography (NX), small-angle neutron scattering (SANS), small-angle X-ray scattering (SAXS), 

and macromolecular X-ray crystallography (MX). 

New advances in experimental methods combined 

with computation are required to better understand 

biosystem and ecosystem dynamics such as enzyme 

function, photosynthetic light capture, and engineering for optimal biofuel synthesis.

FELs, Serial Crystallography, and Electron 

Microscopy. In X-ray and neutron structural biology, the key to future impact and innovation is 

continued development of enabling technologies 

through the construction of state-of-the-art instruments that exploit the world-leading characteristics 

of each of the light and neutron sources. These 

instruments will boost capabilities for conducting basic biological research, thereby producing 

leading-edge science for BER missions in energy and 

environment. At the frontier are new instruments 

that will enable multimodal imaging capabilities 

over length scales from subnanometers to millimeters. Opportunities are available to take further 

advantage of the new XFEL sources and optimize 

their use by developing coupling technology at light 

sources that feeds into XFEL sources and vice versa. 

The commissioning of fourth-generation XFEL light 

sources such as the Linear Coherent Light Source 

at SLAC has created the new paradigm of “diffractbefore-destroy” imaging (Spence and Hawkes 

2008). These femtosecond X-ray sources empower 

new research in MX and absorption and emission 

spectroscopy (Kern et al. 2013) by allowing analysis 

of samples—such as 3D nanocrystals (Aquila et al. 

2012), 2D crystals (Frank et al. 2014), and large 

single-particle complexes (Saldin et al. 2011)—not 

amenable to analysis at synchrotron light sources.

Molecular Dynamics Pump-Probe Methods.

Visualizing protein dynamics relevant to BER 

missions requires pump-probe methods spanning 

the femtosecond to second or longer time regimes. 

From fastest to slowest, typical protein dynamics 

include electronic and photoinduced transitions, 

ligand binding, active site motions, loop and helix 

movement, domain and subunit motions, assembly 

and disassembly, and molecular and macromolecular diffusion. Continued improvements are needed 

for stimulating or triggering a reaction or process 

(i.e., “pumping the system”), in combination with 

new approaches for capturing time-resolved structural and chemical data (i.e., “probing the system”) 

across the relevant time scales. Fast and ultrafast 

X-ray approaches (i.e., synchrotron- and XFELbased, MX, serial crystallography, and spectroscopy) 

and electron approaches [i.e., dynamic transmission 

electron microscope (DTEM), ultrafast electron diffraction, and ultrafast electron microscopy] promise 

new opportunities for interrogating macromolecular 

complexes in their native geometry and chemical 

environment. Simultaneous measurements using 

multiple techniques can provide very new understanding of biological and environmental processes.

Both XFELs and light sources can perform pumpprobe dynamics experiments on the ultrafast time 

scale (tens of femtoseconds to picoseconds to 

microseconds) from small 3D crystals (Levantino 

et al. 2015; Young et al. 2016). However, new source 

developments improving peak brightness, coupled 

with new methodologies to permit atomic resolution 

pump-probe imaging of 2D crystals and, ultimately, 

single-particle macromolecular complexes, would 

be highly beneficial for understanding the structural 

dynamics of a wide range of BER-relevant targets 

in near-native geometries and environments. These 

techniques potentially could revolutionize future 

structural biology and cellular imaging, but they 

require significant capital and human investment 

currently out of reach for many research institutions.

While these “newer” technologies provide novel 

insights into more complex systems and at smaller 

time scales, mature technologies such as MX, 

SAXS, NMR, NX, and SANS continue to evolve 

and fulfill new roles in biosystem imaging and 
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analysis. Synchrotron-based MX remains unparalleled in providing high-throughput, high-resolution 

macromolecular structures, with recent advances 

extending these results to more challenging and flexible systems. The MX workflow is particularly well 

adapted for conducting expansive surveys and offers 

a means for rapidly testing predictions. SAXS also 

supplies high-throughput structural information 

(Brunette et al. 2015; Bale et al. 2016; Boyken et al. 

2016), while NMR, SANS, and NX allow for altered 

contrast in heterogeneous materials and provide 

direct insights into the role of hydrogen atoms that 

can only be inferred by other techniques (Frederick 

et al. 2015). In all cases, support for these critical 

capabilities, as well as their continued innovation, is 

needed to enhance understanding of biosystem and 

ecosystem complexity and unravel mechanisms central to biological and environmental processes. Neutron imaging shows great promise for plant biology, 

revealing plant architecture and internal plant-water 

dynamics, for example, with pulses of water or heavy 

water (D2O) in situ and in real time.

Macromolecular Characterization with CryoEM.

Recent advances have made possible the study of 

macromolecules of smaller size and at much higher 

resolution in vitrified solutions via cryoEM (Merk 

et al. 2016). These advances include new direct electron detector device cameras with very fast readout 

(e.g., Gatan Summit Direct Electron Detector), 

advanced microscopes with very stable optics (e.g., 

Titan Krios), new modes of data acquisition, control for radiation damage, and correction for sample 

motion. The ability to collect data on macromolecules 

in solution in near-native environments without 

having to grow crystals cannot be underestimated. 

The relatively modest number of particles needed 

for high-resolution model reconstruction highlights 

the power of this technique’s recently developed 

hardware and software. Additional developments in 

phase plate technology have expanded this powerful 

technique’s applicability to even smaller protein complexes once thought well beyond reach with conventional film or charge-coupled device (CCD) cameras. 

Observed heterogeneity of molecules in vitrified 

specimens has enabled exciting discoveries into the 

conformational landscape of macromolecular complexes (Amunts et al. 2015; Bai et al. 2015; Nguyen 

et al. 2016). In addition to a cryoEM revolution for 

going smaller with single-particle structure determination, new methods for micro-electron diffraction 

(microED) have enabled atomic-resolution structures from protein crystals previously too large for 

transmission electron microscopy (TEM). Expected 

improvements in sample preparation, hardware, 

detectors, data acquisition, and software will enable 

much wider use and increase the throughput of 

atomic-resolution structure determination. In addition, emerging TEM capabilities on the nanosecond 

to millisecond time scale with atomic to near-atomic 

resolution (Evans and Browning 2013) would extend 

this approach to spatiotemporal regimes relevant to 

secondary structure element, domain, and subunit 

motions central to regulating protein activity. The 

ability to visualize all conformational states of a given 

enzyme during a reaction continuum would help 

advance isozyme engineering for more efficient biofuel production. Continuing development of methods and hardware for single-particle cryoEM, cryoET, 

TEM, and microED is much needed for enabling 

high-throughput structural determination and providing broader access to the BER research community.

Advances in NMR and EPR. NMR spectroscopy 

and EPR have long been used to elucidate protein 

structures, but they have been hampered by a lack of

in situ capabilities for interrogating molecule:substrate binding under conditions pertinent to biogeochemical environments (e.g., ambient temperature 

and pressure). NMR is recognized as an important 

and often indispensable analytical tool for structure 

elucidation, molecular identification, measurements 

of dynamics (on time scales from nanoseconds to 

hours), and imaging. Many major breakthroughs 

in biotechnology, medicine, and clinical treatment 

have been tied to rapid advancement of new and 

improved NMR tools, but a general lack of sensitivity in NMR typically precludes samples with low 

analyte concentrations. However, new commercial 

hardware developments [i.e., 1.7-millimeter (mm) 

NMR microcryoprobe] have made possible the 

detection of submicromolar concentrations for 
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Chapter 8 — Emerging Technologies

sample-limited projects [~30 microliters (μL)] such 

as those that require fractionation prior to NMR 

analysis. Coupling specialized sample containment 

systems that provide environmental control over 

pressure, temperature, and head space gas will yield 

information that corresponds to field and in vivo

conditions. Using these new hardware components 

will enable biological molecules and their origins and 

interactions in environmentally relevant samples to 

be identified and quantified at a level of accuracy that 

far surpasses anything to date. Another long-standing 

limitation of NMR spectroscopy, the need for samples with macroscopic dimensions, potentially could 

be addressed by lab-on-a-chip technology. On a fundamental level, the emerging capability to study individual microsystems such as biological cells using 

a range of tools will yield understanding of microscopic heterogeneity and interaction dynamics.

Another key component is high-performance computational methods development (e.g., 3D models, 

disordered systems, spectroscopic characterization, 

and dynamics). As the complexity of systems under 

investigation by BER researchers increases, the 

development of computational tools that enable 

analysis of datasets from disparate sources over 

multiple length and time scales is critical. National 

laboratory resources provide opportunities for 

scientists to use existing cutting-edge computing 

capabilities for advanced data analysis and modeling, and this access could have an important role 

in developing next-generation capabilities. In all 

these areas, the cross-fertilization between different 

facilities and techniques is essential for effective 

national stewardship.

Advances in Imaging and Microscopy. Biology is 

reducible to a series of highly orchestrated chemical 

events occurring at defined locations within cells. 

New methods are needed to image the localization 

of enzyme reactions and flow of chemicals and macromolecules within and between cells and unravel 

the complex network of molecular interactions 

defining cell phenotypes and functions. Substantial 

improvements in spatial resolution for fluorescence 

microscopy methods recently have been made with 

new super-resolution methods and minimization of 

optical flux needed for detection. These methods now 

enable nanometer positional accuracy (Balzarotti 

et al. 2017). Stimulated Raman scattering microscopy, a nonlinear optical approach, also has significantly expanded as a quantitative direct imaging 

method that allows tracking of cellular dynamics in a 

label-free manner or by using selective Raman tags.

Additional advances in nonfluorescence optical 

detection methods include subdiffraction limit 

measurements using tip-enhanced capabilities and 

stimulated Raman scattering. Over the last few years, 

developments in near-field infrared chemical detection technologies have enabled vibrational signature 

measurements of organic and biological materials 

at the nanoscale. These new capabilities include 

infrared scattering–type scanning near-field optical microscopy (IR s-SNOM) and infrared atomic 

force microscopy (AFM-IR). Efforts are under way 

to combine IR s-SNOM with the more established 

tip-enhanced Raman spectroscopy and optical 

absorption nanospectroscopy to probe the vibrational and chemical signatures of biomolecules at or 

near the surface of cells in aqueous environments.

Ion microscopy has undergone a number of developments in recent years, from vacuum-compatible 

microfluidics enabling in situ probing of liquid interfaces using time-of-flight secondary ion mass spectrometry (ToF-SIMS) to new frontiers for nanoscale 

secondary ion mass spectrometry (NanoSIMS) and 

similar direct imaging approaches. These approaches 

provide maps of molecular components and can be 

used to study complex microbiological processes 

and community interactions.

Genetics and Genomics. Genomic data have been 

generated from thousands of organisms and environments, enabled by vast increases in sequencing 

throughput. Yet much of the biological world remains 

uncharacterized at the molecular level. Sequence 

data from natural environments still typically lack 

high-identity matches to reference genomes, and 

much of the sequence in both reference genomes and 

metagenomes is unannotated or poorly annotated. 
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Improving gene function understanding will require 

advances on multiple fronts.

The continued expansion of the reference genome 

database through genomic sequencing of both 

microorganisms and macroorganisms across the 

tree of life is a critical first step in linking genotype 

to phenotype and gene to function. These genomic 

references are critical infrastructure for countless 

downstream analyses and can be generated not only 

through sequencing of cultivated, known organisms, 

but also single-cell and metagenome sequencing of 

uncultivated, unknown organisms. Improved and 

higher-throughput sequencing technologies and 

approaches will accelerate progress toward genomic 

characterization across the tree of life.

Equally critical to genomic interpretation, but 

lagging far behind sequence data generation, is 

functional characterization of genes and genomes. 

Computational analysis alone will not definitively 

assign function, yet most methods for functional 

analysis remain one gene at a time. High-throughput 

annotation strategies such as transposon mutagenesis and sequencing (Tn-seq) are highly informative but limited in application, so alternatives and 

improvements should be actively sought.

Other Omics. Transcriptomics, proteomics, and 

metabolomics are critical molecular-scale techniques, but the expense, technical difficulty, and 

throughput of these methods still pose impediments, particularly in terms of spatial and temporal 

resolution. Simplified, high-throughput, and inexpensive methods for these inquiries would dramatically expand the ability to interrogate metabolic and 

regulatory processes.

Currently, metabolite characterization requires 

mapping mass spectrometric features to a known 

database, but, with secondary metabolites, such 

databases are limiting. Comprehensive public databases are urgently needed, as are new approaches for 

database-free metabolite characterization.

Direct single-molecule RNA sequencing (e.g., with 

Oxford Nanopore; Garalde et al. 2016) potentially 

could open up whole new avenues of investigation.

Reduced-Input Material for Molecular Assays. 

Analytical instrumentation often vastly outstrips 

current ability to effectively sample, prepare, and 

deliver small samples to the instrument. New 

microfluidic sample preparation–based techniques 

are enabling high-throughput processing of small 

samples with very little loss, and structural biology advancements have significantly reduced the 

amount of material required for structural characterization. XFELs use nanocrystals and microcrystals, 

and efforts to reduce sample needs and determine 

structure with a single crystal are common practice at light sources. Under optimal circumstances, 

cryoEM can produce structures with just several 

thousand individual particles.

Cellular Metabolite Dynamics. Comprehensive 

biosystem characterization requires understanding how a system senses environmental perturbations and the mechanisms that cells use to make 

changes in response to these cues. By developing 

new bioimaging and spectroscopy capabilities 

spanning electron, ion, optical, neutron, and X-ray 

modalities and fusing them with new advances in 

systems biology, computation, and automation, a 

deeper understanding of the link between genome 

to phenome is possible. Exploiting and controlling 

those linkages would radically enhance the ability 

to design and harness biosystems for cheaper, more 

efficient biofuels and bioproducts, increased crop 

yields, and biological routes to soil remediation. 

Biological systems rely on chemistry taking place 

at the correct time and in the right place (Le Gros 

et al. 2012). Consequently, tracking the location 

and concentration of molecules over time is a key 

characterization step for biological investigations. 

The accurate characterization of small numbers of 

molecules produced by native or engineered biosystems remains challenging, particularly for single-cell 

measurements. NMR, FTIR, and MS approaches 

are well suited to identifying chemical composition. 

MS imaging (e.g., NanoSIMS) enables part-permillion detection of ions and isotopes with up to 

~50 nanometer spatial resolution, although it is a 

static and destructive method best suited to imaging 

chemical gradients and cellular assimilation.
November 2017 Biological and Environmental Research Advisory Committee 103

Chapter 8 — Emerging Technologies

Nanotechnology. Nanotechnology enables the 

manipulation of individual molecules and atoms 

and has emerged as a dynamic field of innovation 

(Allhoff et al. 2010). Through nanotechnology, 

scientists can create novel materials and devices at 

the nanoscale, and these structures can be used to 

further BER missions in imaging, monitoring, and 

understanding of biological and environmental 

systems to advance energy innovation and development. For example, nanopores can be used to 

quickly and cheaply identify and characterize small 

molecules and genomes (Branton et al. 2008); 

nanosensor networks can be used to detect chemicals and monitor environmental variables potentially 

in both single and multicellular contexts (Akyildiz 

and Jornet 2010).

Cellular Scale

Linkage of Genotype to Phenotype for Predictive Biology. Biological organisms are adapted to 

compete and survive in a vividly diverse range of 

environments. Despite major advances in genome 

sequencing and synthetic biology approaches 

enabling the synthesis of engineered pathways and 

genes, understanding of an organism’s metabolic 

constituency remains poor and lacks a catalog of 

the full complement of enzymes encoded within 

biological organisms. Improved methods are needed 

to gain a better understanding of the metabolic 

pathways necessary to leverage advanced genetic 

engineering methods for designing organisms that 

synthesize bioproducts with improved or novel 

properties at synthesis rates compatible with 

industrial-scale production.

Better understanding of the metabolome of a 

diverse collection of organisms, along with a mechanistic understanding of the proteins that control 

metabolite biosynthesis, localization, degradation, 

and activity, would enable a predictive model of 

how an organism’s total chemical constituency is 

determined. With this determination of genome 

sequence and transcriptomic and proteomic datasets, predicting how a cell’s chemical environment 

may change with its environment or vary between 

genotypes will be possible. Also potentially within 

reach are predictions that an organism can synthesize a chemical that is never actually detected. Cataloging the genome sequences of diverse organisms 

may then enable a catalog of the predicted chemical 

diversity of life that preserves knowledge of biosynthetic pathways present in extinct or rare organisms.

Phenotyping and Profiling Natural Cells from 

the Environment at the Single-Cell Level. Cells 

are the functional unit of most biosystems, and their 

intracellular organization dictates how cellular phenotypes respond to variations in environmental conditions and genetic and chemical modifications.

To date, most research in single-cell biology has 

focused on mammalian cells because of their large 

size, accessibility, and relative ease of imaging. 

Understanding biology across scales and different 

environments, however, will require far more sensitive and higher-resolution technologies that also 

can analyze microbes and microbial communities. 

Needed are new approaches for handling extremely 

small samples while interfacing with different 

instruments to facilitate multiomics measurements. 

Because of the extremely small amounts of RNA 

present, proteins and metabolites likely to be 

recoverable from microbial cells essentially require 

lossless separation and measurement techniques. 

Automated, miniaturized, and microfluidic-based 

methods will be necessary to adapt current multiomics approaches to the single-cell level.

Proteins and metabolites pose a particular challenge 

because, unlike genomes and transcripts, they cannot be amplified. The large dynamic range of protein 

and metabolite abundances within the cell will 

require even more sensitive instrumentation or separation methods. MS-based imaging approaches also 

hold promise for gaining cellular-scale information 

on proteins and metabolites in a single cell in addition to morphological and contextual data.

In-Depth Spatiotemporal Understanding of Individual Cells. New methods for in situ, dynamic, or 

cryogenic imaging of whole organisms are needed 

to determine the highly heterogeneous organization 
104 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

of cells with nanoscale resolution to refine theory 

and models. Ultrastructural whole-cell imaging 

has improved significantly through the incorporation of phase plate technologies with electron 

tomography approaches. This advance has enabled 

high-resolution, 3D visualization of the cellular 

ultrastructure, organization, and protein localization of intact cells. With this improved resolution, 

difficult-to-purify protein complexes in their native 

cellular context are structurally resolvable using 

computational tomogram averaging, while dynamic 

events (e.g., host and phage interactions and phage 

lifecycle) can be mapped within the cell to reveal 

previously unknown interactions. The field of visual 

proteomics also has emerged in recent years and 

combines whole-cell electron tomography with computational template matching of known structures to 

visualize, identify, and quantify each cell component, 

a long-held dream of many biologists. New advances 

in structural biology, top-down and bottom-up proteomics, high-throughput cryoET, and integration 

with advanced informatics and high-performance 

computing (HPC) would further accelerate this field 

and increase its availability to researchers.

Heterogeneity and stochastic noise are intrinsic 

constraints on life. Biological processes such as transcription and translation are limited by fluctuations 

in the amount and localization of the molecules 

that drive them. Thus, cell regulatory systems have 

evolved to deal with intrinsic biological noise and 

sometimes to exploit it (e.g., bet hedging). Understanding biological regulation requires understanding the role and impact of stochastic variations, 

as well as how variation at the individual cell level 

translates into population behavior.

In situ measurements using advanced imaging technologies are likely to be crucial for understanding 

the spatial and compositional heterogeneity of 

microbial cells, populations, and communities. 

Although much effort has focused on improving the 

spatial resolution and chemical detection capabilities 

of imaging technologies, more attention must be 

given to their ability to discriminate between different biomolecules. Multispectral fluorescence in situ

hybridization (FISH) has proven useful for identifying different microbial species in a community. 

Moreover, combinatorial barcoding of FISH probes, 

together with super-resolution and single-molecule 

fluorescence imaging, has shown promise for quantitative multigene expression analyses in intact cells 

and communities. However, these approaches generally are limited to thin samples with near-ideal optical properties. Improvements need to be made in the 

robustness of FISH techniques, number of different 

identifiable molecules, and their use with nonoptical 

microscopies (e.g., NanoSIMS). Also much needed 

are fixation and permeabilization methods compatible with downstream nucleic acid sequencing (e.g., 

on flow-sorted cells).

Multiplexed protein measurements for single cells 

are increasingly common using either lanthanumlabeled antibodies in conjunction with mass cytometry (i.e., CyTOF) or ion beam imaging (i.e., 

NanoSIMS). However, the limited availability of 

microbe-specific antibodies and difficulty of using 

them to label microbes have restricted this technique 

primarily to the detection of surface proteins. New 

approaches are required for multiplexed, in situ analysis of protein abundance in bacteria.

Current methods for examining gene expression heterogeneity at the single-cell level mostly use specific 

antibodies, multiplexed mRNA FISH techniques, 

gene-tagging approaches using CRISPR techniques, 

or RNA-Seq (e.g., combined with microfluidic cell 

separation or Drop-Seq). Although their adaptation 

for investigating plant and fungal systems is conceptually straightforward, their use in microbial systems 

is far more daunting. Microbes lack the DNA repair 

systems required for CRISPR tagging, and their 

lack of polyA tags and very low transcription burst 

frequency make single-cell RNA-Seq mostly uninformative (Choi et al. 2008), except in the case of 

bacteria large enough to maintain significant mRNA 

pools (e.g., cyanobacteria) or for a few abundant 

transcripts. Thus, new technologies are needed to 

determine the gene expression patterns of individual 

bacterial cells. Because protein abundance represents 

the integration of both gene expression and cellular 
November 2017 Biological and Environmental Research Advisory Committee 105

Chapter 8 — Emerging Technologies

phenotype, this parameter is likely to be the most 

accessible measurement in the near term for defining 

microbial cell heterogeneity. Needed are improvements in the sensitivity, specificity, and spatial 

resolution of both microbial proteomics and transcriptomics. These improvements should be developed in conjunction with new assays to measure cell 

phenotypic (e.g., metabolite) profiles.

Metabolic Pathway Engineering. Engineering 

metabolic pathways in microbes and plants has seen 

some success in the reconstruction of pathways necessary for drug synthesis (Lau et al. 2014; Lau and 

Sattely 2015). Introduction of the morphine pathway 

into yeast, for example, requires methods not only to 

express plant enzymes involved in the biosynthesis 

of this compound, but also to ensure correct localization of the proteins and transport of intermediate 

substrates between cell compartments (Thodey et al. 

2014; Galanie et al. 2015). Thus, understanding how 

cells subcompartmentalize biosynthetic reactions 

between cells and cell subcompartments is needed to 

reconstruct or redesign such synthetic pathways. The 

ability to localize specific enzymatic steps into these 

discrete domains of an organism may prove useful 

in avoiding side reactions that reduce biosynthetic 

efficiency or lead to toxic byproducts. The ability 

to engineer alternate hosts with different metabolic 

capabilities and sensitivities will further expand the 

range of biosynthesis opportunities.

Cell-free expression platforms have emerged as 

powerful alternatives to laborious in vivo methods of 

protein synthesis that support the growing demand 

for easy and cost-effective protein production methods. Cell-free expression platforms permit expression of target proteins in a test tube in a few hours to 

days depending on protein size and requisite yields. 

Several cell-free protein expression systems are 

commercially available based on extracts from Escherichia coli, wheat germ, rabbit reticulocyte, Leishmania tarentolae, insects, or human cells. Although not 

currently commercially available, efficient cell-free 

lysates also can be prepared from tobacco BY-2 cells 

and Chinese hamster ovary (CHO) and yeast cells. 

Additional cell-free expression platforms from other 

source organisms, as well as more efficient pipelines 

for gene synthesis, expression screening, and purification, will help make these approaches readily 

accessible to a broader set of researchers.

Community Scale

Interrogation of Community Membership, Functional Potential, and Activity. Microbes play critically important roles in the environment, shaping 

plant health and productivity, the terrestrial carbon 

cycle, and environmental remediation. They occupy 

every biological niche and represent the largest portion of global biodiversity, with estimates indicating 

that the global protein universe of microorganisms 

exceeds 1012 including novel enzymes. However, 

present knowledge of microorganisms and their 

enzymes is based largely on laboratory studies of a 

small number of pure microbial cultures. The vast 

majority of microbes within environmental consortia 

cannot be cultured, leading to a lack of understanding for how microbial communities are structured 

and how they evolve (Solomon et al. 2014). The 

ability to identify the organisms involved in environmental processes has improved significantly with 

modern sequencing technologies, but obstacles such 

as polymerase chain reaction (PCR) bias and relic 

DNA result in an incomplete understanding of community behavior. Comprehensive characterization of 

environmental communities, including macro- and 

micro-eukaryotes, bacteria, archaea, and viruses, will 

require moving away from PCR-based methods and 

developing techniques to target metabolically active 

organisms regardless of phylogeny.

Microbiome research seeks to define community 

membership, ecological relationships between 

organisms, and the roles specific taxa play in 

system-level chemical and biological processes. 

Regardless of their habitat, microbiomes comprise 

many different taxa exploiting an energy source, yet 

these microbial assemblages often are inherently 

interdependent and dynamic in both space and 

time. The advent of high-throughput sequencing, 

advanced microscopy, and stable isotope tracing 

techniques has made comprehensive understanding 
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Grand Challenges for Biological and Environmental Research: Progress and Future Vision

of in situ microbiome ecology tantalizingly possible. 

However, current microbiome studies often are 

highly descriptive, focused on correlation patterns or 

simple one-on-one interactions between culturable 

organisms. A more mechanistic, spatially and temporally resolved understanding of plant-microbe and 

microbe-microbe interactions is needed to enable 

sustainable biofuel feedstock growth, increase food 

security, increase terrestrial carbon sequestration, 

limit contaminant transport, and enable engineering 

of microbial traits important to plant communities.

While genomic, transcriptomic, metabolomic, and 

proteomic methods have advanced considerably, 

they still are most often applied to single-microbe, 

cultured systems. Results from these data streams 

need to be more effectively integrated, and their 

predictions verified via manipulative experiments, 

metabolic modeling, and quantitative observations 

of metabolic fluxes within and between organisms 

and via imaging or (bio)chemical characterization. 

Current knowledge of plant metabolites is derived 

primarily from highly simplified hydroponic systems, but plants naturally grow in a soil matrix 

where metabolites of interest are likely intercepted 

by the surrounding microbiome before they can be 

collected. Thus, new methods are needed for collecting plant exudates and metabolites under realistic conditions.

Genetic Tools for Recalcitrant Organisms. The 

number of model organisms for which robust 

genetic tools have been developed is relatively 

small compared to the number of organisms whose 

genomes have been sequenced. Although various 

methods have been developed for delivering DNA 

into cells, the ability to precisely rewrite sections 

of a nonmodel organism’s genome is only now 

undergoing rapid development. Advances in this 

area have been greatly accelerated by the development of CRISPR–CRISPR-associated protein-9 

nuclease (Cas9) as a genome editing tool, including 

approaches (Cong et al. 2013; Qi et al. 2013) targeted to both prokaryotic and eukaryotic systems, 

now enabling genetic modifications of an expanded 

set of organisms. However, additional barriers 

mitigate the effectiveness of even CRISPR systems, 

including but not limited to cell wall thickness, 

recombination, and transformation efficiency. Further, these strategies must be developed for systems 

for which no draft genome is available and for 

polyploid cells that limit the effectiveness of genetic 

modification. Ideally, these tools could target individual members of natural consortia for either total 

ablation or specific genetic manipulation.

Biotic and Abiotic Interactions. The ability to 

conduct in situ interrogations of live microbial community systems, including plant-associated microbes, 

is critical to understanding carbon-nitrogen and 

carbon-sulfur cycles in simulated and natural ecosystems, as well as the community roles of individual taxa on community structure and function by 

sequential identification of reduced gases [hydrogen 

sulfide (H2S), nitrous oxide (N2O), and methane 

(CH4)] and shifts in carbon signatures. Measurements of H2S, sulfur dioxide (SO2), N2O, nitric oxide 

(NO), nitrogen dioxide (NO2), CH4, carbon dioxide 

(CO2), and other important gases, while characterizing changing pools of dissolved organic carbon, may 

be accomplished via in situ 1

H, 13C, and 31P NMR 

for microbial communities. Development of environmentally controlled tandem NMR with infrared, 

gas analyzer probes, and gas chromatography–MS 

for in situ interrogation of live microbial community 

systems would enable these measurements, resulting 

in significant progress toward more comprehensive 

understanding of microbial metabolism coupled to 

environmental conditions.

Biological systems are constantly changing and 

adapting to their environment, but most experimental approaches are limited to snapshot-in-time 

surveys of the system. To fully understand biological 

system dynamics, sampling must occur over time 

and space. Stable isotope probing technologies 

enable this type of temporal sampling. Following 

labeled molecules over time through space provides 

understanding of not only the state of the system, 

but also its nutrient flux, metabolite exchange, and 

reaction rates. Many technologies exist to measure 

isotopic enrichment, but improvement in sensitivity 
November 2017 Biological and Environmental Research Advisory Committee 107

Chapter 8 — Emerging Technologies

and selectivity is necessary to increase the granularity of the studies. Importantly, most isotopic analyses are not high throughput, limiting the application 

of this approach. A great advantage of isotopic analyses is the ability to probe from the cell to the system. 

For this impact to be realized, new approaches for 

data integration and modeling are necessary to combine the data from varied spatial scales into a single 

biological model.

Laboratory Scale

Model Ecosystems. The interaction of plants with 

their environment is one of the most complex and 

highly evolved systems on Earth. This interface or 

“interaction zone” between biotic and abiotic processes marks the heart of biogeochemistry. Microbes 

and organic molecules in the belowground environment rarely are randomly distributed; rather, they 

preferentially interact with specific mineral phases 

that have varying degrees of crystallinity, mineral 

size fractions, and redox states. Microbes are known 

to harvest elemental nutrients and even electrons 

from their mineral substrates, and organic molecules 

preferentially associate with mineral phases because 

of their surface charge at a given pH or the presence 

of specific functional groups and ligand chemistry. 

Abiotic parameters such as pH, cation exchange 

capacity, redox, and water saturation also are heavily 

influenced by biotic factors during this interplay, 

resulting in heterogeneity across broad spatial and 

temporal scales and giving rise to biogeochemical 

“hot spots” and “hot moments.” This dynamic and 

heterogeneous nature of ecosystems poses major 

challenges to reproducible scientific investigations in 

controlled, yet field-relevant, conditions.

One potential means of making laboratory analysis 

of root growth more relevant to the field is using 

a transparent medium with actual soil properties. 

The polymer Nafion is transparent, with a refractive 

index close to that of water, and is claimed to have 

physical properties similar to those in soil (Downie 

et al. 2012). Nafion’s primary use as a soil substitute, to date, is to observe root-microbe interactions 

(Downie et al. 2014).

Additionally, phytotrons (i.e., facilities with plant 

growth chambers that enable detailed control of 

environmental conditions such as light, humidity, 

atmospheric chemistry, sterility, temperature, and 

pressure) can provide an experimental platform 

for (1) growing the large number of plant systems 

required for genome-to-phenome mapping (i.e., 

from microbes to atmospheric flux); (2) creating 

replicated experiments; and (3) providing access to 

identical systems for destructive sampling and monitoring of metabolic function, as well as access to the 

rhizosphere for microbe and root exudate studies. 

However, phytotrons lack the inherent complexity of 

the field conditions where plant communities; mesofauna; and natural soil chemical, mineralogical, and 

textural heterogeneity interplay with natural hydraulic conditions to create unique spatial and temporal 

domains from nanometer and microsecond to meter 

and annual scales. In contrast, ecotrons, in which 

several cubic meters of natural ecosystems are transported into massive environment-controlled growth 

chambers, preserve some elements of field conditions and multispecies plant-mesofauna-microbe 

communities with natural soils permitting minimally 

disturbed textures and hydrology. Ecotrons provide many of the benefits of phytotron studies but 

with much greater complexity approaching that of 

field conditions. Both phytotrons and ecotrons can 

provide testbeds for laboratory-to-field technology 

development and sensor calibration.

Laboratory experiments also are crucial for rigorous testing and verification of atmospheric science 

hypotheses under controlled and systematic conditions. The CLOUD and AIDA chambers in Europe 

have demonstrated the utility of large, collaborative 

laboratory experiments that simulate and unravel 

important aerosol and cloud processes. Establishing 

a similar facility in the United States could lead to a 

transformative leap in the understanding of aerosol 

and cloud formation.

Ecosystem Scale

In Situ Measurement Capabilities. Environmental system function is intimately tied to 
108 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

the biogeochemical cycling of major elements. 

Improved mechanistic understanding is needed, 

across multiple levels of spatial and temporal resolution, of the biogeochemical processes that control 

the transformations and mobility of carbon, nutrients, and contaminants within environmental systems and ecosystems critical to DOE. This improved 

understanding will enable prediction of environmental system response and, potentially, engineered 

controls of these systems. In particular, BER seeks to 

understand the controls that biogeochemical processes exert on nutrient and carbon cycling, sustainable biofuel production, and contaminant mobility 

from the molecular (Ångström or nanometer) to 

mesoscale (millimeter or meter), as well as the nonlinear scaling laws that control the effect of these 

smaller length–scale processes on larger length–

scale processes, which, ultimately, affect ecosystem, 

regional, and Earth system phenomena. There also 

is a need to illuminate and quantify biogeochemical interactions across multiple spatial scales, from 

nanometer- to micron-scale cellular metabolism and 

mineralogy to millimeter- and meter-scale community biogeochemical activity, to ecosystem-scale processes. These investigations necessitate quantified 

measurements, from nanometer- to meter-length 

scales, of soil carbon and nutrient flow, related biogeochemical processes, and multiomic characterization of microbial dynamics, along with integration 

of these data into predictive microbial metabolic and 

systems-ecology models.

Imaging technologies need to be developed and 

improved with spatial resolutions from the nanometer- to millimeter-length scales and subsecond temporal resolutions to study biogeochemical processes 

within environmental systems. These approaches 

will require a variety of probes with spatial and temporal resolutions relevant to the biogeochemical system being studied. These new and improved imaging 

technologies must enable researchers to understand 

and, ultimately, predict the dynamic interplay among 

the biotic and abiotic components of the environment from the molecular to mesoscale. Moreover, 

they must be able to use this new understanding to 

predict regional- and global-scale phenomena.

Hot spots and hot moments occur at a range of 

scales in response to widely ranging perturbations—

such as microbial metabolomic responses to diurnal 

cycling of root exudation or dissolved organic matter 

(DOM) inputs to riverine systems from wildfires. 

Understanding ecosystem responses to large perturbations requires rapid deployment of an array of 

field measurements. Future possibilities may include 

not only gathering important samples for later measurement in the laboratory, but also developing the 

next generation of experimental platforms to enable 

making those same measurements in the field. 

Whether using high-resolution mass spectrometers 

to measure DOM chemistry or an array of oxygen 

optodes to measure mixing of groundwater and surface waters, there is a need to capture the spatial and 

temporal variations in ecosystem responses to perturbations that are enabled only by extensive in-thefield measurements.

Environmental Observation. In situ observation of 

human health biomarkers (e.g., 24/7 monitoring of 

blood glucose levels) is now a reality in biomedicine. 

Analogous developments are envisioned in ecosystem science, enabling constant monitoring of plant 

metabolites in leaves and roots, organic matter composition in porewater, or quorum-sensing molecules 

in lakes. One can imagine, for instance, an array 

of radiofrequency tags installed across field sites 

reporting the onset of nutrient limitation or water 

stress, changes in redox state, or even the occurrence 

of disease or pest infestation.

Remote-sensing technologies also will play a key 

role in ecosystem monitoring, and the development 

of drone-deployable instruments for such applications will be a key direction for future work. These 

instruments will enable interrogations of both natural and managed environments on a larger scale, 

with implications for environmental and agricultural 

research. Such technology includes microsatellites, 

whose mass ranges from 10 to 100 kilograms (kg) 

(SpaceWorks 2017). They have become popular 

in recent years because of their affordability and 

potential capability for being launched as arrays 

of microsatellites with a single rocket (Selva and 
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Chapter 8 — Emerging Technologies

Krejci 2012). Microsatellites are increasingly used 

for Earth observation and remote sensing, making them potentially useful for many BER mission 

areas. Multiple microsatellites with Earth imaging 

equipment are already in orbit (Belward and Skøien 

2015). With projected launch costs ranging from 

$14,000 to $60,000 per kg, the number of microsatellites launched per year is expected to more 

than triple between 2016 and 2023 (SpaceWorks 

2017). Most of this growth is expected in the Earth 

observation area (SpaceWorks 2017), representing 

a unique opportunity for BER science to grow along 

with these new data streams. Collaboration between 

BER-funded scientists and the microsatellite development and launch community will be key for making this opportunity a reality.

Planet Scale

Multiscale Process-Level Understanding 

and Integration into Earth System Models.

Process-level understanding of aerosols, clouds, turbulence, atmospheric convective systems, and their 

interactions (see Fig. 8.1. Model Components of 

Land-Atmosphere Interactions, this page) is needed 

to advance knowledge and modeling of water, carbon, and energy cycles of the Earth system. This 

understanding also is needed to better predict future 

climate change and extreme atmospheric events 

that can significantly impact energy infrastructure. 

Such understanding could be gleaned from collocated, 3D measurements of the distributions of 

atmospheric aerosols, clouds, and coupled thermodynamic and dynamical fields, as well as larger-scale 

Fig. 8.1. Model Components of Land-Atmosphere Interactions. The evolution of the atmospheric boundary layer and convection initiation are critically dependent on land surface–atmosphere exchanges, including net radiation (FN), sensible heat 

flux (HS), latent heat flux (λE), and ground heat flux at the land surface (G). [Reprinted under a Creative Commons Attribution 

License (CC BY NC-ND) from Wulfmeyer, V., et al. 2015. “A Review of the Remote Sensing of Lower Tropospheric Thermodynamic Profiles and Its Indispensable Role for the Understanding and the Simulation of Water and Energy Cycles,” Reviews of 

Geophysics 53, 819–95. DOI: 10.1002/2014RG000476. © 2015 Wulfmeyer, Hardesty, Turner, Behrendt, Cadeddu, Di Girolamo, 

Schlüssel, Van Baelen, and Zus]
110 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

surface conditions covering meters for cloud-aerosol 

interactions to 100 kilometers (km) for mesoscale 

convective systems (MCSs; see Fig. 8.2a. Coupling 

of Multiscale Cloud Systems with Atmospheric Circulations and Turbulent Flows, p. 111). Currently 

lacking are such collocated 3D measurements that 

potentially could be obtained by several emerging 

technologies, including photoarray sensors, drones, 

and high-altitude balloons.

Most clouds are formed in turbulent flows in the 

presence of aerosols, and they strongly affect the 

Earth system’s radiative energy budget. Clouds, 

in turn, impact turbulent flows through their 

radiative impact and latent heating and cooling 

through the phase change of water. Turbulence 

is strongly modulated by environmental conditions in the atmosphere and at the surface. 

Aerosol-cloud-precipitation-turbulence interactions occur at spatial scales ranging from meters 

at which cloud particles are nucleated, to several 

hundred meters at which shallow cumulus clouds 

form, to several kilometers at which cumulus congestus and isolated deep convection occur, to tens 

of kilometers at which MCSs are organized, to 

hundreds of kilometers where MCSs are embedded 

(see Fig. 8.2b., p. 111). Cloud microphysics at the 

meter scale are known to have a significant impact 

on MCSs; simulating aerosol-cloud interactions and 

predicting high-impact atmospheric systems, therefore, require improved measurements and understanding across scales.

The scanning cloud radars used at DOE’s ARM 

Research Facility scan a plane slice of clouds in 

about 10 minutes. The temporal and spatial resolution of this sampling is not sufficient for capturing 

the 3D structural and temporal evolution of clouds. 

Phased-array Doppler radars, however, can take volume scans to significantly improve the sampling of 

cloud and precipitation systems. These radars have 

been used to study severe storms (Yoshida et al. 

2017). The phased-array radars should include an 

S-band to measure precipitation particles and an 

X-band to measure cloud particles to cover domain 

sizes comparable to the S-band radar coverage.

DOE recently used UASs built by the University 

of Colorado to take measurements of atmospheric 

pressure, temperature, humidity, and wind speed at 

low altitudes under limited meteorological conditions. Also built was a fixed-wing unmanned aerial 

vehicle with a 22-foot wingspan (ArcticShark) that 

can reach elevations of 3 to 6 km to take atmospheric measurements under limited meteorological conditions. The UAS capabilities should be 

significantly enhanced in four aspects to (1) enable 

measurement operations under all meteorological 

conditions; (2) provide coordinated measurements among multiple unmanned control systems 

and ground radars that can map 3D atmospheric 

motions and thermodynamics within and outside clouds; (3) use miniaturized instruments 

for measuring aerosols and cloud properties; and 

(4) enhance surface flux measurements of energy 

and water, as well as measurements of ecosystem 

and land surface properties.

DOE recently used tethered balloons at Oliktok 

Point in Alaska to measure atmospheric thermodynamic states and winds under limited meteorological conditions with altitudes up to 2 km above 

ground. This capability potentially could be significantly expanded by high-altitude, motor-propelled, 

solar-powered geostationary balloons in the 

stratosphere with profiling instruments attached to 

tethers. Several manufacturers make such balloons 

(e.g., TCOM, www.tcomlp.com; Raven Aerostar, 

ravenaerostar.com). Their use for tropospheric measurements of atmospheric moist processes is yet to 

be explored. Relative to the infrequent launch of 

radiosondes on weather balloons from the ground, 

these high-altitude balloons can provide continuous 

vertical profile sampling of atmospheric winds, temperature, and humidity, along with aerosol and cloud 

microphysical properties.

Modeling and Engineering

Modeling Single Cells. All mathematical models 

of cells are essentially models of single cells, with 

the summation of their range of behaviors giving 

rise to the population-level phenotype. Thus, a deep 
November 2017 Biological and Environmental Research Advisory Committee 111

Chapter 8 — Emerging Technologies

Fig. 8.2. Coupling of Multiscale Cloud Systems with Atmospheric Circulations and Turbulent Flows. (a) Conceptual 

model of a vertical cross section of a mesoscale convective system (labeled as A to B in panel b). (b) Conceptual model 

of the horizontal structure of a frontal system. [(a) Adapted and used with permission from Houze, R. A., Jr., et al. 1989. 

“Interpretation of Doppler Weather Radar Displays of Midlatitude Mesoscale Convective Systems,” Bulletin of the American 

Meteorological Society 70(6), 608–19. © American Meteorological Society (b) Adapted from Houze, R. A., Jr. 2014. “Clouds 

and Precipitation in Extratropical Cyclones.” In: Cloud Dynamics, Second Edition 104, 496 pp., with permission from Elsevier. 

© 2014 Academic Press]

Region of trailing

stratiform rain Region of heavy 

convective showers

R

Cloud base

Radar echo

boundary

Gust front Region of heavy 

stratiform rain

ESCENDING EAR INFLOW

ASCENDING FRONT-TO-REAR FLOW

Cloud top

Storm motion 0°C

*

L1

H2

L4

H1 Old 

cell

Mature 

cell

L2

New cell

L3

D

A B

Fig. 9.15

(a)

(b)
112 Biological and Environmental Research Advisory Committee November 2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

analysis of cell heterogeneity is required for building 

predictive models of cell communities. A particular challenge in building mathematical models of 

microbial cells, however, is the constraint of limiting 

ribosomal pools and other biosynthetic resources, 

especially at the low microbial growth rates typical 

of natural environments. This limitation can result in 

a pattern of competing reactions that changes with 

alterations in gene expression profiles. Understanding how microbes regulate biosynthetic resources 

within a dynamic environment and incorporating 

this knowledge into whole-cell models will be key to 

re-engineering their metabolic machinery without 

compromising their viability. Also important is moving metabolic models away from simple flux-balance 

models to more biophysically based models that 

incorporate protein abundance, feedback, and thermodynamics. This effort will provide a foundation 

for predicting the impact of alterations in gene 

expression or protein abundance on the cell’s physiological state.

Modeling Microbial Communities. Carbon 

dynamics within soil ecosystems are dominated by 

a web of biotic transformations mediated by photosynthetic and heterotrophic microbes and metazoans. Recognition of these dynamics has enabled 

consumer-resource models of food webs to (1) predict the quantitative effects of species removal on 

the remaining taxa within intertidal food webs (Berlow et al. 2009) and (2) forecast seasonal changes in 

microbial and metazoan carbon dynamics in a complex aquatic ecosystem (Boit et al. 2012). However, 

the cryptic nature of soil ecosystems has prevented 

understanding of soil food webs from developing 

as rapidly as that of more easily observed aquatic 

systems (Brose and Scheu 2014). With the advent of 

more powerful omics and isotopic techniques, the 

ability to more accurately model soil ecosystem carbon dynamics is rapidly increasing ( Jakobsen and 

Rosendahl 1990; Digel et al. 2014). For example, 

unique structural aspects of complex soil food webs 

have been identified (Riede et al. 2010), and the 

effects of temperature and moisture on the dynamics 

of simple soil food chains have been explored both 

theoretically (Binzer et al. 2012) and experimentally 

(Lang et al. 2012; Lang et al. 2014). The next challenge is to integrate simple components of soil food 

webs into more realistic and predictive networks by 

resolving resource flow, incorporating key additional 

processes, and integrating the ecological and biogeochemical roles of understudied groups such as 

viruses and microfauna (see also Grand Challenge 

4.1, p. 46).

Modeling Interactions. NWChem software contains scalable implementations of several classes of 

methodologies necessary to describe processes at 

various molecular scales. For example, computational protocols employing the molecular dynamics 

module of NWChem are used routinely in studies of 

small-molecule binding, protein-protein interactions, 

and processes occurring at protein-mineral interfaces. NWChem’s capabilities also have been used 

in high-throughput calculations for metabolomics 

and NMR. There is an urgent need for continuing 

this effort and extending NWChem’s capabilities 

across spatial and temporal scales. Among the most 

pressing needs are (1) development of methods 

of lower complexity for mesoscale systems (e.g., 

semiempirical methods, classical density functional 

theory, and embedding methods) and (2) development of HPC implementations of near-infinite 

run times, quantum mechanics (QM), and QM/

molecular mechanics methods to model biosystems 

at unprecedented scale. Theoretical methods and 

algorithms for extending time scales of dynamics simulations, complex dynamics for describing 

biomolecule-light interactions to support FEL experiments, methods for simulating long-range transport 

in biological systems, and algorithms for modeling 

the thermodynamics of metabolism are also necessary components of future computational frameworks needed to understand biological systems.

Improved Transport Modeling. Computational 

methods are critically needed to simulate various 

aspects of proton-controlled transport in biomass 

cellular materials, which are tightly coupled with 

cellular activities in response to biotic and abiotic 

stresses. Currently, proton-controlled transport 

simulations can only be performed using standard 
November 2017 Biological and Environmental Research Advisory Committee 113

Chapter 8 — Emerging Technologies

force fields that lack a fundamental description of 

proton-transfer processes. Truly predictive modeling 

of transport processes requires the use of accurate 

QM methods, adaptive force fields and molecular 

dynamics, and long-time conformational sampling 

at time scales of milliseconds.

Biosystems Design and Engineering. Advances 

in high-throughput biology and biotechnology 

have led to an array of biological insights in diverse 

organisms, the environment, medicine, agriculture, 

and energy supply. The breadth of genomic diversity 

endows organisms with rich biosynthetic potential 

and allows them to adapt to diverse environments 

(Venter et al. 2004; Tringe et al. 2005). Harnessing 

the potential of species diversity makes biological 

systems ideal for solving global challenges, such as 

generating biologically derived chemicals, fuels, and 

materials to ensure environmental sustainability 

(Way et al. 2014). Achieving these goals, however, 

requires not only a thorough understanding of 

biological systems, but also their safe and programmable control. The ability to modify genetic and 

biochemical molecules in cells, apply advanced computation to design and analyze engineered biosystems, and isolate engineered cells and communities 

with desired function remain defining challenges. 

These advances in biosystems design undoubtedly 

will play an important role in enabling a predictive 

understanding of biological systems relevant to 

energy supply, production of biologically derived 

molecules, and other DOE mission goals.

Biosystems design may provide a valuable approach 

for probing, studying, and introducing new functions into biological systems, and it is being fueled 

by concurrent advances in systems biology and 

the emerging field of synthetic biology. Synthetic 

biology combines principles from biology, chemistry, physics, mathematics, and engineering to 

assemble the biological tools necessary to redesign 

biological systems. More specifically, synthetic 

biology employs engineering principles to reduce 

genetics into DNA “parts,” so that those parts can 

be understood in isolation and reassembled into 

new biological parts, devices, and whole systems to 

build desired or expanded functions in living cells 

(see also sidebar, Synthetic Biology, p. 5). In many 

respects, synthetic organic chemistry serves as a 

model for the nascent field of synthetic biology. This 

new paradigm for biology is one in which a desired 

biological function is conceived, designed, and constructed to work as predicted, reliably and robustly 

using well-defined parts. Given the abundance of 

recent advances, synthetic biology is now poised 

both to make fundamental breakthroughs in the 

understanding of complex biological systems and 

to actualize an array of impactful applications that 

address global challenges in food and energy supply, 

environmental health, and medicine. New tools are 

required for the application of biosystems design 

methods to nonmodel organisms key to DOE mission areas, including unusual microbes, environmental isolates, algae, and plants, as well as microbial 

communities and plant-microbe interfaces. Biosystems design is now capable of producing up to 

billions of genotypic variants, leading to the need for 

improved methods to explore extensive phenotype 

landscapes and link genotype to phenotype. Because 

the safety of engineered biosystems is a concern, 

methodologies focusing on biocontainment and 

genetic isolation also constitute an important need.
114

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Biological and Environmental Research Advisory Committee November 2017
115

Appendices

November 2017 Biological and Environmental Research Advisory Committee

Appendices

Appendix A: Charge Letter ...................................................................................................................117

Appendix B: Grand Challenges Workshop Agenda............................................................................... 119

Appendix C: Grand Challenges Workshop Participants ........................................................................121

Appendix D: Fig. 2.2 Image Credits and Permissions ...........................................................................125

Appendix E: References ........................................................................................................................127

Appendix F: Acronyms and Abbreviations .............................................................................................137
116

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Biological and Environmental Research Advisory Committee November 2017
117

Appendix A – Charge Letter

November 2017 Biological and Environmental Research Advisory Committee

Appendix A: Charge Letter 
118

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Biological and Environmental Research Advisory Committee November 2017
119

Appendix B – Workshop Agenda

November 2017 Biological and Environmental Research Advisory Committee

Biological and Environmental Research Advisory Committee (BERAC)

Grand Challenges II Workshop 2017

Hilton Washington DC/Rockville Hotel

1750 Rockville Pike, Rockville, Maryland

Monday, March 6

8:30 a.m.–9:00 a.m. Stephen Binkley, Acting Director, U.S. Department of Energy (DOE) Office 

of Science: Overviewof DOE Office of Science

9:00 a.m.–9:30 a.m. Sharlene Weatherwax, Associate Director, DOE Office of Biological and 

Environmental Research; and Gary Stacey, BERAC Chair: Description of 

Meeting Goals and ExpectedOutcomes

9:30 a.m.–10:00 a.m. George Church: Synthetic Biology [Eisenhower Room]

10:00 a.m.–10:30 a.m. Inez Fung: Earth System Feedbacks [Eisenhower Room] 

10:30 a.m.–11:00 a.m. Richard Phillips: Biotic Interactions [Eisenhower Room]

11:00 a.m.–11:15 a.m. Break

11:15 a.m.–12:30 p.m. Breakout Groups I: Discussion

1. Earth System Research [Eisenhower Room]

2. Systems Biology Research [Monroe Room]

3. Microbial to Earth System Pathways [ Jackson Room]

12:30 p.m.–1:30 p.m. Lunch (on site)

1:30 p.m.–3:30 p.m. Breakout Groups I: Discussion and Synthesis

1. Earth System Research [Eisenhower Room]

2. Systems Biology Research [Monroe Room]

3. Microbial to Earth System Pathways [ Jackson Room]

3:30 p.m.–3:45 p.m. Break

3:45 p.m.–5:00 p.m. Report from Breakouts; Compare and Contrast Among Focus Groups

5:30 p.m. Adjourn

Appendix B: Grand Challenges Workshop Agenda
120

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Biological and Environmental Research Advisory Committee November 2017

Tuesday, March 7

9:00 a.m.–9:30 a.m. Steven Davis: Energy Sustainability [Eisenhower Room]

9:30 a.m.–10:00 a.m. Shibu Yooseph: Big Data and Computing [Eisenhower Room]

10:00 a.m.–10:30 a.m. Cullen Buie: Micro- and Nanoengineering [Eisenhower Room]

10:30 a.m.–10:45 a.m. Break

10:45 a.m.–12:30 p.m. Breakout Groups II: Discussion

1. Energy Sustainability Across Relevant Scales [Monroe Room]

2. Needs and Applications of Big Data and Computing [ Jackson Room]

3. Identifying Applications of Emerging Technologies in BER-Relevant 

Research (e.g., Nanotechnology) [Eisenhower Room]

12:30 p.m.–1:30 p.m. Lunch (on site)

1:30 p.m.–3:30 p.m. Breakout Groups II: Discussion and Synthesis

1. Energy Sustainability Across Relevant Scales [Monroe Room]

2. Needs and Applications of Big Data and Computing [ Jackson Room]

3. Maximizing User Facilities and Experimental Sites [Eisenhower Room]

3:30 p.m.–3:45 p.m. Break

3:45 p.m.–5:00 p.m. Reports from Breakouts; Compare and Contrast Among Focus Groups

5:30 p.m. Adjourn

WRITERS’ AGENDA

(Selected Writing Team Only)

Tuesday Evening, March 7

Writers’ Dinner: Buffet at Rockville Hilton

Discuss Wednesday schedule and writing plan. Review respective breakout 

reports, meeting notes, and discussion notes to prepare for writing portion.

Wednesday, March 8

9:00 a.m.–10:00 a.m. Produce outlines for each section. 

10:00 a.m.–11:00 a.m. Compile all outlines and edit to complete report outline. [Eisenhower Room]

11:00 a.m.–12:00 p.m. Return to groups for completing outline and writing. 

12:00 p.m.–1:00 p.m. Lunch (on site)

1:00 p.m.–4:00 p.m. Continue writing. Complete full outline, introduction, upcomingchallenges,

and recommendations for implementation.
121

Appendix C – Workshop Participants

November 2017 Biological and Environmental Research Advisory Committee

Steven Allison

University of California, Irvine

Sarah Assmann

Pennsylvania State University

Inês Azevedo

Carnegie Mellon University

Dennis Baldocchi

University of California, Berkeley

Mark Bradford

Yale University

Amy Brunner

Virginia Polytechnic Institute and 

State University

Cullen Buie

Massachusetts Institute of Technology

Katherine Calvin

Joint Global Change Research Institute 

(Pacific Northwest National Laboratory 

and University of Maryland)

Patrick Chain

Los Alamos National Laboratory

George Church

Harvard University

Elsa Cleland

University of California, San Diego

Steven Davis

University of California, Irvine

Scott Denning

Colorado State University

Adam Deutschbauer

Lawrence Berkeley National Laboratory

Wei Ding

University of Massachusetts, Boston

Appendix C: Grand Challenges Workshop Participants

Mary Dunlop

Boston University

Jim Ehleringer

University of Utah

Graham Feingold

National Oceanic and Atmospheric 

Administration

Mary Firestone

University of California, Berkeley

Ian Foster

Argonne National Laboratory

Inez Fung

University of California, Berkeley

James Hack

Oak Ridge National Laboratory

Alex Held

Commonwealth Scientific and Industrial 

Research Organisation (Australia)

Kirsten Hofmockel

Pacific Northwest National Laboratory

Susan Hubbard

Lawrence Berkeley National Laboratory

Bruce Hungate

Northern Arizona University

Charles Jackson

University of Texas

Anthony Janetos

Boston University

Andrzej Joachimiak

Argonne National Laboratory

Ravi Kane

Georgia Institute of Technology
122

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Biological and Environmental Research Advisory Committee November 2017

Kerstin Kleese van Dam

Brookhaven National Laboratory

Cheryl Kuske

Los Alamos National Laboratory

Paul Langan

Oak Ridge National Laboratory

Ruby Leung

Pacific Northwest National Laboratory

Jonathan Lynch

University of California, Davis

Costas Maranas

Pennsylvania State University

Christopher Marx

University of Idaho

Sean McSweeney

Brookhaven National Laboratory

Gerald Meehl

National Center for Atmospheric Research

Claire Monteleoni

The George Washington University

Gloria Muday

Wake Forest University

Michelle O’Malley

University of California, Santa Barbara

Victoria Orphan

California Institute of Technology

Jennifer Pett-Ridge

Lawrence Livermore National Laboratory

Richard Phillips

Indiana University Bloomington

Benjamin Preston

Rand Corporation

David Randall

Colorado State University

James Randerson

University of California, Irvine

Patrick Reed

Cornell University

Karin Remington

Computationality, LLC

G. Philip Robertson

Michigan State University

Ramalingam Saravanan

Texas A&M University

Karen Schlauch

University of Nevada, Reno

C. Adam Schlosser

Massachusetts Institute of Technology

Daniel Segrè

Boston University

John Shanklin

Brookhaven National Laboratory

Zengyi Shao

Iowa State University

Gary Stacey

University of Missouri

David Stahl

University of Washington

Dáithí Stone

Lawrence Berkeley National Laboratory

Joao Teixeira

National Aeronautics and Space Administration 

Jet Propulsion Laboratory

Kathleen Treseder

University of California, Irvine

Susannah Tringe

U.S. Department of Energy Joint Genome 

Institute

Keith Tyo

Northwestern University

Akos Vertes

The George Washington University
123

Appendix C – Workshop Participants

November 2017 Biological and Environmental Research Advisory Committee

Judy Wall

University of Missouri

Michael Wehner

Lawrence Berkeley National Laboratory

Jim Werner

Los Alamos National Laboratory

John Weyant

Stanford University

Will Wieder

National Center for Atmospheric Research

Shibu Yooseph

University of Central Florida

Mark Zelinka

Lawrence Livermore National Laboratory

Minghua Zhang

State University of New York at Stony Brook 

Huimin Zhao

University of Illinois at Urbana-Champaign
124

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Biological and Environmental Research Advisory Committee November 2017
125

Appendix D – Image Credits

November 2017 Biological and Environmental Research Advisory Committee

Figure from BER. 2017. Technologies for Characterizing Molecular and Cellular Systems Relevant to 

Bioenergy and the Environment, DOE/SC-0189, U.S. 

Department of Energy Office of Biological and Environmental Research. [science.energy.gov/~/media/

ber/pdf/community-resources/Technologies_for_

Characterizing_Molecular_and_Cellular_Systems]

Row 1, Image 1. Nitrogenase. Courtesy David S. 

Goodsell and the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank.

Row 1, Image 2. PF1205. Reprinted by permission from Macmillan Publishers Ltd.: Hura, G. L., 

et al. 2009. “Robust, High-Throughput Solution 

Structural Analyses by Small Angle X-Ray Scattering 

(SAXS),” Nature Methods 6, 606–12. © 2009

Row 1, Image 3. Comprehensive imaging of 

densely packed transmembrane proteins using 

photoactivated localization microscopy (PALM). 

Courtesy Greenfield, D., et al. 2009. “Self-Organization of the Escherichia coli Chemotaxis Network 

Imaged with Super-Resolution Light Microscopy,” 

PLoS Biology 7(6), e1000137. 

Row 1, Image 4. Confocal microscopy image of 

bacteria on the surface of poplar roots. Courtesy 

Jennifer L. Morrell-Falvey, Oak Ridge National 

Laboratory.

Row 1, Image 5. Scanning electron microscopy 

(SEM) image of a sulfate-reducing biofilm 

obtained from a borehole used for long-term 

(100+ days) acetate injection during biostimulation activities at the U.S. Department of 

Energy's (DOE) Integrated Field Research Challenge site near Rifle, Colorado. Courtesy Alice C. 

Dohnalkova, Pacific Northwest National Laboratory. 

Biofilm material courtesy Kenneth H. Williams, 

Lawrence Berkeley National Laboratory.

Row 1, Image 6. Positron-emission tomography 

(PET) image showing uptake of radioactive 

nitrate in a poplar sapling. Courtesy Richard Ferrieri, University of Missouri.

Row 1, Image 7. X-ray computed tomography 

cross section of an active layer/permafrost soil 

core from DOE's Office of Biological and Environmental Research Next-Generation Ecosystem 

Experiments–Arctic project. Courtesy Tim Neafsey, Lawrence Berkeley National Laboratory.

Row 2, Image 1. Haem-copper active site in 

respiratory enzymes. Reprinted by permission 

from Macmillan Publishers Ltd.: Hura, G.L., et al. 

2009. “The Octahaem MccA is a Haem c–Copper 

Sulfite Reductase,” Nature 520, 706–09. © 2015

Row 2, Image 2. Density difference map for the 

ensemble structure (mFobs − DFmodel)exp[iφmodel]. 

Reprinted under a Creative Commons Attribution 

License (CC BY 4.0) from Burnley, B. T., et al. 2012. 

“Modelling Dynamics in Protein Crystal Structures 

by Ensemble Refinement,” eLIFE 1, e00311.

Row 2, Image 3. Acetate consumption over time 

of Psychrobacter aestuarii and Geobacter sulfurreducens in axenic cultures and co-cultures. 

Courtesy Alice C. Dohnalkova, Pacific Northwest 

National Laboratory.

Row 2, Image 4. Transmission electron microscopy (TEM) image of Escherichia coli O157:H7. 

Courtesy Centers for Disease Control and 

Prevention.

Row 2, Image 5. Plant root nutrient uptake.

Courtesy Jennifer Pett-Ridge and Erin Nuccio, 

Lawrence Livermore National Laboratory.

Row 2, Image 6. Deconstructed biomass. 

Courtesy National Renewable Energy Laboratory 

Biomass Structural Characterization Laboratory.

Row 2, Image 7. Engineered bacteria produce 

biodiesel. Courtesy Joint BioEnergy Institute, 

Lawrence Berkeley National Laboratory.

Appendix D: Fig. 2.2 Image Credits and Permissions 
126

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Appendix E – References

November 2017 Biological and Environmental Research Advisory Committee

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137

Appendix F – Acronyms and Abbreviations

November 2017 Biological and Environmental Research Advisory Committee

Appendix F: Acronyms and Abbreviations

2D, 3D two-dimensional, three-dimensional

AEC Atomic Energy Commission 

AIM PNNL Analysis in Motion Initiative

ARM DOE Atmospheric Radiation Measurement 

Research Facility

ASCR DOE Office of Advanced Scientific Computing 

Research

BER DOE Office of Biological and Environmental 

Research

BERAC Biological and Environmental Research 

Advisory Committee

BES DOE Office of Basic Energy Sciences

BNL Brookhaven National Laboratory

Cas CRISPR-associated endonuclease 

Cas9 CRISPR-associated protein-9 nuclease 

CESD BER Climate and Environmental Sciences 

Division

CESM Community Earth System Model 

CESMS Center for Energy Sustainability Modeling 

and Synthesis

CF Climate and Forecast ontology 

CH4 methane 

cm centimeter 

CMIP Coupled Model Intercomparison Project

CO2 carbon dioxide 

CRISPR clustered regularly interspaced short 

palindromic repeats

cryoEM cryo-electron microscopy 

cryoET cryo-electron tomography

DOE U.S. Department of Energy 

DOM dissolved organic matter 

E3SM Energy Exascale Earth System Model

EMSL DOE Environmental Molecular Sciences 

Laboratory

EOS experimental and observational science

EPR electron paramagnetic resonance

EPS extracellular polymeric substance

ESGF Earth System Grid Federation

ESM Earth system model 

FACE Free-Air CO2 Enrichment site

FEL free-electron laser 

FICUS Facilities Integrating Collaborations for User 

Science program

FISH fluorescence in situ hybridization

FTIR Fourier transform infrared spectroscopy

GHG greenhouse gas

gRNA guide RNA

GWAS genome-wide association study

H2S hydrogen sulfide 

HEP DOE Office of High Energy Physics

HFIR ORNL High Flux Isotope Reactor

HFSP Human Frontier Science Program

HPC high-performance computing

IAM integrated assessment model 

iESM integrated ESM 

IFL integrated field laboratory 

IPO interdecadal Pacific oscillation 

IR s-SNOM infrared scattering-type scanning near-field 

optical microscopy

JGI DOE Joint Genome Institute 

KBase DOE Systems Biology Knowledgebase

kg kilogram

km kilometer

LCA lifecycle analysis

LCLS SLAC Linac Coherent Light Source

LES large eddy simulation 

MCS mesoscale convective system

microED micro-electron diffraction

mRNA messenger RNA 

MS mass spectrometry

MX macromolecular X-ray crystallography

N2O nitrous oxide 

NanoSIMS nanoscale secondary ion mass spectrometry

NASA National Aeronautics and Space 

Administration

NCBI NIH National Center for Biotechnology 

Information

NERSC National Energy Research Scientific 

Computing Center

NEST Network of Energy Sustainability Testbeds

NGEE Next-Generation Ecosystem Experiments 

NIH National Institutes of Health

NIMBioS National Institute for Mathematical and 

Biological Synthesis

NMR nuclear magnetic resonance

NP DOE Office of Nuclear Physics

NSF National Science Foundation

NSLS-II BNL National Synchrotron Light Source II

NSRC DOE Nanoscale Science Research Center 

NWP numerical weather prediction 

NX neutron macromolecular crystallography
138

Grand Challenges for Biological and Environmental Research: Progress and Future Vision

Biological and Environmental Research Advisory Committee November 2017

ORNL Oak Ridge National Laboratory

PanDA Production and Distributed Analysis project

PB petabyte

PCR polymerase chain reaction 

PI principal investigator

PNNL Pacific Northwest National Laboratory

QM quantum mechanics 

rRNA ribosomal RNA 

SANS small-angle neutron scattering

SAXS small-angle X-ray scattering

SFA BER Scientific Focus Area

sgRNA single-guide RNA 

SLAC SLAC National Accelerator Laboratory

SNS ORNL Spallation Neutron Source

SOA secondary organic aerosol 

SOM soil organic matter

SPRUCE Spruce and Peatland Responses Under 

Changing Environments

TEM transmission electron microscopy

UAS unmanned aerial system 

μm micron

XFEL X-ray FEL
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