Suggested citation for this report: BERAC. 2010. Grand Challenges for Biological and Environmental Research: 

A Long-Term Vision; A Report from the Biological and Environmental Research Advisory Committee March 2010 

Workshop, DOE/SC-0135, BERAC Steering Committee on Grand Research Challenges for Biological and 

Environmental Research (www.science.doe.gov/ober/berac/BER_LTVreport.pdf).

Cover 

Images on the cover 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: 

A Long-Term Vision

A Report from the 

Biological and Environmental Research Advisory Committee

March 2010 Workshop

Chair

Gary Stacey (University of Missouri)

U.S. Department of Energy

December 2010

Prepared by the BERAC Steering Committee on 

Grand Research Challenges for Biological and Environmental Research

Report available online at www.science.doe.gov/ober/berac/BER_LTVreport.pdf

DOE/SC-0135
Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop iii

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

Chair 

Gary Stacey 

University of Missouri

Janet Braam 

Rice University

Robert E. Dickinson

University of Texas at Austin

James R. Ehleringer

University of Utah

Joanna S. Fowler

Brookhaven National Laboratory

Paul Gilna

Oak Ridge National Laboratory

Susan Hubbard

Lawrence Berkeley National Laboratory

Andrzej Joachimiak

Argonne National Laboratory

Ruby Leung

Pacific Northwest National Laboratory

Gerald ( Jay) Mace

University of Utah

Stephen R. Padgette

Monsanto Company

Joyce E. Penner

University of Michigan

Gregory Petsko

Brandeis University

David A. Randall

Colorado State University

Karin Remington

National Institute of General Medical Sciences

Margaret A. Riley

University of Massachusetts

G. Philip Robertson

Michigan State University

Gary Sayler

University of Tennessee

Herman Shugart

University of Virginia

James Tiedje

Michigan State University

Judy D. Wall

University of Missouri

Warren M. Washington

National Center for Atmospheric Research

Raymond E. Wildung

Pacific Northwest National Laboratory

Mavrik Zavarin

Lawrence Livermore National Laboratory

Minghua Zhang 

State University of New York at Stony Brook

Designated Federal Officer

David Thomassen

U.S. Department of Energy Office of Biological 

and Environmental Research

Biological and Environmental Research Advisory Committee 
iv Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

Gary Stacey

Chair, University of Missouri

James R. Ehleringer

University of Utah

Joyce E. Penner

University of Michigan

Gregory Petsko

Brandeis University

David A. Randall

Colorado State University

Margaret A. Riley

University of Massachusetts

Gary Sayler

University of Tennessee

Mavrik Zavarin

Lawrence Livermore National Laboratory

Workshop Steering Committee
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop v

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

Contributing Authors

Chapter 2: Grand Challenges in Biological Systems

Adam Arkin, Nitin Baliga, Janet Braam, George Church, Jim Collins, Robert Cottingham, Joseph Ecker, 

Mark Gerstein, Paul Gilna, Jean Greenberg, Peter Greenberg, Jo Handelsman, Susan Hubbard, 

Andrzej Joachimiak, James Liao, Loren Looger, Elliot Meyerowitz, Eric Mjolness, Greg Petsko, Gary Sayler, 

Michael Simpson, Gary Stacey, Michael Sussman, and James Tiedje

Chapter 3: Grand Challenges in Climate Research

David Bader, Paola Cessi, William Collins, Scott Denning, Robert Dickinson, David Easterling, Jae Edmonds, 

Johannes Feddema, Christopher Field, Ann Fridlind, Inez Fung, Isaac Held, Robert Jackson, Anthony Janetos, 

William Large, Margaret Leinen, Ruby Leung, Stephen Long, Gerald Mace, Carrie Masiello, Gerald Meehl, 

Donald Ort, Bette Otto-Bliesner, Joyce Penner, Michael Prather, David Randall, Philip Rasch, Edwin Schneider, Herman Shugart, Peter Thornton, Warren Washington, Raymond Wildung, Warren Wiscombe, 

Donald Zak, and Minghua Zhang

Chapter 4: Grand Challenges in Energy Sustainability

Jeffrey Bielicki, Marilyn Buford, Elsa Cleland, Virginia Dale, Cliff Duke, James Ehleringer, Alan Hecht, 

Daniel Kammen, Ruby Leung, Gregg Marland, Diane Pataki, Margaret Riley, and Philip Robertson

Chapter 5: Grand Challenges in Computing for Biological and Environmental Research

Robert Cottingham, Paul Gilna, Susan Hubbard, Joyce Penner, and David Randall

Chapter 6: Grand Challenges in Education and Workforce Training

Jo Handelsman and Margaret Riley
vi Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Grand Challenges for Biological and Environmental Research: A Long-Term Vision
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop vii

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

Table of Contents

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

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

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

Advancing Complex Systems Science........................................................................................................................3

Defining a Long-Term Scientific Vision for BER .......................................................................................................3

2. Grand Challenges in Biological Systems................................................................................... 5

Summary of Research Recommendations.....................................................................................................................6

Systems Biology..................................................................................................................................................................5

Synthetic Biology ...............................................................................................................................................................7

Importance of In Situ Analyses for DOE Biological Systems...............................................................................9

DOE Relevance and Potential Impact of Systems and Synthetic Biology...................................................11

Grand Challenge Research Recommendations...................................................................................................12

Enabling Predictive Biology..........................................................................................................................................13

Measuring and Analyzing Biological Systems........................................................................................................17

Exploring Ecosystem Function and Elemental Cycling.......................................................................................19

3. Grand Challenges in Climate Research ................................................................................... 25

Summary of Research Recommendations..................................................................................................................26

Grand Challenge Research Recommendations...................................................................................................26

Earth System Models.......................................................................................................................................................26

Cloud and Aerosol Processes........................................................................................................................................28

Ocean and Terrestrial Processes..................................................................................................................................29

Biological Processes.........................................................................................................................................................30

Human Interactions.........................................................................................................................................................33

Observing Systems...........................................................................................................................................................33

Important Research Payoffs........................................................................................................................................34

Ensuring a Competent Workforce.............................................................................................................................34

4. Grand Challenges in Energy Sustainability............................................................................ 37

Summary of Research Recommendations..................................................................................................................38

Grand Challenge Research Recommendations...................................................................................................37

Outlining the Challenges...............................................................................................................................................37

Energy Sustainability Research Recommendations.............................................................................................39

5. Grand Challenges in Computing for Biological and Environmental Research................ 43

Summary of Research Recommendations..................................................................................................................44

Grand Challenge Research Recommendations...................................................................................................44

6. Grand Challenges in Education and Workforce Training..................................................... 51

Summary of Recommendations....................................................................................................................................52

Grand Challenge Education and Training Recommendations.......................................................................53
viii Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

Appendix 1: Charge Letter............................................................................................................. 55

Appendix 2: Grand Challenges Workshop Agenda................................................................... 57

Appendix 3: Grand Challenges Workshop Participants ........................................................... 59

Appendix 4: Bibliography .............................................................................................................. 61
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop ix

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

Executive Summary

The interactions and feedbacks among 

plants, animals, microbes, humans, and the 

environment ultimately form the world in 

which we live. This world is now facing challenges 

from a growing and increasingly affluent human 

population whose numbers and lifestyles are driving 

ever greater energy demand and impacting climate. 

These and other contributing factors will make 

energy and climate sustainability extremely difficult 

to achieve over the 20-year time horizon that is the 

focus of this report. Despite these severe challenges, 

there is optimism that deeper understanding of our 

environment will enable us to mitigate detrimental 

effects, while also harnessing biological and climate 

systems to ensure a sustainable energy future.

This effort is advanced by scientific inquiries in 

the fields of atmospheric chemistry and physics, 

biology, ecology, and subsurface science—all made 

possible by computing. The Office of Biological 

and Environmental Research (BER) within the 

Department of Energy’s (DOE) Office of Science 

has a long history of bringing together researchers from different disciplines to address critical 

national needs in determining the biological and 

environmental impacts of energy production and 

use, characterizing the interplay of climate and 

energy, and collaborating with other agencies and 

DOE programs to improve the world’s most powerful climate models. BER science focuses on three 

distinct areas:

• What are the roles of Earth system components 

(atmosphere, land, oceans, sea ice, and the biosphere) in determining climate?

• How is the information stored in a genome translated into microbial, plant, and ecosystem processes that influence biofuel production, climate 

feedbacks, and the natural cycling of carbon?

• What are the biological, geochemical, and physical forces that govern the behavior of Earth’s 

subsurface environment?

Ultimately, the goal of BER science is to support 

experimentation and modeling that can reliably 

predict the outcomes and behaviors of complex biological and environmental systems, leading to robust 

solutions for DOE missions and strategic goals.

In March 2010, the Biological and Environmental 

Research Advisory Committee held the Grand 

Challenges for Biological and Environmental Research: 

A Long-Term Vision workshop to identify scientific 

opportunities and grand challenges for BER science 

in the coming decades and to develop an overall 

strategy for drafting a long-term vision for BER. 

Key workshop goals included:

• Identifying the greatest scientific challenges in 

biology, climate, and the environment that DOE 

will face over a 20-year time horizon.

• Describing how BER should be positioned to 

address those challenges.

• Determining the new and innovative tools 

needed to advance BER science.

• Suggesting how the workforce of the future 

should be trained in integrative system science.

This report lays out grand research challenges 

for BER—in biological systems, climate, energy 

sustainability, computing, and education and 

workforce training—that can put society on a path 

to achieve the scientific evidence and predictive 

understanding needed to inform decision making and planning to address future energy needs, 

climate change, water availability, and land use.
x Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Executive Summary

Cross-Cutting Science Themes

Common science themes arose across the workshop’s breakout sessions. These themes include 

complex systems science across scales, multidisciplinary research, computing and mathematics, and 

human impacts.

Complex Systems Science Across Scales

Spanning BER science is the need to understand 

complex biological and environmental systems 

over many spatial and temporal scales, from 

molecular to global and nanoseconds to centuries. 

Although capabilities for measuring physical, 

chemical, and biological parameters have greatly 

advanced, science still struggles to make meaning 

from these data, to integrate the parts into an 

understandable whole, and to move efficiently to 

predictions of biological and environmental 

outcomes. The biological and climate sciences are 

moving from piecemeal studies of individual 

components to a point where a whole system–

level of understanding is possible and necessary to 

address energy and climate challenges.

Increasingly clear is that the components of our 

environment are interconnected. Just as animals, 

plants, and communities of organisms are made up 

of many highly interactive individual cells, the Earth 

is composed of numerous interacting processes—

from biological systems to atmospheric chemistries 

to oceans to global water cycles—that are governed 

by complex feedforward and feedback pathways. 

Our ability to model and predict the impacts of 

environmental and Earth system change is critical 

to our eventual understanding of this complexity and the development of potential solutions. 

For example: How do changes at the molecular 

level influence the ability of a cell or multicellular 

organism to respond to and impact its environment? How does the environment respond to the 

altered behavior of biological systems? How do 

these environmental changes interact with climate 

to mitigate or accelerate climate change? These 

questions become even more daunting when we try 

to extrapolate from the ancient record or to predict 

impacts into the future over large time scales. Yet 

these efforts are necessary for understanding the 

potential severity and impacts of climate change.

Multidisciplinary Research

Addressing the issues of complexity and scalability 

necessitates the intense interaction and integration 

of data and knowledge emanating from a broad 

array of scientific disciplines. A tighter coupling of 

theory, observations, experiments, models, and simulations developed across disciplinary interfaces is 

key to the deeper systems understanding required. 

As more sophisticated models are developed, we 

will be able to test their consistency with laboratory experiments and forecast that which we are 

unable to do experimentally. New hypotheses will 

be drawn from model results, enabling the design of 

new rounds of experiments that refine concepts to 

enable the predictive capabilities required for solutions to DOE mission challenges.

Computing and Mathematics

Computing and mathematics underlie the science 

required to address the grand challenges identified 

in this report (summarized on the following pages). 

As common models of scientific understanding are 

developed from the growing body of data, these 

approaches and new knowledge will be captured by 

computational technologies. However, the explosion 

in data, emanating from diverse methods, presents 

severe challenges for storage, integration, analysis, 

and, most important, understanding. New paradigms clearly will be required. Similarly, as the science progresses, there is an equally pressing need for 

the education and training of scientists to keep pace.

Human Impacts

The roles that humans play are important determinants of and vital contributors to global sustainability. By 2100, the world’s population is projected 

to increase 40% over today’s level, and this growing 
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop xi

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

population will simultaneously be seeking a higher 

standard of living. The rapid pace of anthropogenic 

change requires that human impacts be factored into 

assessments if we are to have an accurate understanding of the Earth system 20 years from now. 

Likewise, ecosystem changes lead to changes in patterns of human behavior and activity. Therefore, any 

comprehensive Earth system model (ESM) must 

incorporate human activities, which by necessity 

include an analysis of socioeconomic factors.

Summary of Grand Challenge 

Research Recommendations

The vision and research challenges outlined as follows establish an initial framework of ideas that are 

expected to be further refined at follow-up workshops engaging the scientific community.

Grand Challenges in Biological Systems

As modern experimental and computational tools 

rapidly transform biology from an observational 

to an informational, data-intensive science, one 

manifestation is the expansion of the complementary fields of systems and synthetic biology. While 

systems biology provides the approaches needed to 

address biological complexity and achieve a systemslevel understanding, synthetic biology tests understanding to see if a system functions as expected. 

Then such validated understanding potentially could 

be used to design and construct novel biological 

systems for purposes relevant to DOE missions.

Systems biology is a comprehensive, quantitative analysis of the manner in which components of a biological system interact functionally over time and space. 

By exploring how all components work together 

as a functional system, systems biology enables the 

discovery of the organizing principles and emergent 

properties that can be seen only via a systemic view of 

the entire biological process. An added complexity is 

understanding microbial and plant systems within the 

context of dynamic, real-world environments.

High-throughput technologies used in systems 

biology research are generating immense datasets 

previously unheard of in biology. These advances 

demand improvements in the management of large 

bioinformatic knowledgebases and the development of a new generation of computational tools 

that will support spatial and temporal modeling.

Synthetic biology is essentially a tool chest of 

methods and approaches for understanding natural 

systems using simplified or controllable analogs 

such as “minimal cell architectures,” genetic circuits, 

metabolic networks, and constructed metabolic 

pathways. Coupled with computational modeling 

analogous to electronic circuit design, synthetic 

biology can enable the design and construction of 

new bioinspired materials, organisms, pathways, 

or information-processing systems. Key issues 

involve characterizing what nature already has 

assembled and determining how to access, harness, 

and improve biological components or processes 

to meet future challenges or understand system 

responses to change.

Significant advances in analytical methodology, 

particularly mass spectrometry, now provide capacity for near high-throughput environmental analysis 

of biological materials, including proteins and small 

information molecules, giving rise to the rapidly 

expanding field of infochemicals. New biologically 

inspired research in this area can take advantage of 

leadership-class DOE computational and analytical 

characterization capabilities.

Integrating systems and synthetic biology approaches 

with conceptual and numerical models is helping to 

build connections from genome- and molecular-level 

research to investigations at physiological and ecosystem levels. Research needs range from systemslevel understanding at the cellular level to challenges 

associated with investigating systems of increasing 

biological complexity.
xii Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Executive Summary

Workshop participants identified the following 

grand challenge research recommendations for 

biological systems:

• Enabling predictive biology.

– Develop a simulation model of a single cell 

for accurately predicting phenotype from 

genotype-environment interactions.

– Establish new model organisms for relevant 

ecological process understanding.

– Use robust biochemical, functional, and 

experimental evidence to enhance genome 

and metagenome annotation.

– Develop the biological understanding for 

generalizing and applying models from simple 

systems to more complex systems, working 

toward an ultimate goal of modeling the evolution and dynamics of a complex biological 

system under environmental stress conditions.

– Identify general design principles used in 

natural systems, articulate these, and then use 

them to distill our understanding of biology.

• Measuring and analyzing biological systems.

– Apply advanced computational and analytical capabilities to characterize the information molecules and network interactions 

used by biological systems.

– Improve capabilities for imaging a single cell 

at a resolution of one molecule per cell.

– Measure microbial processes and interactions in the real world and in experimental 

simulations.

– Provide standardizable, reproducible sampling protocols and observations enabling 

functional characterizations of systems and 

synthetic design.

– Define the range of analytes, ligands, and 

fluxes of materials to be measured.

– Define scalability requirements for measurement technologies and develop capabilities 

for in situ and in vitro sensors.

• Exploring ecosystem function and elemental 

cycling.

– Understand, predict, and manipulate the 

types and rates of ecosystem responses and 

feedbacks that result from and influence 

climate change.

– Deploy synthetic (or nonsynthetic) biology 

to understand and manipulate ecosystem 

function.

– Determine the molecular basis of robustness, 

fitness, and selection.

– Develop a complete understanding of the 

biogeochemical cycles important to regulating carbon flux through biological systems.

– Apply functional metagenomics to enable 

mass balance closure for biogeochemical 

cycles and transfer this information to biosystem design.

– Manage plant and microbe stress response to 

control carbon biosequestration and remediation of metals and radionuclides.

– Define the fundamental microbial basis for 

permafrost carbon-methane transformations.

– Develop designs for optimizing carbon flow 

for biomass production, carbon allocation, 

and biosequestration to reduce rates of atmospheric CO2 accumulation and to increase 

terrestrial carbon storage by 50% in 20 years.

– Determine carbon and nutrient dynamics in 

natural systems.

Grand Challenges in Climate Research

Twenty years from now, observation and modeling 

systems will produce much more detailed analyses 

and predictions of both weather and climate. Included 

will be additional regional-scale diagnostics such as 

temperature, precipitation, and extreme weather 

event statistics. ESMs will also become more 

effective at finer scales and for shorter time periods.
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop xiii

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

These dramatic increases in model resolution—

enabled in part by exponential increases in computing power—will result in approximately 1-km 

global grid spacing for the atmosphere and 1- to 

5-km resolution for oceans. Such resolution is comparable to the scale at which many observational 

measurements are made. Forthcoming observational technologies and tools will describe Earth 

system processes in greater detail, enabling more 

robust models of their effects on climate, as well as 

the degree to which change in the climate system 

affects these processes. These processes include ice 

sheet and glacier dynamics; clouds, aerosols, and 

precipitation; global biogeochemical cycling of 

carbon, water, and other natural compounds; soils 

and terrestrial vegetation, including human decision making about crop choices and management; 

groundwater resources; biological interactions and 

feedbacks; anthropogenic greenhouse gas emissions; and interactions among all of these processes.

Comprehensive comparisons of ESM predictions 

with observations will be possible, as well as quantification of model errors. However, breakthrough 

developments are needed to determine how to 

translate information about disparate, coupled 

processes and phenomena across scales—from the 

molecular and pore scale to the local, regional, or 

global scale of the prediction. ESMs are also complex in terms of their conceptual basis, embodiment 

as computer programs, and sheer numbers of variables and parameters. New approaches are needed 

to deal with this complexity, especially for analyzing 

and understanding model results.

Decadal projections will be an important outcome 

of these emerging models. These forecasts will 

capture El Niños and decadal modes of climate 

variability and their influences on extreme weather. 

They also will quantify regional time-evolving 

climate change to inform decision makers balancing options about mitigation of and adaptation to 

climate change with other societal needs. Emerging 

ESMs will be used for predicting not only climate 

change, but also the consequences of particular 

societal choices on ecosystems. Needed developments include tools that predict energy consumption and the consequences of energy use, as well as 

tools that support energy infrastructure planning.

Workshop participants identified the following 

grand challenge research recommendations for 

climate science:

• Develop higher-resolution models to integrate 

many more relevant processes than current models and to describe climate change over much 

longer time scales.

• Improve parameterizations and basic knowledge 

about aerosols that affect clouds and factors 

that control their number and concentration for 

microphysics, radiative transfer, and turbulence 

processes to quantify indirect aerosol forcing and 

resulting precipitation changes.

• Develop ecosystem-observing systems to 

monitor biogeochemical cycles, estimate critical 

process parameters, and provide model tests 

in ocean and terrestrial biospheres, including 

subsurface soils.

• Advance understanding of important biological 

interactions and feedbacks to identify potential 

tipping points and possible mitigation strategies 

such as carbon biosequestration.

• Improve integration of anthropogenic climate 

forcings into ESMs and develop new techniques 

to evaluate these coupled models at both global 

and regional scales.

• Establish new observational technologies and 

use them to comprehensively compare ESM 

predictions with observations and to quantify 

model errors.

Grand Challenges in Energy 

Sustainability

Sustainability and its sibling, resilience, have emerged 

in this century as the new discipline of sustainability 

science and technology. It can be thought of as neither basic nor applied research, but as a field defined 
xiv Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Executive Summary

by the problems it addresses rather than by the 

disciplines it employs. The emerging research field of 

sustainability science deals with the broad challenges 

and interactions between natural and social science 

to ensure that natural resources, ecosystem services, 

and economic opportunities are globally available 

to future generations. Over the coming decades, 

drivers such as climate and land-use change; food, 

water, and energy security; and life-cycle assessment 

in industrial production and agriculture will intersect 

internationally. There is an overwhelming need for a 

research agenda that is responsive to these interconnections in order to define, quantify, and ameliorate 

(where possible) changes in the underlying fabric 

supporting sustainability.

Endeavors to achieve energy sustainability suggest the need to understand the consequences and 

processes necessary for mitigating the problems 

and externalities resulting from how the energy 

and industrial sectors are organized. Research must 

address problems caused by present activities (e.g., 

CO2 emissions), develop future alternatives, and 

anticipate potential problems and consequences of 

these alternatives so that they can be minimized.

Research also needs to address not only technical 

and scientific issues and constraints, but also the 

degree to which solutions are implemented and the 

subsequent consequences. Varied ethical, legal, and 

societal implications are inherent in the science and 

technology of energy systems, and the degree to 

which solutions are adopted affects social systems.

Sustainability, by its very nature, calls for multidisciplinary research that spans spatial and temporal 

scales. Compartmentalized research, development, 

and associated agendas can inform understanding 

of system components, but sustainability is an area 

of study that must address the overlap and interrelationships of systems.

Workshop participants identified the following 

grand challenge research recommendations for 

energy sustainability:

• Analyze and compare potential approaches to 

organize land use, water use, and energy systems 

in ways that achieve sustainable energy, food, 

biodiversity, and ecosystem functioning.

– Develop the understanding needed to 

produce enough energy to support more 

people at a higher standard of living in ways 

that sustain growth and minimize negative 

environmental impacts.

– Determine the means to double, over 20 years, 

the share of energy needs met by bioenergy 

in environmentally and economically sustainable ways.

– Develop affordable and competitive options 

for energy supply and conversion that minimize negative direct, indirect, and life-cycle 

impacts on climatic, environmental, and 

ecological systems. 

• Identify and characterize potential Earth system 

drivers, feedbacks, and vulnerabilities to state 

changes so that their consequences and triggers 

might be avoided.

– Characterize the spatial and temporal variabilities of biological systems throughout 

Earth and be able to understand, predict, and 

manipulate their rates of change.

– Develop technologies to remotely monitor 

ecosystem services in real time and at landscape scales.

• Develop unifying models and frameworks 

capable of testing and evaluating the significance 

of potential global change issues, including 

energy, land use, and water. The impacts these 

issues have on both society and the environment 

also must be tested and assessed.

– Develop the scientific basis for life-cycle 

analysis and full-cost accounting of ecosystem services for extant and next-generation 

energy technologies.
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop xv

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

– Advance the science to detect, understand, 

and mitigate the environmental and security 

issues associated with the potential resurgence of nuclear power.

– Advance the discipline of ecological informatics and incorporate social and policy elements 

into science-based sustainability research.

– Develop the science perspective necessary 

to set target resolutions for the spatial and 

temporal dimensions underlying systems of 

interest and be able to convert global change 

estimations into model visualizations.

– Determine the degree to which future sustainable energy facilities should be centralized.

Grand Challenges in Computing for 

Biological and Environmental Research

As experimental systems become increasingly 

digital, more accurate, and efficient, the resultant 

datasets continue to grow exponentially and encompass an expanding diversity of data types. These data 

represent not only fundamental measurements but 

all of the surrounding conditions and factors that 

influence biological function and relate to a more 

accurate and detailed description of the surrounding environment. This increase in scientific data has 

been faster than anticipated over recent decades 

because of improvements in electronics, computing, 

and experimental systems. Growth over the next 

20 years is expected to be significantly greater, given 

the goals of improved models and the expected further advancements in experimental techniques.

A theme shared across BER science is that as the 

research community develops common “models” 

of scientific understanding, the approaches and 

new knowledge are captured in computational 

systems. Substantial increases in data—along with 

possible limits in the growth of computer processing speed—will demand new approaches such as 

parallel computational methods. Similarly, experimental protocols and the reported data should 

accurately describe current scientific understanding 

and experimental results and provide the supporting ontological and semantic information to 

enable machine-readable support for subsequent 

searching, analysis, and reuse. Ultimately, these 

models will be captured entirely in computational 

systems that then will provide an automated basis 

for data storage, integration, query, and retrieval. 

These systems will enable computational simulations and projections that allow further assessment 

and understanding, resulting in new directions for 

future experiments and model improvements.

Working toward these new approaches is becoming 

critical because of the increasing scale and complexity of data. For these data to be useful, computing 

capabilities are required that can consistently and 

coherently manage and integrate the large sets of 

data and information.

Computational modeling at multiple scales is 

required for understanding biological systems 

within a bacterium or in the context of environmental and ecological interactions that impact the 

carbon cycle and climate. Models could interact 

at each scale, but today there is no continuity 

between models of microbial systems and those 

that predict global climate. As models become 

refined and increasingly precise at each scale, 

learning how a model can interact meaningfully 

with those at adjacent scales will present opportunities to gain further insight.

Workshop participants identified the following 

grand challenge 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 peer-reviewed 
xvi Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Executive Summary

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.

Grand Challenges in Education 

and Workforce Training

BER-supported research clearly is shifting from a 

focus on the parts to a quest for the whole, developing 

approaches for studying processes over a hierarchy 

of scales ranging from the subnanometer to kilometers. There is an imperative need to educate our 

future workforce to think about properties of whole 

systems, which rarely can be explained simply as an 

accumulation of parts. This new research framework 

necessitates reorienting education from teaching the 

known to exploring the unknown. Education research 

dictates that the teaching of science should capture 

the nature of inquiry, presenting science as methods 

of scientific investigation rather than as extant bodies 

of knowledge. Inquiry-driven teaching impels instructors to use problem-based learning, open-ended 

laboratory exercises, and collaborative discussion—

all techniques shown to foster higher-order thinking 

skills as well as retention of information. BER must 

develop a clear, unifying vision for education and 

workforce development, emphasizing the integration of scientific disciplines and tackling complexity 

through the use of multidisciplinary teams.

Workshop participants identified the following 

grand challenges for education and workforce 

training:

• Engage science educators.

• Develop a centralized education mission.

• Initiate interdisciplinary fellowships.

• Enhance career development programs.

• Support engineering education to address crucial shortages in engineering disciplines relevant 

to BER’s grand research challenges.

• Install education experts at the national 

laboratories.

• Develop collaborative teaching programs.
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 1

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

Chapter 1

S

trengthening the economy and national 

security by reducing our dependence on oil, 

developing sustainable energy alternatives, 

restoring contaminated environments, and understanding how carbon dioxide (CO2) and other 

energy-related emissions impact Earth’s climate and 

carbon cycle—all of these headline issues represent 

some of the greatest challenges facing the United 

States today and for decades to come. The science 

that can provide innovative solutions to these 

challenges and inform policy and decision making 

is what drives the research programs supported 

by the Office of Biological and Environmental 

Research (BER) within the Department of Energy 

(DOE) Office of Science (see sidebar, Drivers for 

BER Science, p. 2). BER’s mission is to provide 

research opportunities and scientific user facilities 

that advance our understanding of complex biological and environmental systems important to DOE 

missions so that, ultimately, we can build predictive 

models of these systems.

History of BER Science 

BER has a long history of bringing together 

researchers from different disciplines to address 

critical needs in determining the biological and 

environmental impacts of energy production and 

use, characterizing the interplay of climate and 

energy, and collaborating with other agencies 

and DOE programs to improve the world’s most 

powerful climate models. 

With origins in atomic energy research during 

the middle of the 20th century, BER’s predecessor 

agencies were charged with examining the biological effects of exposure to radiation and energy 

by-products. Recognizing the need for a complete 

reference sequence of human DNA, BER initiated 

its Human Genome Program in 1986, which led 

to the international Human Genome Project and 

launched a new era of genome-enabled biology. Early 

DOE research to understand and predict the global 

distribution of radioactive particles from nuclear 

weapon tests led to development of the first general 

circulation models, forerunners to the global climate 

models currently supported by BER and other 

agencies. Furthermore, DOE supported the first 

ecological studies to examine the fate and effects of 

radioactive substances in the environment. Insights 

and tools resulting from this research have translated 

to current BER studies of contaminant transport and 

carbon cycling in terrestrial ecosystems. 

Today, BER continues to advance research at the 

leading edge of science areas essential to achieving a sustainable future for energy, climate, and 

the environment. By revealing the physical, 

chemical, and biological drivers of climate change, 

BER is working to resolve some of the greatest 

uncertainties in projecting future climate. Using 

the power of genomics and systems biology, 

BER is exploring how microbes and plants can 

provide new options for energy production and 

carbon biosequestration. By collectively studying 

and modeling the interconnected microbiological, hydrological, and geochemical processes in 

the subsurface, BER is building a foundation of 

knowledge for assessing and improving proposed 

approaches to environmental remediation. In 

essence, BER programs and activities currently are 

driven by three science questions:

• What are the roles of Earth system components 

(atmosphere, land, oceans, sea ice, and the biosphere) in determining climate?

• How is the information stored in a genome translated into microbial, plant, and ecosystem processes that influence biofuel production, climate 

feedbacks, and the natural cycling of carbon?

1 Introduction
2 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter One: Introduction

Historical Trends in CO2 Concentration. Analysis of air bubbles in an Antarctic 

ice core has shown that natural processes have kept the atmospheric CO2 concentration within a range of about 170 to 300 parts per million (ppm) over at least 

the last 800,000 years. As a result of human activities, the present CO2 concentration of about 385 ppm is about 30% above its highest level in hundreds of thousands years. [Figure adapted from: U.S. Global Change Research Program (www.

globalchange.gov). Figure based on data from: Lüthi et al. 2008 and Tans 2008.]

CO2 Concentration (ppm)

Year

Drivers for BER Science: Energy and Climate 

One of the greatest 

risks to U.S. energy 

security, economic growth, 

and environmental health 

is our dependence on oil. 

Each year the United States 

exports billions of dollars 

to import about 60% of the 

petroleum it consumes, 

primarily for transportation. Greater strain on the 

world’s finite oil supplies 

is expected as developing 

countries become more 

industrialized and energy 

consumption increases, 

driving prices higher in 

a global energy market 

threatened by oil-induced 

economic turbulence. By investing in scientific innovation that can sustainably expand and diversify our 

domestic energy supply, we can begin to take control 

of the nation’s energy future. This investment presents numerous opportunities for biological systems 

science supported by the Department of Energy 

Office of Biological and Environmental Research (BER) 

to help develop new biology-based energy alternatives and to understand and predict the effects that 

different energy options will have on biological and 

environmental services and resources.

Energy choices are inextricably linked to future 

climate. Global climate has been relatively stable for 

thousands of years; however, the warming trend of the 

last few decades continues to intensify, and the most 

recent decade is the warmest in the instrumental 

record. The increase in the global average surface 

temperature is faster and steadier than can be 

explained by natural variability alone, and multiple 

lines of strong scientific evidence show that the rapid 

accumulation of anthropogenic CO2, primarily from 

fossil energy use, is one of the most significant factors 

driving this temperature change and influencing 

future climate. Human activities have pushed atmospheric CO2 concentration to a level greater than any 

the Earth has experienced in hundreds of thousands 

of years (see figure). Unknown are the rate and 

magnitude of biological, environmental, and climatological responses to this unprecedented increase.

Understanding how the biosphere and Earth’s 

biogeochemical cycles will change in a warmer, 

higher-CO2 world is a key scientific challenge that 

must be addressed to improve climate forecasting and 

inform decisions about climate mitigation activities. 

Crossing certain temperature thresholds in biological 

and environmental systems could unleash massive 

amounts of stored carbon or initiate other abrupt, 

unexpected changes at local, regional, and global 

scales. For example, permafrost currently occupies 

16% of global soil area, sequestering an estimated 

1672 Pg (1.67 × 1012 metric tons) of carbon—more 

than double the amount in the atmosphere. Relative to the 10 Pg of carbon emitted annually from 

human activities, permafrost thawing and associated 

microbe-mediated conversion of permafrost carbon 

to greenhouse gases could add 100 to 750 Pg to the 

atmosphere by 2100, yet these processes are poorly 

understood and not represented in climate models. BER is improving the integration of research on 

biological processes that play critical roles in carbon 

cycling, research on the physical and geochemical 

processes influencing climate, and development of 

climate and Earth system modeling. This integrated 

approach uniquely positions BER to provide the 

foundational science that can improve climate projections and inform sustainable strategies for producing 

energy, sequestering carbon, and identifying and 

responding to climate risks.
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 3

Chapter 1

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

• What are the biological, geochemical, and 

physical forces that govern the behavior of 

Earth’s subsurface environment? 

Advancing Complex Systems 

Science

Although the questions driving BER science cover 

a diverse set of mission priorities, a common theme 

across all of these research areas involves applying and improving approaches to understand and 

predict the behavior of complex systems. Defining 

a complex system depends on the context, but, in 

general, such a system consists of many heterogeneous, interdependent components that collectively interact, self-organize, and evolve to exhibit 

larger-scale, emergent properties or behaviors that 

no individual component can display in isolation. 

Whether studying the movement of radionuclides 

in a groundwater plume, the interface between a 

microbial cell and a plant cell wall, the response of a 

soil microbial community to elevated temperatures, 

or global climate simulations of a future scenario 

that doubles current atmospheric CO2—BER science must deal with complexity. 

To influence or manipulate a complex system, the 

understanding of it needs to be sufficient to enable 

scientists to anticipate how the system will respond 

under certain conditions and where and for how 

long this response will have an effect. This is why 

modeling and simulation play such significant 

roles in BER science—they underlie the ultimate 

goal of prediction. Biological and environmental 

systems, however, are especially challenging to 

model. These systems are not only structurally and 

spatially complex with many different interacting parts that span molecular to global scales, but 

they also are dynamically complex, encompassing 

processes that occur over time scales ranging from 

nanoseconds to centuries. 

A reductionist research approach that completely deconstructs a system and characterizes 

its constituent parts, though important, cannot 

provide the level of understanding needed for 

biological and environmental systems. A holistic 

approach, which aims to define the organizing 

principles that transform a collection of parts 

into a functional system, also is needed. Thus the 

quest to understand a complex system requires a 

combination of reductionist and holistic strategies. Computing also is an essential component 

of this complex systems science approach. For the 

diverse systems investigated by BER scientists, a 

variety of models and software tools is needed to 

integrate the large streams of data and information 

emanating from these studies. The rapid evolution 

of computing power and analytical technologies 

over the last few decades has enabled scientists 

to take on systems and problems of increasing 

complexity. This trend is expected to continue in 

the coming decades.

Defining a Long-Term Scientific 

Vision for BER 

Given the extent to which biological, environmental, and climate research has advanced in the last 

two decades, it is exciting to imagine where BER 

science could be in another 20 years. Although 

the research planning process often tends to focus 

on near- to mid-term time frames of 3 to 5 years, 

defining longer-term directions for science also 

is important. In March 2010, the Biological and 

Environmental Research Advisory Committee 

held a workshop to identify scientific opportunities 

and grand challenges in the coming decades and to 

develop an overall strategy for drafting a long-term 

vision for BER. Key workshop goals included:

• Identifying the greatest scientific challenges 

in biology, climate, and the environment that 

DOE will face over a 20-year time horizon.

• Describing how BER should be positioned to 

address those challenges.

• Determining the new and innovative tools 

needed to advance BER science.

• Suggesting how the workforce of the future 

should be trained in integrative system science.
4 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter One: Introduction

The following chapters describe the grand research 

challenge recommendations that workshop participants identified for biological systems, climate 

research, energy sustainability, computing for biological and environmental research, and education 

and workforce training. The vision and challenges 

in this report establish an initial framework of ideas 

expected to be further refined at follow-up workshops engaging the scientific community.
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 5

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

Chapter 2

“It appears to me that beyond this stratum of molecular biology, or above it, 

as some . . . would say, is a second stratum, a stratum which contains problems 

of strategy, of programming, of how to use the various and ingenious molecular devices invented by creatures to make a creature or society. To this class of 

problems I give the name systems biology. . . This appears to be a problem in the 

programming—programming of the use of the information contained in the 

genetic material.” 

— James Bonner. 1960. California Institute of Technology Archives.

2 Grand Challenges in Biological Systems

Complexity is a major challenge for most 

scientific disciplines in the 21st century, 

especially biology (see sidebar, Why Are 

Biological Systems So Complex?, p. 7). The functions of any complex biological system—including 

the microbial and plant systems important to the 

Department of Energy’s (DOE) Office of Biological and Environmental Research (BER)—arise 

from the extraordinary arrangements and dynamic 

interactions among numerous components that 

form higher-order structures at each level of biological organization. Transformational advances 

in the capacity and speed of genome sequencing, 

high-throughput experimental technologies, and 

computational resources over the last 20 years now 

enable global analyses of molecular species at the 

whole-cell level. Computational and mathematical tools now permit scientists to acquire, store, 

transmit, integrate, mine, and finally model data 

to begin to convert the growing wealth of data 

into accessible knowledge or information. These 

powerful new tools and techniques are helping 

biologists examine how numerous molecular-scale 

phenomena collectively lead to more complex, 

larger-scale behaviors.

As modern experimental and computational 

tools rapidly transform biology from an observational to an informational, data-intensive 

science, one manifestation of this evolution is 

the expansion of the complementary fields of 

systems and synthetic biology. While systems 

biology provides the approaches needed to 

address biological complexity effectively and 

achieve a systems-level understanding, synthetic 

biology tests this understanding by using it to 

design and construct novel biological systems for 

useful purposes. Building on a long tradition of 

successful biological research, BER is playing a 

leadership role in applying the tools of systems 

and synthetic biology to critical 21st century 

problems in energy security, carbon and greenhouse gas (GHG) management, adaptation to 

climate change, and development of bioinspired 

materials and processes central to environmental 

sustainability (see box, Summary of Research 

Recommendations, p. 6, and sidebar, Biological 

Research to Advance DOE Missions, p. 8).

Systems Biology

Systems biology is a comprehensive, quantitative 

analysis of the manner in which components of a 

biological system interact functionally over time 

and space (Aderem 2005). Although not a new 

concept, systems biology has piqued a recent 

explosion of interest driven by the increasing 

availability of full genome sequences, which has 

enabled the expanding development and application of new tools for a systems-level analysis 

of cellular function. The ultimate goal is a new, 

predictive view of biological function, augmenting the older, descriptive understanding.
6 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Two: Grand Challenges in Biological Systems

Enabling Predictive Biology 

2.1 Develop a simulation model of a single cell 

for accurately predicting phenotype from 

genotype-environment interactions.

2.2 Establish new model organisms for relevant 

ecological process understanding.

2.3 Use robust biochemical, functional, and 

experimental evidence to enhance genome 

and metagenome annotation.

2.4 Develop the biological understanding for 

generalizing and applying models from 

simple systems to more complex systems, 

working toward an ultimate goal of modeling the evolution and dynamics of a complex 

biological system under environmental 

stress conditions.

2.5 Identify general design principles used in 

natural systems, articulate these, and then use 

them to distill our understanding of biology.

Measuring and Analyzing Biological Systems

2.6 Apply advanced computational and analytical capabilities to characterize the information molecules and network interactions 

used by biological systems.

2.7 Improve capabilities for imaging a single cell 

at a resolution of one molecule per cell.

2.8 Measure microbial processes and interactions in the real world and in experimental 

simulations.

2.9 Provide standardizable, reproducible sampling protocols and observations enabling 

functional characterizations of systems and 

synthetic design.

2.10 Define the range of analytes, ligands, and 

fluxes of materials to be measured.

2.11 Define scalability requirements for measurement technologies and develop capabilities 

for in situ and in vitro sensors.

Exploring Ecosystem Function 

and Elemental Cycling

2.12 Understand, predict, and manipulate the 

types and rates of ecosystem responses and 

feedbacks that result from and influence 

climate change.

2.13 Deploy synthetic (or nonsynthetic) biology 

to understand and manipulate ecosystem 

function.

2.14 Determine the molecular basis of robustness, 

fitness, and selection.

2.15 Develop a complete understanding of the 

biogeochemical cycles important to regulating carbon flux through biological systems.

2.16 Apply functional metagenomics to enable 

mass balance closure for biogeochemical 

cycles and transfer this information to biosystem design.

2.17 Manage plant and microbe stress response to 

control carbon biosequestration and remediation of metals and radionuclides.

2.18 Define the fundamental microbial 

basis for permafrost carbon-methane 

transformations.

2.19 Develop designs for optimizing carbon flow 

for biomass production, carbon allocation, 

and biosequestration to reduce rates of 

atmospheric CO2 accumulation and to 

increase terrestrial carbon storage by 50% 

in 20 years.

2.20 Determine carbon and nutrient dynamics in 

natural systems.

Summary of Research Recommendations

feedbacks as it is about the flow of biological substances. In order to understand the vast quantities 

of molecular components and information shared 

in just a single cell, large bioinformatic databases 

and the latest computational tools and advances 

in spatial and temporal modeling are essential to 

the success of systems biology.

Similar to the scientific revolution of molecular 

biology, systems biology represents a fundamental change in the way researchers ask questions. In 

addition to providing a global view of all biological system components, systems biology is about 

programming and the strategies needed to bring a 

collection of biological parts to life as a dynamic, 

functional system. This holistic approach to 

biology is as much about information flow and 
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 7

Chapter 2

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

•	 Hierarchical and multiscalar levels of biological organization and 

activity. Biological systems—from a single bacterial cell to a forest 

ecosystem—are the dynamic products of many hierarchical levels of 

components interacting with each other and their environment across 

many scales of space and time. However, the connections between 

these different levels of interactions are poorly understood.

•	 Vast diversity of components within biological systems. At each level 

of organization, a biological system contains numerous parts—thousands 

of proteins, nucleic acids, and metabolites at the molecular level within a 

cell, or millions to billions of different cells at the microbial community or 

tissue level. All of these parts have diverse functions, and they interact in 

complex ways. These functions and interactions underpin the phenotypes 

observed for a particular set of environmental conditions.

•	 Environmental dependency of biological phenotype and response.

An organism’s phenotype or the behavior of a biological system is 

highly dependent on environmental conditions. Biological function and 

response depend not only on a system’s inherent properties (e.g., its 

genome), but also on how it interacts with other biotic and abiotic components of its environment. Environmental changes (e.g., fluctuations in 

nutrient or water availability, temperature change, or the introduction 

of invasive species) can have significant impacts on these interactions, 

so understanding and predicting environmental changes are essential 

parts of predicting biological response.

•	 Emergent properties. Even if all the parts of a biological system are thoroughly characterized, unanticipated functions and features can emerge 

at all levels of biological organization. The collective response of myriad 

microscale events, or even rare or isolated occurrences, can lead to seemingly unpredictable larger-scale outcomes. This nonlinear and adaptive 

nature of self-organizing biological systems is a hallmark of biological 

complexity and makes extrapolations difficult. Workshop participants 

suggest that a new mathematics is needed to model biological systems to 

accurately capture issues of dynamic growth, interactions, and change.

Synthetic Biology

Synthetic biology is (1) “the design and construction of new biological parts, devices, and systems; 

(2) the redesign of existing, natural biological 

systems for useful purposes” (syntheticbiology.org); 

and (3) the inspiration for new materials and 

processes derived from the design, functions, and 

products of living cells. Often considered a component of systems biology, synthetic biology represents a tool chest of methods and approaches for 

understanding natural systems using simplified or 

controllable analogs such as “minimal cell architectures,” genetic circuits, metabolic networks, and 

constructed metabolic pathways. By integrating with 

computational science, synthetic biology can enable 

Why Are Biological Systems So Complex?

Organisms and Ecosystems

Tissues and Organs

Cells

Molecules

Activities and interactions at the molecular level underpin the functions and 

interactions that occur at higher levels of biological organization, such as cells, 

tissues, organs, organisms, and ecosystems. [Image credits: iStockphoto.]
8 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Two: Grand Challenges in Biological Systems

the design and construction of essentially new 

materials, organisms, pathways, or informationprocessing systems.

The analytical tools of synthetic biology already 

are being used—with traditional workhorse 

organisms of microbiology and biotechnology—

to metabolically engineer new biofuel pathways 

(Brynildsen and Liao 2009; Atsumi et al. 2009). 

Key issues for synthetic biology involve characterizing what nature already has assembled 

For over six decades, the Department of Energy’s 

(DOE) Office of Biological and Environmental 

Research (BER) and its predecessor programs have 

supported landmark research applying and developing the latest technological advances to study 

biological problems important to DOE’s energy, 

environment, climate, and basic science missions. 

Since initiating its Human Genome Program in 1986, 

BER has spearheaded the development of modern 

genomics-based systems biology and played a 

major role in seeding and fostering the contemporary biotechnology revolution.

Although several federal agencies support and 

benefit from the continued advances in systems 

biology, BER—with its rich history in interdisciplinary, problem-focused research in biology, 

climate and carbon cycle science, subsurface 

biogeochemistry, and computational science—

is particularly suited to play a leadership role in 

understanding biological systems at the energyclimate interface. The following examples of BER 

science have driven the emergence of an integrated multidisciplinary research agenda that fully 

embraces the tools of molecular biology, genomics, and computational analysis.

•	 Plant and microbial genomics. BER’s Genomic 

Science program provides the science and technology development needed for a systems-level 

understanding of DOE-relevant organisms. This 

program and BER support for genome sequencing at the DOE Joint Genome Institute have been 

major platforms for significant progress in plant 

and microbial genome sciences.

•	 DOE Bioenergy Research Centers. The three 

DOE Bioenergy Research Centers are applying 

cutting-edge technologies to understand the 

processes essential for the production of cellulosic biofuels. These centers provide collaborative 

research environments well suited for applying 

systems and synthetic biology approaches to 

advance DOE BER missions.

•	 Subsurface biogeochemistry to inform remediation of legacy waste. Long-term BER support 

for subsurface biogeochemical research has contributed to a new understanding of the microbial 

systems and the interrelated hydrological, 

geophysical, chemical, and biological processes 

controlling contaminant fate and transport in 

the subsurface, an area of unique and urgent 

importance to DOE.

•	 Structural biology leveraging DOE scientific user facilities. BER supports experimental stations at several DOE Office of Science 

facilities. These include the synchrotron light 

sources located at Argonne National Laboratory, Brookhaven National Laboratory, Lawrence 

Berkeley National Laboratory, and SLAC National 

Accelerator Laboratory, as well as neutron 

beam sources located at Los Alamos National 

Laboratory and Oak Ridge National Laboratory. 

These facilities provide imaging and analytical 

capabilities that drive research in fundamental 

materials science and structural biology critically important in computational biology and 

dynamic modeling.

•	 Molecular Systems Science. BER’s Environmental Molecular Sciences Laboratory is advancing 

understanding of physical, chemical, and biological processes at the molecular and systems 

levels. This research is leading to new strategies 

for bioenergy production and carbon biosequestration, improved catalysts and materials for 

industrial applications, tools for managing and 

predicting the movement of subsurface legacy 

wastes such as radionuclides and heavy metals, 

and approaches for mitigating climate change.

Biological Research to Advance DOE Missions
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 9

Chapter 2

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

and determining how to access, harness, and 

improve biological components or processes to 

meet future challenges or understand system 

responses to change. In a broad systems context, a 

grand challenge is advancing the use of synthetic 

biology’s tool chest within natural ecosystems to 

investigate complex dynamics and adaptations, 

molecular (even quantum) interspecies interactions (e.g., microbe-microbe and plant-microbe), 

and emergent properties.

Importance of In Situ Analyses 

for DOE Biological Systems

Many of the biological challenges and opportunities important to DOE missions involve the 

additive complexity of understanding microbial 

and plant systems within the context of a particular real-world environment at scales ranging from 

molecular to ecosystem levels. For example, we can 

obtain the entire genome sequence of a bioenergy 

crop such as switchgrass, but predicting the photosynthetic rate or some other measurable phenotype 

requires understanding how this plant functions 

within a specific environment. The metabolism 

for a microbe studied in the laboratory may look 

nothing like the metabolism of the same microbe 

living in the soil—intimate associations taking 

place in the microscale environment can have a 

profound influence on microbial metabolism. The 

overall vision is to understand, predict, and perhaps 

manipulate the relationship among microbial and 

plant systems relevant to DOE missions, the environment in which such organisms exist, and the 

effects that changes to one have on the other.

With metagenomics and metaproteomics, the 

systems biology paradigm is moving out of the 

laboratory and into the environment where 

biological systems can be studied in situ (see 

sidebar, Metagenomics of Natural Microbial 

Communities, p. 10). Previously laborious and 

slow methodologies have yielded to significant 

advances in analytical tools, microarrays, RT-PCR 

(reverse transcription polymerase chain reaction), 

high-throughput sequencing, and modern mass 

spectrometry. These tools now provide the capacity for near high-throughput environmental analysis of DNA, mRNA, proteins, and metabolites.

For a relatively simple microbial community consisting of a small number of species and thriving in 

highly acidic drainage from a mine, BER supported 

groundbreaking metagenomic research that reconstructed near-complete genomes of novel microbial 

species, characterized the community’s structure, 

and revealed important insights into the community’s shared metabolic network (Tyson et al. 2004). 

Subsequent research characterized the metaproteome of this community (Ram et al. 2005). The 

greater complexity of most natural microbial communities, however, currently limits the applicability 

of these metaomic methods to other soil communities of interest to DOE.

An important part of understanding biological 

processes and systems in natural environments is 

informational biology—the flow and pools of information molecules in ecosystems that are elaborated 

by abiotic or biotic transmitters and responded to 

by receivers in the environment. This physical flow 

of information can include nucleic acids and other 

information-rich molecules, smaller metabolites, 

and signaling compounds. Characterization of 

these molecules shared among different organisms in ecosystems—coupled with the explosive 

growth anticipated for metaomic analysis of diverse 

environments—will produce a tremendous bioinformatic resource base. If successfully managed, 

this resource will be ripe for synthetic exploitation 

across many areas of enzyme, organism, and system 

development and improvement.

Ultimately, computational resources will have the 

daunting task of dealing with the large datasets 

needed to address the temporal and spatial heterogeneity of environmental analysis. Such capabilities will be necessary to provide critical dynamic 

information on organismal (gene) distributions and 

responses (messages) in ecosystems. Though very 

promising, current research in this area has yet to 

address major issues like quantitative standard analysis, sampling representativeness and replication, 
10 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Two: Grand Challenges in Biological Systems

Transcending the limitations of 

investigating isolated organisms cultivated in the laboratory, 

metagenomics—the sequencing 

and analysis of genetic material 

extracted from natural microbial habitats—is expanding our 

understanding of the structure and 

function of microbial communities. 

Although only a few decades old, 

the ability to recover and analyze 

nucleic acids from environmental 

samples (representing the indigenous biological 

community) has revolutionized the approach to measuring the biomass, diversity, and functional activity 

of microbes in the environment. Recommendations 

for advancing metagenomics research, which has 

great potential for contributing to the missions of 

many governmental agencies and commercial stakeholders, were described in the National Research 

Council report, The New Science of Metagenomics: 

Revealing the Secrets of Our Microbial Planet (2007).

Metagenomic sequencing of microbial communities 

is the foundation underlying measurements of gene 

transcripts, proteins, metabolites, and other molecular species actively expressed by these communities. 

Genomics, transcriptomics, proteomics, metabolomics, and other global measurements of the molecularlevel activities of microbial consortia can collectively 

be described as “metaomics.” As sequencing and 

high-throughput experimental technologies continue 

to advance, these different metaomic approaches will 

become more integrated. For example, concomitant 

advances in PhyloChip and functional gene arrays 

have produced significant capabilities for characterizing small subunit rDNA biodiversity and functional 

gene abundance in complex environmental samples. 

However, chip-based arrays are expected to be overtaken by high-throughput sequencing methods to 

produce an integrated genomic and transcriptomic 

analysis of environmental samples. Growing evidence suggests that pyrosequencing approaches are 

progressing rapidly to provide snapshots of 16S rDNA 

diversity in environmental analysis.

Metaomics will inform and reinforce the continued 

support of genomic and systems biology research 

on isolated microbial populations such as the critical subsurface anaerobes Geobacter, Shewanella, 

and Desulfobacter, as well as other microorganisms important to the Department of Energy’s 

Office of Biological and Environmental Research. 

Studies of cultured organisms can provide key 

reference points for interpreting metaomic data, 

and metaomics can help enable the growth and 

analysis of microbes considered “unculturable” by 

shedding light on the metabolic and environmental 

requirements of these microbes.

Complementary laboratory-based and in situ

approaches to exploring the microbial world are 

revealing new insights and hypotheses addressing 

the functions and relationships among community members. For example, metagenomic 

sequencing of a low-complexity community 

thriving in acid mine drainage (AMD) led to the 

discovery of unique microbes so small that they 

are practically invisible to a light microscope 

(Baker et al. 2006). Known as ARMAN (archaeal 

Richmond Mine acidophilic nanoorganisms), these 

microbes represent a completely new phylum and 

contain many genes that have never been seen 

before. Using 3D cryogenic electron tomography 

to image AMD community samples (Comolli et al. 

2009), researchers were able to determine the size 

and structure of ARMAN cells, which appear to be 

free-living, and several are associated with viral 

particles (see figure). Although the role of these 

microbes in the AMD community is not yet known, 

this research shows that combining metagenomics with 3D imaging can provide some valuable 

insights into the physiology and functions of 

uncultivated organisms.

Metagenomics of Natural Microbial Communities

Ultrasmall ARMAN cells have 

a compact genome with only 

1 million base pairs and a diameter of about 200 to 400 nanometers, roughly one-fifth the 

size of the average bacterial cell. 

The pink rod-like extensions and 

the gold lemon-shaped objects 

at the cell surface are viruses. 

[Image credit: Luis R. Comolli, 

Lawrence Berkeley National 

Laboratory.]
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 11

Chapter 2

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

efficiency in extracting organisms or nucleic acids 

and proteins, and the ascendant challenges of data 

analysis. Fully attaining this capability also requires 

the assembly and bioinformatic analysis of databases 

likely to be orders of magnitude larger than current 

genomic databases for the hundreds of species thus 

far examined by DNA sequencing. Investigating 

the vast array of cellular constituents (e.g., RNA, 

proteins, and metabolites) present under many different biological and environmental states for every 

genome of interest will significantly multiply the 

data generated from future genomic studies.

DOE Relevance and Potential 

Impact of Systems and Synthetic 

Biology

To address the issues of climate change, energy, 

and remediation of legacy waste, DOE will lead 

in integrating systems and synthetic biology 

approaches with conceptual and numerical models 

that elucidate and predict the role of biological systems in the changing in situ environment. Research 

activities and developments in these complementary fields of study naturally converge to produce an 

analytical framework to:

• Contribute fundamental new knowledge about 

the mechanisms of biological response of ecosystems to stressors from environmental change.

• Provide mechanistic insight into collective, adaptive, and emergent properties of complex systems.

• Promote development of biobased and bioinspired materials, processes, and technology to 

meet energy, fuel, feedstocks, remediation, and 

carbon management challenges.

Applying this analytical framework to natural 

ecosystems will extend systems biology concepts 

and synthetic biology tools to the investigation of 

complex dynamics and adaptations of microbial 

communities, molecular interspecies interactions 

(e.g., microbe-microbe and plant-microbe), and 

emergent properties. In addition to advancing 

the understanding of (1) community gene networks, (2) regulatory circuits, and (3) information 

processing underlying fundamental microbial and 

plant processes and their interactions within the 

environment, this integration of systems and synthetic biology with ecosystem research will directly 

contribute to the following objectives:

1. Developing new organisms and genetic resources 

to create novel, optimized processes for bioenergy alternatives ranging from gasoline replacements and renewable biodiesel to methane 

(CH4), hydrogen (H2), and electricity (see sidebar, Applying Systems and Synthetic Biology: A 

Bioenergy Example, p. 12).

2. Mechanistically understanding control points 

and fitness determinants that may be modified 

within ecosystems to permit rapid adaptation 

to changing climate regimes and global change 

stresses.

3. Improving the capacity, productivity, and sustainability of biomass resources to serve as reliable and plentiful feedstocks for fuel and fiber.

4. Optimizing carbon cycle flow for greater terrestrial carbon uptake and soil sequestration 

to create an intermediate sink for excess fossil 

carbon release.

5. Interceding in carbon mineralization pathways 

of geologically stored labile carbon to inhibit 

release as CH4 and other GHGs in response to 

global warming.

6. Manipulating soil-plant-microbe interactions to 

ensure sustainable contaminant and radionuclide 

remediation in the soil and deeper subsurface.

7. Creating modular components of the biofuel 

production system to provide essential ecosystem services based on life-cycle assessments 

and to support sustainable bioenergy choices 

and paradigms.

8. Discovering, understanding, and using environmental processes to optimize designs of 

biomaterials and bioinspired products and 

reduce net energy intensity.
12 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Two: Grand Challenges in Biological Systems

Asustainable biofuel economy 

depends on achieving high biomass production at low cost, generating minimal (or ideally beneficial) 

environmental effects, and using 

lands not suited for food production. 

Biological research is needed to contribute to five major objectives in the 

biofuel economy:

1. Improve plant biology important 

to productivity and develop plant 

products that are more easily converted to 

liquid fuels.

2. Address potential plant-yield reduction resulting from the impact of various biotic and 

abiotic stresses.

3. Optimize microbial processes for efficiently converting plant carbon to advanced fuel products.

4. Understand and improve soil-water-plantmicrobe relationships so that the microbes 

aid sustainable, low-cost plant productivity 

on marginal lands envisioned for use by the 

biofuel economy.

5. Identify the suite of environmental and socioeconomic conditions under which bioenergy 

crop production is most suitable.

For microbial production of improved biofuels, 

metabolic engineering and systems and synthetic 

biology approaches already have demonstrated 

major success in defining and developing gene 

networks and control strategies. These approaches 

have led to the development of controllable cells 

and metabolic activity for process analysis, sensing, and new synthesis routes for alternative liquid 

fuels such as biodiesel (see figure) and isobutanol 

(Atsumi et al. 2009; Brynildsen and Liao 2009; 

Steen et al. 2010). Results from this research also 

are helping to characterize microbial response networks associated with different biofuel products 

and providing key insights into mitigating stress 

from increased biofuel production. The availability 

of knowledgebases relating genes to metabolism 

and the use of genetic tool kits such as “BioBricks” 

to create novel architectures in well-characterized 

microorganisms are paving the way for continued 

success in the production of biofuels that can 

directly replace petroleum products. The longterm challenge is to integrate and “scale up” this 

new level of reductionist understanding to remove 

or minimize current inefficiencies in the overall 

biofuel production process.

A mass of Escherichia coli

cells (upper left corner) 

sequester themselves 

from the oily biodiesel 

droplets they produce. 

[Image credit: Eric Steen, 

DOE Joint BioEnergy 

Institute.]

Grand Challenge Research 

Recommendations

Integrated applications of new systems and 

synthetic biology approaches are helping to build 

connections from genome- and molecular-level 

research to investigations at physiological and 

ecosystem levels. Though discussed as individual 

challenges, the future long-term research opportunities described in this section have a natural 

connection and progression among biological 

systems important to DOE. For example, achieving the goal of accurately modeling a cell, a community of cells, and an ecosystem of organisms 

provides the ability to predict or, indeed, manipulate how all of these respond to (or affect) local or 

global environmental change.

The following grand challenge recommendations 

start with research needs focused on systems-level 

understanding at the cellular level and then move 

Applying Systems and Synthetic Biology: A Bioenergy Example
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 13

Chapter 2

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

Advancing the resolution, throughput, speed, 

and temporal and spatial requirements of 

research technologies is essential to revealing 

the connections between the genome and functional processes occurring at cellular and higher 

scales. Thus the importance of improving tools, 

techniques, and probes to characterize biological 

and ecological systems is well recognized by the 

research community. In addition to the needed 

observational, experimental, and computational 

capabilities identified in the section, Grand Challenge Research Recommendations, p. 12, the reader 

is encouraged to examine two recent Department 

of Energy (DOE) Office of Biological and Environmental Research (BER) reports that summarize relevant research and technology development needs 

defined by the scientific community.

In May 2009, BER supported a workshop on developing a new generation of technologies for characterizing cellular- and multicellular-level functions 

related to DOE missions. The diverse scientists and 

engineers participating in this workshop presented 

their findings in the report New Frontiers in Characterizing Biological Systems (DOE/SC-0121).

In another BER 

workshop held 

in August 2009, 

researchers with 

expertise in complex systems and environmental, microbial, and Earth sciences described tools 

and approaches needed to quantify and predict 

complex behavior in the subsurface. Output from 

this workshop was published in the 2010 report 

Complex Systems Science for Subsurface Fate and 

Transport (DOE/SC-0123).

on to challenges associated with investigating 

systems of increasing biological complexity. The 

recommendations also address improvements in 

the technologies and techniques that support the 

science described in these grand challenges. For 

other recent BER reports summarizing needed 

tools and approaches identified by the research 

community, see sidebar, BER Workshop Reports 

Focus on Characterizing Complex Biological 

Systems, this page.

Enabling Predictive Biology

2.1 Develop a simulation model of a single cell for 

accurately predicting phenotype from genotypeenvironment interactions.

Genomics, transcriptomics, proteomics, metabolomics, high-resolution imaging, structural genomics, 

and structural dynamics have begun to provide a 

“parts list” of individual cells. In addition, systems 

biology approaches have revealed partial schematics 

of cellular systems, but we still do not fully understand the workings of a complete cell from any one 

organism. A fully functional, predictive model of a 

single cell—the smallest unit of a living system—

represents a grand challenge that underpins many of 

the recommendations in this chapter.

A dynamic model of an entire cell will require 

selecting one or more cell types (e.g., a eubacterium or some other single-celled microorganism 

or a plant cell) and integrating new research results 

with all existing data and scientific studies relevant 

to that cell type (see sidebar, Holistic, Dynamic 

Model of a Single Cell, p. 16). Workshop participants suggest the following criteria for choosing a 

single-cell system for intensive study:

BER Workshop Reports Focus on Characterizing Complex Biological Systems
14 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Two: Grand Challenges in Biological Systems

Shewanella, and Desulfovibrio. Clearly needed are 

genetic systems for efficiently characterizing and 

modifying new model organisms relevant to the 

carbon cycle, especially photosynthetic organisms 

and rhizosphere heterotrophs and azotrophs.

2.3 Use robust biochemical, functional, and 

experimental evidence to enhance genome 

and metagenome annotation.

The predictability and functionality of a comprehensive single-cell model or any newly designed 

gene networks, circuits, organisms, and communities highly depend on the robustness and accuracy 

of genome annotation. For existing models, there 

is an uncertainty concerning the veracity of annotated genomes—an issue that will be confounded 

further as more synthetic biology strategies are 

undertaken on a greater diversity of network 

elements and organisms. For metagenomics, this 

issue likely will become a critical element to future 

progress in design and application.

2.4 Develop the biological understanding for generalizing and applying models from simple systems to more 

complex systems, working toward an ultimate goal of 

modeling the evolution and dynamics of a complex biological system under environmental stress conditions.

From the single-cell level, we should be able to 

advance to predictive models of communities of 

cells, a microbial colony, a leaf, an organ, organisms, and then to communities of organisms. This 

will involve improving our understanding of gene 

networks, epigenomics, cell-cell communication, 

information flow, tissue interactions, horizontal 

gene transfer, and hybridization. Workshop participants envisioned a long-term goal of predicting 

how a particular crop variety would grow in different environments simply by knowing its genotype. 

Achieving this higher level of predictability will 

require understanding how organisms and communities of organisms drive or respond to environmental change.

Complex biological systems and ecosystems 

evolve in response to a variety of environmental 

stresses (Comte et al. 2010; Zhang et al. 2009). 

• DOE relevance (e.g., biogeochemical cycling 

and cellulose metabolism).

• Multiple genome sequences available for 

comparison.

• Genetically tractable (e.g., organisms amenable 

to mutagenesis and gene transfer).

• Extant genome-scale models.

• High-throughput phenotyping available.

• Ability to make comparisons within an evolutionary context.

The entire genetic, transcriptomic, and metabolic networks of the cell should be represented 

in silico, and where protein, metabolite, and kinetic 

parameters do not yet exist, they should be range 

estimated from the closest, best-available data 

subject to parameter refinement as new data are 

developed. The model should be able to simulate 

microbial growth and reproduction from a minimal growth environment.

2.2 Establish new model organisms for relevant 

ecological process understanding.

Identifying and developing new organismal models 

highly relevant to bioenergy production and 

carbon-cycle science are critical research needs for 

BER. Escherichia coli remains the primary model 

for most microbial systems biology research. As 

we move to a complete understanding of all of its

network and metabolic functions under diverse 

environmental regimes, E. coli will continue to 

serve as a foundational model for all other organisms. Capturing the greater network and metabolic 

versatility prevalent in the environment is needed 

for organisms and processes representing key 

biogeochemical cycling components (e.g., cellulose 

degradation, methanogenesis, and methane oxidation), aspects of plant host susceptibility, and other 

fundamental ecological properties. These organisms should be elevated to the equivalent state 

of network understanding anticipated for E. coli

over the next decade. BER has contributed to an 

advanced understanding of some potential models for subsurface biogeochemistry—Geobacter, 
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 15

Chapter 2

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

Understanding, diagnosing, and predicting these 

changes and their dynamics are critical to sustaining natural systems and designing new synthetic, 

stable biological systems. By identifying key markers and impact factors, researchers will be able 

to develop capabilities for detecting, modeling, 

and predicting the loss of community diversity, 

changes in metabolic potential, and the emergence 

of increased sensitivity to additional stresses.

With continued improvement in the availability and utility of models for specific cellular and 

developmental systems within a selected species, a natural extension will be to investigate and 

dynamically model the evolution of such systems as 

they interact with their environment and respond 

to various stresses. Types of fundamental evolutionary changes include both changes of molecular 

identities within roughly constant networks and 

changes of network superimposed on roughly constant molecular components (e.g., major changes 

in expression and morphology resulting from 

regulatory alterations in a key developmental control gene, as reported in Chan et al. 2010). Reconstruction of ancestral biomolecular networks may 

become possible, providing clues to some otherwise unobservable ancient network functionalities. 

Understanding the past evolutionary dynamics of 

cellular and multicellular networks, especially at 

the level of dynamic models, also could contribute significantly to predicting future evolutionary 

response to altered environments. An example 

problem for evolutionary projection is estimating 

the number of generations required for a given 

adaptation, which can be very large (Blount et al. 

2008) or very small (Grant and Grant 2006).

When moving outside the laboratory or beyond 

studies of artificially created communities, we are 

immediately faced with the complexity and unique 

environmental context that must be addressed 

when studying natural systems. Efforts should be 

made early and continuously to integrate knowledge from models of less complex organisms (see 

sidebar, Holistic, Dynamic Model of a Single Cell, 

p. 16) or environments and then test these models

against natural communities. In the field of metagenomics, analyses of low-complexity microbial 

communities (Tyson et al. 2004; Ram et al. 2005) 

are providing the foundational insights, research 

capabilities, and computational tools needed to 

help enable investigations of more complex natural 

communities important to DOE.

2.5 Identify general design principles used in natural 

systems, articulate these, and then use them to distill 

our understanding of biology.

One possibility offered by a systems approach to 

investigating biological function is the identification of general design principles for biology at 

different levels. We already have some information about the design of gene circuits, which are 

comprised of small modular circuit elements such 

as feedback and feedforward modules (Alon 2007). 

These modules appear to be the building blocks of 

gene circuit evolution (Gao and Davidson 2008). 

Synthetic gene circuits have been assembled from 

modules of regulatory genes to connect genomic 

information to network understanding of the control and regulation of proteins involved in particular cellular processes (Hasty et al. 2002). Similar 

circuit diagrams are being drawn now for biology at 

the cellular level, with circuit elements for cell-cell 

interactions also showing modular construction. 

These circuits, however, have additional emergent 

properties that arise from cellular signaling, leading 

to changes in gene regulatory networks, and from 

new types of interactions (such as plant hormone 

transport), adding a spatial element to cell-level 

interactions that are absent in gene regulatory 

networks (e.g., Jönsson et al. 2006). These tools 

and approaches also can provide a conceptual 

framework for analyzing a metacommunity and 

facilitate the development of additional tools to 

probe network connectivity within the environment. For example, research on metagenome and 

metaproteome networks and modules could reveal 

insights into the fundamental regulation of community or ecosystem processes. In a conceptual sense, 

these systems are considered complex and adaptive 

(Levin 2002; Hartvigsen et al.1998) and should 
16 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Two: Grand Challenges in Biological Systems

An ultimate goal is to have computational, predictive models that describe organisms in their 

environment, simulate the dynamic interactions 

within these organisms and their communities, and 

incorporate the relationships and feedbacks that 

occur among organisms, their environment, and 

the climate system. However, the complexities of 

these interactions currently are beyond our understanding and likely will continue to be so 20 years 

hence. Therefore, an initial step toward this ultimate 

goal is identifying a simpler, more tractable system 

for which a truly holistic model could be achieved. 

Workshop participants suggested developing a 

comprehensive, dynamic model of a single cell (e.g., 

a bacterium) as an attainable goal over the next two 

decades. Creating this “simpler” model would provide insights needed to build the larger community 

or ecosystem models that we hope to have someday.

The DNA sequence of a cell’s genome allows 

us to predict, to a first approximation, what the 

entire proteome will look like, at least in the case 

of prokaryotes. However, the detailed map of the 

composition, structures, and interactions of all 

other macromolecules (e.g., RNAs and polysaccharides) and small molecules (e.g., metabolites 

and the lipids that maintain membrane structure) 

remains a mystery. Even the least complex bacterial 

cells have hundreds of thousands of such molecular 

species. Vast improvements in modern analytical 

instruments, particularly mass spectrometers, present the possibility of determining the molecular 

weights and structures of all the molecules required 

by a cell—from the smallest metabolite to the largest macromolecular structure. For the first time, we 

would be able to access the full “parts list” of a single cell. Comparable to the publicly funded Human 

Genome Project, which required 400 DNA sequencers, determining the full molecular contents of a 

single cell also may require only a few hundred 

dedicated instruments, together with supplies, personnel, and informatic support, focused on this one 

Can all the molecules required for life 

and their interactions be comprehensively 

described for one cell?

Developing a comprehensive, dynamic model for 

a bacterial cell with some manageable number of 

genes, cellular components, and outputs could help 

us determine how much and what kinds of biological information are needed to attain reliable predictions. [Image credit: TurboPhoton.]

goal. This research also would need to characterize 

the interactions between the cell’s molecules and 

analyze the dynamic changes that occur in response 

to environmental perturbations.

In addition to empowering systems and synthetic 

biology, the impacts of this project could be as diverse 

and far-reaching as those of the Human Genome 

Project. Once the structures of all molecules required 

for life are known and standards become routinely 

available, this information undoubtedly would find 

direct application to the study of other organisms, 

both prokaryotic and eukaryotic. A single-cell model 

represents a cost-effective first step toward predicting 

in silico what the effect of any genetic or environmental perturbation would be on that cell’s biochemical 

machinery. In essence, having this predictive capability would place all of life’s biochemical processes 

under our control, and, extended in the future to 

more complex organisms, it could transform currently 

unpredictable, hit-or-miss paradigms for agricultural 

production of energy crops or other biology-driven 

practices to predictable engineering endeavors.

Holistic, Dynamic Model of a Single Cell
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 17

Chapter 2

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

yield to the same analytical approach. Many new 

modules remain to be discovered, and the computational environment for using them as building 

blocks to understand basic principles of gene regulation and organismal development—and hence to 

invent new types of networks—is only just emerging. These same design principles and circuit motifs 

may operate at other biological levels—not only 

in more complex types of cell-cell communication 

(as in the interactions between plant roots and soil 

microbes), but at higher levels of biological organization. Such levels include interactions between tissues and organs, interactions between members of 

the same species in populations, interactions with 

other species in ecosystems, and interactions with 

the abiotic environment. Discovering and applying 

these organizational principles are the great challenges in biology for the next 20 years.

Measuring and Analyzing 

Biological Systems

2.6 Apply advanced computational and analytical 

capabilities to characterize the information molecules 

and network interactions used by biological systems.

Computational resources and the ability to resolve 

small information molecules, particularly by mass 

spectrometry, are giving rise to rapid expansion 

of the field of infochemicals (Dicke and Sabelis 

1988). Many of these informational chemical signals and transfers are the result of recognition by 

receptor proteins, most of which remain uncharacterized. This area presents promising opportunities 

for structural biology research to access powerful 

computational tools and protein docking models 

to understand the molecular basis of an interaction. This approach also could aid in predicting 

the nature and ecosystem function of unknown 

information molecules and receptors in the environment. New biologically inspired research in this 

area can take advantage of tremendous computational resources (see Chapter 5: Grand Challenges 

in Computing for Biological and Environmental 

Research, p. 43) and complex protein characterization capabilities rapidly expanding within DOE 

laboratories. This is a massive research area potentially yielding hundreds of thousands of single 

nucleotide polymorphisms (SNPs) for kinases, 

transporters, transcription activators, and other 

proteins with recognizable domains but unknown 

biochemical confirmation within the metatranscriptome and metaproteome. Thus this effort 

requires coordinated, high-throughput, collaborative biochemical analysis among investigators 

nationally and perhaps internationally.

2.7 Improve capabilities for imaging a single cell 

at a resolution of one molecule per cell.

New demands are increasing for single organism–

single molecule resolution to further reduce variation and experimental noise, increase throughput, 

and reduce cost. Great progress has been made in 

areas such as single-cell isolation by fluorescentactivated cell sorting, single-cell resolution of 

fluorescently probed or reporter cells, single-cell 

PCR amplification, and single-cell gene introductions and silencing. However, ease of use, convenience, and throughput for these technological 

advances need to be improved to meet future 

demands. Additional improvements are needed in 

developing multichannel formats to meet dynamic 

throughput requirements, sequencing from one 

molecule in a single cell, and achieving dynamic 

detection of subattomole concentrations (perhaps 

down to a single protein molecule or metabolite) 

to address subcellular rates and fluxes.

2.8 Measure microbial processes and interactions 

in the real world and in experimental simulations.

In situ or in vivo observations are difficult to make. 

Often, the acts of sampling and sample processing 

introduce uncertainty into the analysis by perturbing genetic expression and physiological function 

or by debilitating and killing resident organisms 

and interactions. The biofilm and environmental 

research communities deal with these issues by 

applying robust experimental simulations. For 

systems-level modeling, real-world physiology 

must be defined and kinetic parameters quantified. 
18 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Two: Grand Challenges in Biological Systems

The nonculturability issue also needs further testing 

and experimental verification to predict the degree 

of manipulation and control that can be exerted over 

the system either from a synthetic biology or control engineering standpoint. If indeed large portions 

of the community are found to be truly nonculturable, then new genetic tools, gene transfer systems, 

or nanophysical techniques are needed to permit 

manipulation and experimental observation of 

organisms in their complex environmental settings.

2.9 Provide standardizable, reproducible sampling 

protocols and observations enabling functional 

characterizations of systems and synthetic design.

Key issues for implementing synthetic biology 

at the environmental scale are accurate, precise 

observations of fundamental network elements 

to understand the system(s) of interest to DOE. 

Given the complexity of the environment, a critical 

need is establishing protocols that will provide 

the necessary observational information and 

will enable comparability across laboratories and 

environments. The types of observability needed 

to achieve computational design include information flow (genes and metabolites) within and 

among populations, knowledge of how stochastic 

processes are used and propagated in the system, 

influences of circadian rhythms and diurnal cycles, 

and metagenomic to proteomic component data.

2.10 Define the range of analytes, ligands, and fluxes 

of materials to be measured.

We anticipate that virtually all ranges of biomolecules and their life spans will need to be identified 

as systems and synthetic biology strive to develop 

a more complete understanding of host metabolic 

network interactions under dynamic environmental conditions. These biomolecules might include 

numerous small molecules (e.g., signaling molecules and metabolites), as well as proteins, and 

require visualization on the scale of nanometers to 

centimeters. Initially, these requirements are likely 

to manifest themselves at the level of segmentation and compartmentalization within the cell or 

at the organism level, but they will rapidly expand 

to more dynamic issues relative to tracking and 

trafficking across tissues and to the environment, 

including biofilms, and, ultimately, into complex 

matrices such as soils. Measurement of carbon 

and nitrogen fluxes within these systems, along 

with determination of phenotypic growth rates, 

will be a challenge to overcome.

2.11 Define scalability requirements for measurement technologies and develop capabilities 

for in situ and in vitro sensors.

Current measurement capabilities range from 

remote satellite imaging to monitoring individual 

cell-cell communications and applying point-of-use 

sensors. Hyperspectral satellite imaging is required 

to achieve individual plant species quantification 

and physiologic function within a landscape. At the 

microbial level, attaining a similar capability within 

a water body would be desirable. Although measuring physiologic function at the microbial species 

level is unlikely, measuring certain community-level 

metabolic activities—such as differential chlorophyll dynamics for discriminating photosynthetic 

production rates—is achievable. Pigment production, marl formation, and artificial reporter tags 

(e.g., fluorescent proteins and bioluminescence) 

are further examples of targets for remote sensing 

with systems-level applications. In subsurface- and 

rhizosphere-oriented research, measurements of 

environmental parameters, such as pH, redox, and 

specific chemistries, are needed within communities and also at the level of cell-cell interactions. 

These needs could be met with a range of chemical sensors on scalable platforms, biosensors, and 

surrogate biosensors and bioreporters. Process- and 

product-specific gene(s) or protein targets need to 

be defined in order to guide sensor development 

in these areas. In addition to sensors that measure 

specific properties at a specific scale, approaches 

or tools are greatly needed for providing integrated 

signatures of macroscopic processes that are indicative of complex system transitions and behavior at 

scales relevant to ecosystem or climate predictions.
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 19

Chapter 2

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

Exploring Ecosystem Function 

and Elemental Cycling

2.12 Understand, predict, and manipulate the types 

and rates of ecosystem responses and feedbacks 

that result from and influence climate change.

As environmental and biological systems respond 

to climate change, new populations could begin 

to dominate communities, or important species 

could be lost, potentially leading to unanticipated 

consequences on the delicate balance of ecosystems. Relatively modest changes in climate could 

cause significant changes in organisms and ecosystems that provide essential functions and services. 

For example, the spread of new tree pests could 

threaten forest health, thereby amplifying carbon 

emissions through the loss of forest biomass. 

Another possible outcome could be the emergence 

of new plant pathogens or reservoirs of human 

pathogens that cause ripple effects on society, 

industry, the economy, and the environment.

Research should continue to fully explore the diversity, complexity, and feedbacks at work in natural 

environments subjected to multiple stress conditions including large-scale stress such as climate 

change. The challenge then is to understand the 

feedbacks between climate change and evolutionary changes in ecosystems—from the molecular 

to global level—and predict how these feedbacks 

will impact natural and human-altered environments. Meeting these challenges will require DOE 

investment in the development of new methods to 

explore and model ecosystem dynamics, evolution, 

and interactions. This will involve investigating 

evolutionary adaptation by population changes, 

genotype changes, plasticity, and the evolution of 

ecosystems when new organisms are introduced. 

A true systems biology approach to this problem 

needs a combination of experimental and computational methods to understand how things happen 

and predict what is likely to happen in situ and 

under various scenarios. To achieve a predictive 

understanding of biological response to climate 

and environmental change, research support for 

mathematical modeling should be integrated with 

data gathering and experiments designed to probe 

mechanisms for climate-driven changes in organisms and their ecosystems.

The connection between ecosystems and sustainable energy requirements is a key reason why 

BER should be a leader of this research involving 

biological systems at the climate-energy interface. 

For example, biologically derived fuels likely will 

play an increasing role in meeting U.S. energy 

needs and reducing carbon emissions in the near 

future, and the ability to produce such fuels on 

a large scale depends heavily on the interaction 

between climate and local ecosystems. In response, 

ecosystem changes can lead to shifts in patterns 

of human settlement, activity, and energy use. 

In another example, geoengineering has been 

discussed as a possible strategy to mitigate global 

warming (see sidebar, Understanding the Potential Consequences of Geoengineering, p. 32). For 

geoengineering to be considered a serious option, 

the potential large-scale effects—which cause 

planet-wide changes in climate—and local impacts 

on ecosystems and biological processes must be 

understood and put into context.

2.13 Deploy synthetic (or nonsynthetic) biology 

to understand and manipulate ecosystem function.

Even rudimentary developments of current 

synthetic biology can be tested and exploited for 

environmental and ecological use, albeit largely 

under laboratory or mesocosm simulation. To 

obtain useful, realistic measurements and kinetic 

data from natural and synthetic microbial communities without going into the field, a key 

challenge identified by the workshop participants 

is the ability to construct laboratory mesocosm 

environments that are quantitatively accurate 

representations of actual natural environments. 

These “realistic” mesocosms would provide a 

suitable environment for testing synthetic microbial systems in a controlled setting. BER has 

supported pioneering the use of designed organisms to enhance process understanding at the 

field scale (Ripp et al. 2000). Recent advances in 

genetic containment and control strategy greatly 
20 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Two: Grand Challenges in Biological Systems

improve the prospects for more expanded fundamental studies in this area to meet future needs. 

An example of a natural system where systems and 

synthetic biology approaches could be used to 

advance our mechanistic understanding of microbial activity is the interplay between microbialgeochemical transformations and water advection 

in the subsurface. This microbe-environment 

interaction exhibits intermittent and thresholdgoverned feedbacks characteristic of complexity. 

Systems-level laboratory and field experimentation could be tightly coupled with new top-down 

modeling strategies to address questions that 

could advance our ability to predict and manipulate microbial communities in dynamic natural 

environments. Key research questions include:

• Which environmental variables control microbial community structure and function?

• What are the relationships between structural 

and functional heterogeneity in microbial 

communities, and how do these relate to the 

changing properties of the natural physical and 

geochemical environment?

• Which forces drive or limit adaptation of 

individual populations and the microbial community to changing environmental conditions?

Targeted research on these and other topics can 

be projected to provide information on failure or 

success rates. Such research would aid in gathering fundamental knowledge to understand critical 

environmental parameters, structural and mechanistic processes, and impediments to future success.

2.14 Determine the molecular basis of robustness, 

fitness, and selection.

A major challenge in importing new functionality 

or populations into ecosystems is a lack of fundamental understanding on how new genotypes and 

phenotypes are selected and maintained within 

the systems. Whether they can be established 

in an unoccupied niche or must displace others 

from occupied niches is not well understood. Also 

unclear are the fundamental properties contributing to the fitness of organisms or genes to be introduced into and maintained in the system. Intricate 

networks, genetic exchange and information flow, 

and modularity of system components are expected 

to contribute to these properties, which need to be 

identified and characterized at the ecosystem level. 

However, these are largely untested hypotheses that 

must be evaluated to achieve future success. Indications suggest that modern agribusiness already may 

be exploring these issues from a systems perspective to delineate selective characteristics for which 

certain breeds may be more successful than others.

2.15 Develop a complete understanding of the 

biogeochemical cycles important to regulating 

carbon flux through biological systems.

Earth’s natural carbon cycle is driven by primary 

productivity and organic carbon decomposition. The dynamics of this cycle are essential to 

(1) food, fiber, and biofuel production; (2) carbon storage in biomass, soils, and sediments; and 

(3) atmospheric CO2 concentration. The extant 

biology and its environment determine the fluxes 

in the carbon cycle. Understanding, predicting, 

and wisely managing this cycle require a systemslevel understanding of microbes, plants, biological communities, and ecosystem biogeochemical 

processes. This systems approach is necessary 

because of complex interactions—both within 

biological communities and with controlling 

environmental parameters such as moisture, 

nutrients, soil structure, temperature, and light. 

In particular, the carbon cycle is highly dependent on nitrogen and phosphorus and, in certain 

geographic regions, other nutrients like sulfur, 

iron, and micronutrients. Thus, the carbon cycle 

cannot be productively understood in isolation. 

A challenge for the next 20 years is ascertaining 

how to link knowledge from the systems biology 

of the cell with that from ecosystem science in 

ways that provide mechanistic understanding at 

different levels of resolution. If successful, systems biology—now practiced at the level of the 

molecule, cell, and organism—becomes “systems 

biology of the biosphere.”
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 21

Chapter 2

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

2.16 Apply functional metagenomics to enable mass 

balance closure for biogeochemical cycles and transfer this information to biosystem design.

Given the need to couple dynamic biogeochemical cycling submodels with Earth system models 

for climate-consequence projections, applying 

metagenomics and systems biology approaches can 

help identify and understand uncharacterized biological components of the carbon cycle and other 

biogeochemical cycles. These insights can help 

reduce the uncertainty of long-term projections for 

atmospheric carbon levels and climate change. Our 

knowledge of biogeochemical cycles is not static—

only a decade ago, essential new processes were 

discovered, elucidating anaerobic ammonia oxidation in marine sediments. This discovery helped 

to complete the mass balance closure for nitrogen 

cycling in that system. Coupling carbon decomposition with dynamic aspects of GHG release in 

marine and terrestrial ecosystems appears essential 

for coupling of climate and global temperature 

predictions and related feedback responses.

Sequencing from environmental samples has 

enormous potential for advancing gene discovery, 

but the power of metagenomic analysis is severely 

limited in the ability to reconstruct more than a 

few genomes, let alone hundreds or thousands 

that exist in a given sample. Metatranscriptomic 

and metaproteomic approaches, as well as more 

advanced analytical techniques for exploring 

system response, must contend with the fact 

that recovery of nucleic acids and proteins from 

environmental samples is low (often <20%) and 

highly variable. These advanced analyses are critically important if gene network architecture can 

be developed to understand regulatory controls 

of processes within the system. Thus resolving 

low-abundance genes and transcripts in a complex, 

high-biomass background represents another 

important challenge. Perhaps a user facility, similar 

to the DOE Joint Genome Institute, to support 

metacommunity analyses should be established. 

2.17 Manage plant and microbe stress response 

to control carbon biosequestration and remediation 

of metals and radionuclides.

Using plants and microorganisms in remediation of contamination is well established but far 

from optimal in its application at broad scales 

(see sidebar, Application of Systems Biology: 

Environmental Remediation, p. 22). To achieve 

new solutions for environmental remediation, 

greater progress in engineering interactions 

between these biotic partners is needed. Another 

barrier to research progress in this area has been 

the limited interest in interchanging component 

parts to create new products or processes that 

can support large-scale engineering use. Developing rhizosphere communities to help plants deal 

with nutrient and essential element acquisition 

during hydrologic or temperature stress may 

be one approach to maintain sustained rates of 

primary production during less-than-optimum 

growing conditions. Likewise, channeling excess 

photosynthate to the rhizosphere could promote 

biopolymer synthesis for metal immobilization 

or control the redox environment to accentuate 

metal or radionuclide reductive conditions. We 

may need to conceptualize a “virtual rhizosphere” 

(perhaps analogous to the “virtual liver” for 

toxicogenomic studies by the U.S. Environmental 

Protection Agency and the National Institutes of 

Health) to begin assembly of the tools, models, 

and databases that can provide the necessary 

capabilities for complex system research in ecosystem analysis.

The response of natural ecosystems to environmental stress is largely a result of interactions 

among species comprising the ecosystem. Positive 

and negative feedbacks of these interactions are 

analogous to those observed for gene regulatory 

networks and metabolic flux within individual 

cells. These interactions are hierarchical and may 

be manifest at the system level but unobservable. 

Regardless, they may act as buffers or mediators of 

change, acclimation, adaptation, or system evolution. In natural soil and subsurface environments, 
22 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Two: Grand Challenges in Biological Systems

interactions between the microbial community, 

minerals, dissolved constituents, and water flow 

occur within a heterogeneous physical framework. Future research on interactions within these 

environments needs to transcend reductionistic 

mechanisms and integrate with metacommunity 

analysis. A particular focus should be placed on 

understanding biogeochemical dynamics at the 

mineral-microbe interface, microbial community 

responses to variable environmental conditions, 

biogeochemical reaction rates in heterogeneous 

media, and feedbacks between biogeochemistry 

and water flow. This continuous, iterative process 

ultimately will lead to useful models and improved 

understanding of in situ biological systems. 

Horizontal and interspecies transfer of genetic 

information is well understood. Mechanistically, 

these processes, whether by conjugation, transformation, or transduction, have been demonstrated 

at the microbial level in the environment. Transfer 

mechanisms from microbe to plant also are well 

known, co-evolutionarily linked, and biotechnically 

exploitable. Unfortunately, there are few genetically developed systems for organisms from nature, 

but comparative genomic information already 

has provided further evidence that microbe-plant 

transfer mechanisms are perhaps more common 

than anticipated. This is indicated by the frequency 

of prokaryotic sequences residing in the genomes 

of sequenced host species. A recent example is the 

occurrence of prokaryotic intracellular endophytes 

in Populus and the co-occurrence of gene homologs 

in the plant genome (personal communication 

Jerry Tuskan, Oak Ridge National Laboratory).

2.18 Define the fundamental microbial basis 

for permafrost carbon-methane transformations.

The consequences of large-scale carbon storage 

in permafrost or in labile carbon pools in frozen 

Russian Yedoma sediments remain unknown and 

problematic. The mass of material, if released as 

GHGs in short time intervals, would aggravate 

Background image is a 

scanning electron micrograph of microbes and 

particles sampled from a 

subsurface community 

at a contaminated site in 

Rifle, Colorado. Inset shows 

transmission electron 

micrograph of a single 

microbe from this community with dark nanocrystals 

of iron and sulfur forming at 

the cell membrane. [Image 

credit: Williams et al. 2009.]

Application of Systems Biology: Environmental Remediation

Contamination of soils and sediments 

by radionuclides, heavy metals, and 

organic chemicals at sites that were once 

part of the nuclear weapons complex 

remains a longstanding legacy for the U.S. 

Department of Energy and its predecessor agencies. Some of these contaminants 

have escaped their original containment 

structures, and those that are soluble have 

moved with the groundwater and present 

human and environmental risks. Certain 

microorganisms within natural subsurface 

communities can reduce problematic 

radionuclides and metals, rendering them insoluble 

and blocking their further movement (see figure). 

Several can oxidize organic pollutants and some 

metals, and others can bind or sequester pollutants. 

Metal reduction and other types of remediation processes will need to be sustainable over many years. 

Scientific progress that can bring lasting solutions to 

these problems will require a new understanding of 

very long term interactions among microorganisms, 

minerals, migrating fluids, and dissolved constituents 

over a wide range of scales. The long-term challenge 

is to advance from the mechanistic knowledge of 

potentially useful microbial processes to a systemslevel, predictive understanding that enables optimization of microbial functions in situ.
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 23

Chapter 2

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

atmospheric CO2 greatly. Yet, turnover of plant 

residues is a complex process with many routes, 

which may or may not lead to GHG releases. Many 

microbial guild assemblages participate in the process, often competing for carbon sources, terminal 

electron acceptors and donors, and dependency for 

balanced micro- and macronutrients—all of which 

influence terminal carbon releases. The opportunity 

exists to intercede at any one of these levels to alter 

the course of carbon releases.

2.19 Develop designs for optimizing carbon flow for 

biomass production, carbon allocation, and biosequestration to reduce rates of atmospheric CO2 accumulation and to increase terrestrial carbon storage by 50% 

in 20 years.

An estimated 55% of the CO2 released annually 

from human activities remains in the atmosphere; 

the rest is taken up by the oceans and terrestrial 

environment (IPCC 2007). Since 1960, the 

percentage of human emissions incorporated into 

natural carbon sinks has decreased 5% and may 

be expected to diminish further with time (see 

sidebar, Optimizing Terrestrial Biosequestration of 

Carbon, p. 24). Microbial biomass is the dominant 

form of fixed carbon in short-term pools, exceeding that of higher plants. It is entirely feasible to 

design systems to increase net primary production 

and CO2 fixation, alter heterotrophic ability to 

fix CO2 in soils, inhibit methane production and 

escape, and store carbon in novel exo-biopolymers. 

A challenge should be undertaken to demonstrate 

this capability at the microcosm and mesocosm 

scale, with the intention to have a field-deployable 

system available within 20 years.

2.20 Determine carbon and nutrient dynamics 

in natural systems.

Critical discrimination among anthropogenic, natural, and “background” carbon sources is needed to 

predict biotic fluxes and the efficacy of remediation 

efforts, as well as to measure, report, and verify 

reductions in carbon emissions. Improvements in 

the ease-of-use or availability of isotopic labeling 

techniques also are necessary to provide analytical 

fingerprints and differentiate fossil-derived 

nutrient and carbon sources from those generated 

via contemporary cycling from biomass sources. 

The ability to discriminate between “natural” 

(ecosystem) driven versus purely anthropogenic 

carbon emissions from agricultural, urban, and 

industrial sources is needed for both experimental 

and modeling purposes. These needs extend not 

only to the atmospheric environment, but also to 

soil, water, and plant and microbial biomass, as 

well as to the small metabolite pool in the environment. In addition to advancing isotopic 

techniques, these improvements also are needed 

for developing reference fingerprints relative to 

carbon or nutrient pool composition with 

particular relevance to discriminating slow 

(recalcitrant) and fast turnover pools.
24 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Two: Grand Challenges in Biological Systems

Optimizing Terrestrial Biosequestration of Carbon

I

n the past 50 years the capacity of terrestrial 

and ocean sinks to take up CO2 has decreased 

by 5%, and this ominous trend is expected to 

intensify. Given the current anthropogenic release 

of 10 petagrams of carbon per year (Pg C/y), the 

cumulative effect of losing this natural capacity 

for carbon storage will continue to exacerbate 

the atmospheric CO2 level. As a result, elevated 

CO2 may contribute to changes in forest-coupled 

climate-carbon dynamics that potentially could 

release 100 to 510 Pg total carbon by 2100. An 

additional 100 to 750 Pg could come from permafrost thawing and decomposition by 2100. Since 

land sinks are estimated to take up 30% (or 3 Pg 

C/y) of annual anthropogenic carbon emissions 

(Canadell et al. 2007), a 50% improvement in this 

number would represent a very significant carbon 

mitigation effect.

Mechanistically, synthetic biology could accomplish this mitigation by increasing carbon sequestration in above- and belowground biomass 

through a variety of approaches that could channel plant carbon allocation to the rhizosphere, 

produce belowground carbon storage biopolymers, and shunt carbon metabolites away from 

pathways that release the greenhouse gases CO2 

and methane (CH4). Channeling excess photosynthate or metabolites for eventual rhizosphere 

excretion into productive pathways for biosynthesis would be the goal. Biological approaches to 

address this goal include

•	 Enabling the conversion of C3 plants to C4 using 

pathway reconstruction, perhaps by adding a 

phosphoenolpyruvate carboxylase.

•	 Directly modifying ribulose bisphosphate carboxylase by reducing O2 fixation and increasing 

enzyme efficiency for CO2 fixation.

•	 Increasing heterotrophic CO2 fixation competency by engineering carboxysomes (bacterial compartments containing carbon-fixation 

enzymes) into soil microbes.

•	 Altering root cellulose or lignin content with 

the intent of long-term renewable carbon storage harvestable as belowground biomass for 

biofuel feedstocks.

•	 Developing rhizosphere inocula with pathways 

that could shunt carbon into storage polymers. 

In addition to enhancing intermediate-term 

carbon storage, these polymers would improve 

soil condition and water-holding capacity. For 

example, novel pathways for high-molecularweight heteropolysaccharides are being discovered for wastewater microorganisms, which 

produce the polymers during growth on lowmolecular-weight organic acids such as acetate 

(a prime substrate for methanogenesis) (Allen 

et al. 2004). Pathways from these wastewater 

microbes could be engineered into novel rhizosphere bacteria that generate carbon-storage 

polymers from substrates for methanogenesis.

The ribulose 

bisphosphate 

carboxylase 

enzyme 

(RuBisCO) fixes 

CO2 . [Image 

credit: Sandia 

National 

Laboratories.]

By enhancing microbe-root interactions and altering root content, carbon storage in plant and soil 

biomass could be improved.
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 25

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

Chapter 3

Today, we face the challenge of providing energy services for a growing and 

increasingly affluent human population 

(especially in the developing world) while maintaining a sustainable climate system. Although 

difficult, this problem is one of the most important for society to confront and overcome. 

The Department of Energy’s (DOE) Office of 

Biological and Environmental Research (BER) is 

poised to play a central role in meeting this challenge through its support of the basic science 

underpinning potential solutions (see sidebar, 

BER Contributions to Climate Research, p. 27).

Twenty years from now, policymakers will be 

planning how to adapt to predicted climate 

change and to effectively mitigate further 

changes. Climate science will be able to meet 

those needs—with more accurate climate predictions and more detailed assessments of the 

overall impacts of new technologies—provided 

we improve scientific understanding of the climate system and human interactions with it. We 

envision six grand challenges (see box, Summary 

of Research Recommendations, p. 26). Surmounting them will require a deeper insight into 

the following Earth system processes:

• Ice sheet and glacier dynamics.

• Clouds, aerosols, and precipitation.

• Global biogeochemical cycling of carbon, 

water, and other natural compounds.

• Soils and terrestrial vegetation, including 

human decision making about crop choices 

and management.

• Groundwater resources.

• Biological interactions and feedbacks.

• Anthropogenic greenhouse gas (GHG) 

emissions.

• Interactions among all of these processes in 

the human-influenced Earth system.

Elucidating these processes will allow us to more 

adequately model their effects on climate and 

the degree to which change in the climate system 

affects these processes.

Comprehensive observation, analysis, diagnosis, 

and prediction of all Earth system components 

are required for successfully addressing the 

grand challenges. Today’s operational analysis 

is adequate for weather prediction but disconnected from comprehensive Earth system 

modeling. Observing technologies developed 

20 years from now should be able to assimilate 

the broadest possible range of observations into 

comprehensive Earth system models (ESMs). 

The resulting ESMs will document the changing state of the Earth system and predict many 

aspects of global change. Such integrated analyses and predictions will provide fundamental 

information to policymakers balancing decisions 

about mitigation of and adaptation to climate 

change with other societal needs.

3 Grand Challenges in Climate Research

“The Earth is the only world known so far to harbor life. There is nowhere else, 

at least in the near future, to which our species could migrate. Visit, yes. Settle, 

not yet. Like it or not, for the moment the Earth is where we make our stand.”

— Carl Sagan. 1994. Pale Blue Dot: A Vision of the Human Future in Space.
26 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Three: Grand Challenges in Climate Research

ESMs of 2030 will address coupled interactions 

among the atmosphere, ocean, land, sea ice, land 

ice, biogeochemistry, dynamic vegetation, lakes and 

soils, aerosols, chemistry, and changes in energy 

technologies and resource management. They 

will represent the reservoirs and evolving flows of 

energy, water, carbon, nitrogen, and other species.

These comprehensive ESMs also must incorporate 

human activities—such as global energy systems 

and land use—that affect climate and in turn 

respond to changes in climate, freshwater availability, and sea level. Policymakers will be able to 

use comprehensive ESM simulations to explore 

alternative combinations of adaptation and mitigation strategies that will shape changes in both society and climate for the next century and beyond. 

Therefore, ESMs must enable investigations of 

future evolutions of energy and land-use systems, 

the consequences for the physical climate system 

and biogeochemical cycles, and potential human 

adaptations. To pursue targeted model refinements 

and use predictions effectively, the uncertainties 

associated with ESM predictions must be quantifiable and also attributable to their underlying causes.

Grand Challenge Research 

Recommendations

Earth System Models

3.1 Develop higher-resolution models to integrate 

many more relevant processes than current models 

and to describe climate change over much longer 

timescales.

A major scientific challenge is to create models that 

accurately represent the evolution of the entire Earth 

system. Models have evolved from ones representing 

only the atmosphere to those that couple the atmosphere, ocean, and land surface and recently to ESMs 

with active carbon cycles. Now abundantly clear is 

that a much more complete, process-based model 

representation of the Earth system will be needed 

to understand in detail how the system operates and 

to provide information about how it might evolve. 

This representation will take into account not only 

physical processes but also biogeochemical cycles, 

hydrological and biological interactions and their 

feedbacks, and human decision making.

Earth System Models

3.1 Develop higher-resolution models to integrate 

many more relevant processes than current 

models and to describe climate change over 

much longer time scales.

Cloud and Aerosol Processes

3.2 Improve parameterizations and basic knowledge about aerosols that affect clouds and 

factors that control their number and concentration for microphysics, radiative transfer, and turbulence processes to quantify indirect aerosol 

forcing and resulting precipitation changes.

Ocean and Terrestrial Processes

3.3 Develop ecosystem-observing systems to 

monitor biogeochemical cycles, estimate critical process parameters, and provide model 

tests in ocean and terrestrial biospheres, 

including subsurface soils.

Biological Processes

3.4 Advance understanding of important biological 

interactions and feedbacks to identify potential 

tipping points and possible mitigation strategies such as carbon biosequestration.

Human Interactions

3.5 Improve integration of anthropogenic climate 

forcings into Earth system models (ESMs) and 

develop new techniques to evaluate these coupled models at both global and regional scales.

Observing Systems

3.6 Establish new observational technologies and 

use them to comprehensively compare ESM 

predictions with observations and to quantify 

model errors.

Summary of Research Recommendations
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 27

Chapter 3

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

Increases in model resolution will be dramatic, 

with approximately 1-km global grid spacing for 

the atmosphere and 1- to 5-km resolution for 

oceans. Achieving even higher resolutions would 

be useful but may be unfeasible for climate simulations in 2030. A 1-km grid spacing is sufficient to 

allow many processes treated very simply in today’s 

models to be represented using a “first-principles” 

approach. Such resolution also is comparable to the 

scale at which many observational measurements 

are made. Surface boundary condition components (including transient current, historical, and 

potential future conditions) related to soils and 

vegetation must be improved greatly to simulate 

surface-atmosphere interactions fully and more 

effectively at local and regional scales.

The 1-km atmospheric grid—combined with 

more realistic parameterizations of turbulence, 

cloud-particle microphysics, and radiation—will 

significantly improve simulations of clouds and 

convection as well as major weather events like 

tropical cyclones. This resolution also will allow 

more accurate integration of atmospheric chemistry, land- and ocean-surface carbon and nitrogen 

interactions, and soil and groundwater processes 

into the models. Such integration subsequently 

will enable analysis and better prediction of the 

radiative forcing by short-lived species such as 

aerosols. Even at this resolution, breakthrough 

developments are needed to determine how to 

translate information about disparate, coupled 

processes and phenomena across scales—from 

the molecular and pore to the local, regional, or 

The Office of Biological and Environmental 

Research (BER), within the Department of 

Energy’s (DOE) Office of Science, has played a major 

role in advancing climate change research, both 

through its own research programs and the provision of major facilities and resources to further the 

scientific community’s progress. Specifically, BER 

has collaborated with the National Science Foundation and University Corporation for Atmospheric 

Research to support the development of the Community Earth System Model (formerly the Community Climate System Model). Through collaboration 

with DOE’s Office of Advanced Scientific Computing 

Research, BER also has made available the country’s 

most advanced computational facilities for climate 

research, including simulations for Intergovernmental Panel on Climate Change (IPCC) assessments.

Climate model intercomparisons, which are now an 

important part of the international climate research 

enterprise, began in the late 1980s under BER support, notably continuing through the Atmospheric 

Model Intercomparison Project. BER’s Program for 

Climate Model Diagnosis and Intercomparison 

(PCMDI) at Lawrence Livermore National Laboratory 

has provided essential data management support to 

IPCC and other major national and international climate modeling projects. 

Additionally, BER’s 

support for 

integrated 

modeling of 

energy and 

land-use 

systems has 

provided 

important 

insights into 

the various ways that anthropogenic perturbations 

to the carbon cycle might evolve. 

BER also has been very active in the collection of 

climate data. The Atmospheric Radiation Measurement Climate Research Facility has gathered 

invaluable field data used for testing the cloud and 

radiation components of climate models. BER’s longterm support for fundamental research on the carbon cycle, especially its terrestrial components, and 

the response of plants to enhanced atmospheric 

CO2 has provided important process-level knowledge about the current carbon cycle, as well as 

some of the responses to a changed climate future.

BER Contributions to Climate Research

Image credit: Department of Energy’s 

Atmospheric Radiation Measurement 

Climate Research Facility.

Ground-based scanning radiometer
28 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Three: Grand Challenges in Climate Research

global scale of the prediction. The models and 

approaches might be mechanistic, mathematical, 

statistical, or phenomenological in nature. New 

variants of volume averaging, hybrid mixture 

theory, statistical and continuum mechanisms, 

and decomposition methods are necessary for 

addressing important scale transitions.

These very high resolution models will be made 

possible in part by exponential increases in computer power. Multiple machines must be dedicated 

to running ESMs, and each must be capable of 

delivering a sustained exaflop (1018 floating point 

operations per second) or more. Although this 

requirement is well recognized, its importance cannot be overstated.

ESMs are complex in terms of their conceptual 

basis, embodiment as computer programs, and 

sheer number of variables and parameters. New 

approaches are needed to deal with this complexity, 

especially for analyzing and understanding model 

results. As ESMs evolve to include more complex 

and interactive processes, a system approach, as well 

as improved process representations and understanding, will be needed to explore nonlinearity 

in the human-Earth system. Such investigations 

will help improve capabilities for predicting abrupt 

climate changes.

Despite the emphasis on increasing resolution, 

ESMs with lower resolutions still will play a role in 

climate modeling, especially for long-term simulations. Changes in ice sheets, for example, are key to 

understanding and predicting critically important 

shifts in sea level. Simulations of ice-age cycles with 

low-resolution ESMs will provide crucial tests of 

ice-sheet models and other ESM components. They 

also will offer powerful evidence for the ability of 

ESMs to predict future climate change and will 

serve as useful checks of many ESM components, 

including representations of the carbon cycle. These 

simulations will reveal insights into questions such 

as, what determines the lower and upper limits of 

CO2 and methane concentrations in the cold and 

warm phases of an ice-age cycle? Can the time scales 

of past high sea-level stands and the subsequent 

initiation of ice ages be properly simulated? Simulations of ice-age cycles also will provide new knowledge about the nature and predictability of abrupt 

climate fluctuations that modulate longer cycles.

Research challenges related to other key processes 

requiring better integration into ESMs are outlined 

in the following sections.

Cloud and Aerosol Processes

3.2 Improve parameterizations and basic knowledge 

about aerosols that affect clouds and factors that 

control their number and concentration for microphysics, radiative transfer, and turbulence processes 

to quantify indirect aerosol forcing and resulting 

precipitation changes.

Higher model resolution cannot solve all problems. With 1-km grid spacing, parameterizations 

still will be needed for at least three types of 

physical processes:

• Microphysics of the creation and evolution of 

cloud particles and aerosols.

• Radiative transfers of the flows of solar and terrestrial (Earth-emitted infrared) radiation.

• Turbulence.

The greatest remaining uncertainty in atmospheric 

models in 2030 probably will be associated with 

microphysics. However, uncertainties in the interactions of turbulence dynamics, radiation, and 

microphysics are coupled in ways not included in 

today’s global models. Moreover, the most poorly 

understood of these small-scale processes (e.g., 

formation of liquid and ice particles) are precisely 

the targets of many geoengineering proposals 

(see sidebar, Understanding the Potential Consequences of Geoengineering, p. 32).

A combination of new and traditional approaches 

will be needed to make progress in this area. 

Traditional approaches include field experiments 

in which the most detailed data are gathered 

from ground, aircraft, and balloon platforms. 
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 29

Chapter 3

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

Extended support is crucial to advance current 

instruments used for in situ measurements and 

to develop additional technologies, especially 

for fully characterizing ice crystal properties 

and emulating cloud-particle formulation or 

evolution (e.g., ice nucleation). New approaches 

can include direct numerical simulation of the 

evolution of cloud particles in a “control volume” 

and experimental manipulation of cloud systems 

(e.g., to study the effects of aerosols on liquid and 

ice water content, effective radius, particle size 

distribution, and precipitation). New observing 

systems and a means of accurately interpreting 

resultant observations are needed to detect such 

effects and transfer the information into a modeling framework. Links with the natural biological 

system also should be quantified (e.g., bacteria 

can seed cloud particles, and organic particles are 

produced from vegetative emissions).

Today’s climate models are unable to resolve interactions among radiation, microphysics, and turbulence dynamics. New approaches also include 

extending the ability of these large-scale models 

to appropriately address their inadequacies, either 

through very high resolutions in limited domains 

(e.g., 50-m resolution) or through improved 

parameterizations. Such approaches will allow us 

to better quantify indirect forcing by aerosols and 

changes to precipitation driven by aerosols.

Ocean and Terrestrial Processes

3.3 Develop ecosystem-observing systems to monitor biogeochemical cycles, estimate critical process 

parameters, and provide model tests in ocean and 

terrestrial biospheres, including subsurface soils.

A 1- to 5-km grid for modeling oceans will resolve 

eddies and frontal and coastal processes, all of 

which are crucial for the exchange of properties 

with the atmosphere. For example, the vertical 

transport of properties in convectively unstable 

regions (such as the Nordic Seas) clearly is affected 

by secondary circulation of baroclinic eddies, 

which need to be resolved. We now know that only 

when sea surface temperatures in the Gulf Stream 

frontal zone are resolved down to tens of kilometers is their signature expressed in the tropopause, 

affecting the position and strength of storm tracks. 

Coastal regions 1 to 10 km wide are where much 

of oceanic upwelling and downwelling occur. Here, 

vertical transport of heat and nutrients has important feedbacks on atmospheric circulation (e.g., the 

position of the intertropical convergence zone) and 

economic impacts on fisheries.

Through sustained monitoring of both the oceanic 

uptake of heat, carbon, and other atmospheric 

constituents and the associated ocean acidification 

and sea-level rise, we can evaluate high-resolution 

models and use them to simulate remediation scenarios. Parameterized ocean turbulence from the 

microscale, including internal waves, will remain a 

source of uncertainty. At this scale, process models 

and experiments will remain the methods of 

choice for understanding turbulence and designing 

realistic parameterizations.

The terrestrial surface also responds to change in 

ways that promote significant positive feedbacks to 

climatic warming. There are model-based indications of such feedbacks, as well as biogeographically based computations and observations of CO2 

change lagging warming in the Little Ice Age and 

Pleistocene deglaciation events. Arguments suggest that these feedbacks may be strongly positive, 

neutral, and even negative. Clearly, this global feedback question needs to be better understood in the 

context of other major Earth systems (atmosphere 

and ocean).

Developing the ability to predict changes in soil 

moisture and groundwater on seasonal and longer 

time scales is imperative for modeling terrestrial 

ecosystems and their interactions with the rest of 

the climate system. Soil moisture strongly controls 

plant productivity in many terrestrial ecosystems and also influences runoff, stream flow, and 

numerous other key ecosystem services. Below 

the upper half meter of soil, water flow through 

a fractured medium and hydraulic redistribution 
30 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Three: Grand Challenges in Climate Research

by deep roots are two of many poorly understood 

hydrological processes that alter soil moisture 

near the surface and vertical distribution of moisture to the water table.

The subsurface—actively linked to the atmosphere 

and biosphere through the hydrological and carbon 

cycles—serves as a storage location for much of 

Earth’s fresh water. Coupled hydrological, microbiological, and geochemical processes occurring 

within the subsurface environment cause the local 

and regional natural chemical fluxes that govern 

water quality. To advance our predictive capability 

of feedbacks to the climate system and the impacts 

of climate on groundwater availability and quality, 

long-term watershed-level observing systems are 

needed. These systems would include measurements of the subsurface and biosphere, perhaps 

focusing on the Arctic and tropics, which are terrestrial systems with large carbon stores.

As predicted years ago, climate change has been 

particularly rapid in the Arctic, as the insulation 

capacity of thick sea ice changes and the positive 

sea ice–albedo feedback operates. Furthermore, 

releases of permafrost carbon stores in a warming 

Arctic represent another possible strong positive feedback on climate warming (see sidebar, 

Potential Permafrost Loss Poses Growing Concern, p. 31). Future ecosystem-observing systems 

might be modeled after those developed by DOE’s 

Atmospheric Radiation Measurement Climate 

Research Facility to monitor clouds and radiation.

Soil microbiology is an essential component of 

the Earth system. Although dynamic vegetation 

models have been developed, dynamic soil models still are needed. Such models should represent 

potential future changes in microbial populations 

regulating the release and consumption of trace 

gases to the atmosphere, while acknowledging 

important scaling limitations. Better representation of the processes controlling trace-gas emissions also could provide important synergies with 

BER efforts in microbial genomics and sequencing. Next-generation models also need to include 

additional subsurface resolution and processes 

that control a large fraction of carbon and nitrogen dynamics, water availability for plant growth, 

and potential hydrological feedbacks to the 

physical climate system. To support development 

and evaluation of soil and vegetation models, we 

need—as a first priority—to create databases 

on global soil characteristics, soil depth, and 

above- and belowground vegetation properties. 

Particularly significant in these calculations is the 

depth of soil carbon active in the global carbon 

budget under different conditions. Changes in 

the active soil depth strongly affect the amount 

of soil carbon, which already is the dominant 

storage pool of terrestrial carbon from a global 

perspective. Also required are detailed, long-term 

soil measurements in multiple landscapes across 

the globe.

Oceans and land contribute to atmospheric composition too. These contributions include reactive 

gases and particles that are significant because of 

their role in total radiative forcing, influences on 

human health, and impacts on biological productivity. The carbon feedback has not been well 

quantified, although some studies suggest that it 

may be strongly positive. Nitrogen dynamics also 

are important for determining primary production, particularly because of the large anthropogenic magnification of the nitrogen cycle. We need 

to build a predictive, process-based capacity for 

understanding trends in the abundances of various 

atmospheric chemical species and enhance the ability to simulate and monitor potential futures.

Biological Processes

3.4 Advance understanding of important biological 

interactions and feedbacks to identify potential tipping points and possible mitigation strategies such 

as carbon biosequestration.

There is broad agreement that the types of changes 

being projected in the physical climate system will 

affect not only a multitude of biophysical and biogeochemical processes, but also the future viability 
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 31

Chapter 3

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

of both terrestrial and marine ecosystems. Disturbance processes such as fires and direct human 

disturbances like deforestation, reforestation, and 

agricultural intensification need to be incorporated 

into ESMs. Responses of these major ecosystem 

components and their sensitivities to climate 

variability and change need to be understood to 

simulate longer-term consequences.

The paired biological processes of photosynthesis 

and respiration dominate global carbon cycling. 

Most organic matter formed by photosynthesis 

is rapidly returned to the atmosphere as CO2

through respiration by plants and microbial 

decomposers, but a small fraction is temporarily 

removed from active cycling by biosequestration. 

Current estimates are that terrestrial processes 

Permafrost (i.e., permanently 

frozen subsoil) currently 

occupies 18.8 x 106 km2, or 16%, 

of global soil area. More important, permafrost now sequesters 

an estimated 1672 Pg (1.67 ×

1012 metric tons) of organic 

carbon. Concern over this soil 

has emerged because current 

models for global heating indicate an 80% to 99% reduction 

in permafrost area by 2100. This 

warming and the concomitant 

action of soil microbial populations could convert stored 

permafrost carbon into CO2 or, 

under anaerobic conditions, CH4 (methane)—both potent greenhouse gases (GHGs). Although model simulations differ, permafrost thawing would be expected to add 100 to 750 Pg (1 to 7.5 x 1011 metric tons) of 

carbon to the atmosphere. For comparison, total anthropogenic carbon releases are estimated to be 10 Pg 

annually. Consequently, permafrost thawing has tremendous potential for significantly increasing the levels 

of GHGs released into the atmosphere as a result of heightened microbial activity.

Currently, no model can accurately predict the effects of heating on permafrost. Turnover of plant residues 

as permafrost thaws is a complex process with numerous routes that may or may not lead to predictable 

releases of GHGs. Many microbial guild assemblages participating in the process often compete for carbon 

sources, terminal electron acceptors and donors, and micro- and macronutrients—all of which influence 

terminal carbon releases. Understanding the systems biology of microbial cells and microbial communities at both subzero and above-zero temperatures and under a variety of soil moisture and geochemical 

conditions could reveal novel physiology and regulatory controls. Such insights would enhance our understanding and prediction of changes already under way in permafrost regions and their feedbacks on global 

change. Indeed, opportunities may exist to intercede at any one of these levels to alter the course of carbon 

releases at both the molecular and organismal scale. From a design perspective, organisms or pathways that 

alter carbon flow from methane into biopolymers requiring gluconeogenesis exist and could be employed 

in large-scale mitigation strategies.

The biology and climate of permafrost regions—including the effects of both soil microbial and plant 

communities—represent an important and exciting opportunity to integrate the climate and environmental 

research missions of the Department of Energy’s Office of Biological and Environmental Research.

Image credit: iStockphoto.

Potential Permafrost Loss Poses Growing Concern
32 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Three: Grand Challenges in Climate Research

have prevented about 30% of anthropogenic 

emissions from remaining in the atmosphere. 

First-generation climate–carbon cycle models 

predict that the biosphere’s capacity to sequester 

anthropogenic carbon will peak by midcentury, 

and current evidence suggests that the airborne 

fraction of these emissions began to increase 

in the first decade of this century. The lack of 

adequate understanding about so-called “tipping points” introduces enormous uncertainty 

into longer-term climate projections. Examples 

of these tipping points include the potential 

for huge organic carbon releases resulting from 

warming of high-latitude permafrost or the 

increased frequency of forest disturbances, such 

as large-scale beetle outbreaks that become more 

prevalent as temperatures rise. Although critically important, many of these biological carbon 

cycling processes are absent or only minimally 

represented in first-generation climate models.

Twenty years from now, the new generation of 

ESMs will benefit from improved understanding of the biological processes driving the global 

carbon cycle and greater integration of them into 

biogeochemical models. The biosphere also plays 

a vital role in regulating the global water cycle, 

although the processes involved are more dynamic 

and take place at smaller geographic and temporal 

scales. A pressing need will be to develop tests of 

climate models so that their predictions can be used 

confidently to foresee future climate and ecosystem services and to prioritize mitigation strategies 

based on their effectiveness. These tests and current 

estimates rely on empirical land data that are highly 

uncertain. In contrast, emissions from fossil fuel use 

and cement production are relatively certain, large, 

Clearly, humans are strongly perturbing the 

Earth system, and their impacts are increasing 

with time. These impacts are inadvertent today, but 

there is a possibility that humanity will choose to 

deliberately manipulate—or “geoengineer”—small 

components of the Earth’s climate system. This process introduces a host of potential risks and rewards, 

and research is needed to explore potential consequences and outcomes to the planet. So far, two 

geoengineering approaches have been suggested:

•	 Carbon Dioxide Removal (CDR)

•	 Solar Radiation Management (SRM)

We must be able to predict, with error bars, the 

potentially broad consequences of geoengineering 

on society and the Earth system.

Some possible early steps in geoengineering 

provide an opportunity to improve our knowledge 

of fundamental aspects of the climate system critical 

for predicting climate change. For example, an 

experiment designed to perturb the brightness of a 

climatically negligible cloud system for brief periods 

over a small area could verify our understanding of 

cloud-aerosol interactions. These interactions are 

crucial components 

of the climate system 

and are responsible 

for major uncertainties in characterizing 

climate change. 

However, care must 

be taken to quantify 

both large- and 

smaller-scale 

changes that may 

take place. Comparing small but measurable perturbations to 

the predicted 

behavior in a model 

provides an unprecedented opportunity to confirm 

our understanding and conduct real hypothesis 

testing. This kind of experiment provides vital 

information about the climate system and also is a 

critical first step for an SRM “cloud whitening” 

strategy. Similar opportunities can be constructed for 

other components of the climate system and for 

different geoengineering strategies.

Image credit: Department of 

Energy’s Atmospheric Radiation Measurement Climate 

Research Facility.

Understanding the Potential Consequences of Geoengineering
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 33

Chapter 3

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

and rapidly increasing (Le Quéré et al. 2009, van 

de Werf et al. 2009, GCP 2009). Therefore, efforts 

need to be directed at improving not only models, 

but their underlying data as well.

Human Interactions

3.5 Improve integration of anthropogenic climate forcings into ESMs and develop new techniques to evaluate 

these coupled models at both global and regional scales.

Energy production, its transformation and use, 

and direct land-use change make up the major 

anthropogenic forcings of the current climate 

system that release CO2 , other GHGs, and aerosols (including short-lived species) to the atmosphere. In addition, land-use practices (e.g., rice 

cultivation and fertilizer applications on crops) 

also are responsible for significant fractions of the 

non-CO2 GHG and reactive gas budgets affecting atmospheric composition both regionally 

and globally. Furthermore, land-cover changes and 

water management both control the disposition 

of energy from the surface into radiative, sensible, 

and latent heat fluxes—potentially altering climates on local scales.

Integrated assessment models (IAMs) simulate 

these types of emissions as functions of economic 

decisions on energy, technology, and land use but 

do not incorporate the social, political, or cultural 

influences of land-use change (CBES 2009; see also 

Chapter 4: Grand Challenges in Energy Sustainability, p. 37). Increasingly, IAMs interact with ecosystem and atmosphere models and also incorporate 

responses to mitigation and adaptation measures.

Because humans play such an important role in 

shaping anthropogenic climate forcing through 

industrial, energy, and land systems, coupling IAMs 

to ESMs will enable a more complete representation of the Earth system. Understanding the history 

of land-use change and its contributions to total 

atmospheric forcing over the past several centuries 

also is a critical component of such work. Nevertheless, it is important to recognize that, despite 

the utility of developing comprehensive ESMs, 

the ability to project effects of human decisions 

is limited. New techniques thus will be needed to 

evaluate coupled ESM and IAM models at both 

global and regional scales.

Observing Systems

3.6 Establish new observational technologies and 

use them to comprehensively compare ESM predictions with observations and to quantify model errors.

To create a comprehensive analysis of the Earth 

system’s evolving state, a very broad range of observations will need to be combined and the data 

assimilated with ESMs. Key goals are to:

• Monitor evolving reservoirs of carbon, heat, and 

freshwater and quantify and attribute errors in 

model results.

• Understand the diversity of forces influencing 

changes in land management and type, potentially through in situ measures of land changes. 

Observing spectrally resolved trends in radiation flows—at both the top of the atmosphere 

and the Earth’s surface—also may be possible, 

along with relating those trends to changes in 

clouds, water vapor, aerosols, snow and ice, land 

use, and groundwater resources.

• Determine, through observations and modeling, the rates of oceanic uptake of carbon and 

heat and the subsequent impact on marine 

productivity, ocean acidification, and sea-level 

rise, among other things. Measurements of the 

distributions and flows of carbon, hydrogen, 

and oxygen isotopes also can be particularly 

useful for documenting both physical and biological processes.

• Obtain much better information on surface 

boundary conditions, including vegetation 

characteristics and the global distribution of 

soil properties (e.g., depth, texture, infiltration, 

permeability, and nutrient levels). Soils, and 

belowground processes in general, are poorly 

understood and frequently misrepresented in 

current models. This inadequacy and poor representations of hydrological processes in current 
34 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Three: Grand Challenges in Climate Research

models result in significant model disagreement 

on how land-cover changes affect climate. These 

challenges must be resolved before models can 

be used effectively for regional-scale attribution 

of climate change.

Major investment in new observational technologies and tools is needed to enable comprehensive 

comparisons of ESM predictions with observations, as well as quantification of model errors. 

This will entail new strategies for assimilating 

high-resolution spatiotemporal observations of 

the hydrological cycle into improved atmospheric 

models that can resolve the coupling between 

hydrological processes and atmospheric motions at 

the measured scales. Similarly, new observational 

technologies are needed that can provide much 

more complete spatial sampling of the system 

under study. Models actually can guide observational strategies by revealing where measurements 

will be most useful. For example, measurements 

can be concentrated where models indicate sensitivity to poorly understood processes. Moreover, 

observing systems must be based on both remotely 

sensed and in situ technologies to benefit from the 

strengths of both and the synergy that occurs when 

they are used together.

Important Research Payoffs

Detailed knowledge of how the hydrological 

cycle, terrestrial and oceanic carbon cycles, 

land-use changes, and fossil fuel emissions are 

evolving in response to shifts in forcing will allow 

real hypothesis testing with ESMs. Monthly and 

yearly monitoring of operational Earth system 

forecasts will enable us to identify and attribute 

errors—an approach very similar to the one used 

today by operational weather forecasting centers. 

Over decades, this will lead to vastly improved 

climate projections.

An important application of these emerging models will be decadal projections. High-resolution 

ESMs will use observations of the initial climate 

state and estimates of evolving forcing to generate predictions of the system’s state one to three 

decades in the future. These decadal forecasts 

will capture El Niños and decadal modes of climate variability and their influences on extreme 

weather. They also will quantify regional timeevolving climate change to inform adaptation 

policies. Both private and public enterprises can 

benefit from such predictions. Information on 

even longer time scales is needed for setting infrastructure goals and policy directions. Monitoring 

system response to policy actions will enable 

improved projection capabilities over time.

Twenty years from now, scientifically based observation and modeling systems will produce much 

more detailed analyses and predictions of both 

weather and climate, including additional regionalscale diagnostics such as rainfall and extreme event 

statistics. These analyses and projections will continue to increase in value, driven largely by better 

ways to translate model output into information 

that society needs. Equally important as improvements in the quality and quantity of this information is better access to it.

Models will be used for predicting not only climate 

change, but also the consequences of particular 

climate change policies on ecosystems. Needed 

developments include tools that predict energy 

consumption and the consequences of energy use 

and support energy infrastructure planning.

Climate change attribution (i.e., connecting 

observed changes to particular causes) is just as 

important as prediction. Attribution is particularly 

challenging on regional scales. Regional climate 

changes—already seen in the Arctic—will become 

more visible over the next few decades.

Ensuring a Competent Workforce

A skilled workforce will be needed to provide 

climate services, including projections of energy 

requirements and freshwater needs. For example, 

a new discipline could evolve whose practitioners 

have a background in climate modeling and statistical analysis, as well as training in applications and 

stakeholder interactions. This new element of the 
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 35

Chapter 3

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

labor force would work at the interface of science 

and society, translating results from models into 

usable information for stakeholders. The demands 

on stakeholders also will change because some 

understanding of the relevant science will be essential. For example, a water manager who is told that 

there is a 40% chance of drought in the Southwest 

must be able to optimally use that information.

For decades, finding scientifically knowledgeable people in the general population has become 

increasingly difficult. The population at large has a 

deplorably low level of scientific literacy, and there 

are reasons to think this situation will worsen. 

Schools at all levels, from primary to postgraduate, 

are under tremendous financial stress, with little 

prospect for relief. Our society simply must use all 

possible means to improve this situation. Useful 

steps would include incentives for students at all 

levels to consider and ultimately pursue careers 

in science and engineering. Students who pursue 

nontechnical careers should acquire at least a reasonable understanding of what science actually is: 

namely, a way to learn about the world.

A further issue associated with meeting the 

above challenges is increasing the doctoratelevel workforce in the area of climate change and 

ESMs. Huge increases in the complexity of these 

models are projected, but only minor improvements are possible if just a small number of 

people are actively working in this field. Today, 

international assessments—such as those by the 

Intergovernmental Panel on Climate Change 

(IPCC)—aid progress through the efforts of 

many scientists. But progress also is hampered 

because, for example, as the IPCC focuses attention on the biggest current challenges, it also 

draws time away from efforts needed to overcome them. An increased doctorate-level workforce will improve this situation significantly.

Creating “DOE Cooperative Institutes” at universities would be one approach for achieving a more 

highly educated workforce. Providing a mechanism 

for continuing investment in graduate education, 

these institutes would focus on issues relating to climate, energy, and the environment. For example, an 

institute devoted to permafrost and climate change 

could be created at a northern university with natural interests in this area and ready access to permafrost experimental sites. (For more on workforce 

development, see Chapter 6: Grand Challenges in 

Education and Workforce Training, p. 51.)
36 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Three: Grand Challenges in Climate Research
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 37

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

Chapter 4

The present organization of human society 

and its well-being rest on sustaining the 

conditions and processes related to economic, sociopolitical, and environmental systems 

(Holdren 2008). These processes and conditions 

are created, maintained, and evolved by the systems 

associated with them. The systems, however, do not 

exist in isolation; they are interlinked, and we and 

our technologies have major effects on them. Society would not be worried about changes in climate 

and, more generally, the Earth system, if human 

activities and the externalities of the technological choices being employed were not altering the 

conditions under which we live.

We choose how to structure and live our lives, 

adopting and incorporating responses and solutions according to our preferences and access to 

resources. Witness the differences between the 

United States and Europe regarding acceptance 

of nuclear energy, biotechnology, and genetic 

engineering, as well as the degree to which 

behaviors are embedded and resistant to change. 

An essential component of energy sustainability 

is fundamental knowledge of relevant natural 

and physical processes—from the foundational 

constituents of microbes to the combinations that 

make up ecosystems and produce the functions 

and services they provide to the characteristics 

of global atmospheric circulation. A major thrust 

of this understanding is preserving these processes and the conditions under which they exist 

for the benefit of humanity (see box, Summary of 

Research Recommendations, p. 38, and sidebar, 

What Is Sustainability?, p. 39). An equally essential 

component of energy sustainability is an integrated 

understanding of how humans perceive and influence these processes.

Research challenges discussed in other chapters—

biological systems, climate, and computing—form 

the foundation of energy sustainability research for 

the Department of Energy’s Office of Biological 

and Environmental Research (BER).

Grand Challenge Research 

Recommendations

Outlining the Challenges

Endeavors to achieve energy sustainability suggest 

the need to understand the consequences and 

processes necessary for mitigating the problems 

and externalities resulting from how the energy 

and industrial sectors are organized. Research 

must address problems caused by present activities (e.g., CO2 emissions), develop future alternatives, and anticipate potential problems and 

consequences of these alternatives so that they 

can be minimized. Such understanding is partly 

technological, but mostly scientific.

Additionally, our primary energy sources will 

have to evolve from those relying on sunlight 

stored in the chemical bonds of fossil fuels to 

those that rely more on present-day sunlight and 

other renewable resources. Such energy sources 

4 Grand Challenges in Energy Sustainability

“... . Reliable and affordable energy is essential for meeting basic human 

needs and fueling economic growth... [yet] the harvesting, transport, 

processing, and conversion of energy using the resources and technologies 

relied upon today cause a large share of the most difficult and damaging 

environmental problems society faces.”

— J. P. Holdren. 2008. “Science and Technology for Sustainable Well-Being,” 

Science 319(5862), 424–34.
38 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Four: Grand Challenges in Energy Sustainability

degree to which solutions are implemented and the 

subsequent consequences. Technological solutions 

undoubtedly will face social, physical, and economic 

constraints, such as the present difficulties in siting 

wind farms (e.g., off the coast of Cape Cod) or the 

environmental concerns limiting the deployment 

of solar thermal technologies in California. Varied 

ethical, legal, economic, and other societal implications are inherent in the science and technology of 

energy systems, and the degree to which solutions 

are adopted affects both social and natural systems.

could include (1) direct conversion of sunlight by 

engineered technology; (2) wind; (3) extraction 

of energy stored in biota via photosynthetic and 

other processes; and (4) various other ocean and 

tidal, hydroelectric, geothermal, solar, or biological 

means. These energy pathways are mostly related 

to technology deployment, but the development of 

technologies and their associated processes must be 

based on a strong scientific foundation.

Research also needs to address not only technical 

and scientific issues and constraints, but also the 

4.1 Analyze and compare potential approaches to 

organize land use, water use, and energy systems in ways that achieve sustainable energy, 

food, biodiversity, and ecosystem functioning.

•	 Develop the understanding needed to 

produce enough energy to support more 

people at a higher standard of living in 

ways that sustain growth and minimize 

negative environmental impacts. 

•	 Determine the means to double, over 

20 years, the share of energy needs met 

by bioenergy in environmentally and economically sustainable ways.

•	 Develop affordable and competitive 

options for energy supply and conversion 

that minimize negative direct, indirect, 

and life-cycle impacts on climatic, environmental, and ecological systems.

4.2 Identify and characterize potential Earth system drivers, feedbacks, and vulnerabilities to 

state changes so that their consequences and 

triggers might be avoided.

•	 Characterize the spatial and temporal variabilities of biological systems throughout 

Earth and be able to understand, predict, 

and manipulate their rates of change. 

•	 Develop technologies to remotely monitor ecosystem services in real time and at 

landscape scales.

4.3 Develop unifying models and frameworks 

capable of testing and evaluating the significance of potential global change issues, 

including energy, land use, and water. The 

impacts these issues have on both society 

and the environment also must be tested 

and assessed.

•	 Develop the scientific basis for life-cycle 

analysis and full-cost accounting of 

ecosystem services for extant and nextgeneration energy technologies.

•	 Advance the science to detect, understand, and mitigate the environmental and 

security issues associated with the potential resurgence of nuclear power.

•	 Advance the discipline of ecological informatics and incorporate social and policy 

elements into science-based sustainability 

research. 

•	 Develop the science perspective necessary 

to set target resolutions for the spatial 

and temporal dimensions underlying 

systems of interest and be able to convert 

global change estimations into model 

visualizations.

•	 Determine the degree to which future 

sustainable energy facilities should be 

centralized. 

Summary of Research Recommendations
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 39

Chapter 4

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

Also important is quantifying the extent to which 

different entities link systems, perhaps through 

the resources that these entities provide or the 

degree to which the resources are impacted. Land 

use and water resources and quality are prime 

examples of such linkages. Understanding not 

only the role of these linkages, but also how science can relax the constraints imposed by them 

is necessary.

Sustainability, by its very nature, calls for multidisciplinary research that spans spatial and temporal 

scales. Compartmentalized research, development, 

and associated agendas can inform understanding 

of system components, but sustainability is an area 

of study that must address the overlap and interrelationships of systems. In particular, biologists 

and ecologists must engage with social scientists to 

understand the forces and implications of changes 

in energy sustainability. Although we cannot predict 

with certainty the makeup of tomorrow’s society, 

the future Earth and climate systems will be different from today’s, and there is great concern that our 

desired future may elude us if we continue on our 

current trajectory. The choices we make are determined partly by values, and different kinds of futures 

call for different kinds of information, tools, and 

responses. Many issues and challenges fall under the 

realm of sustainability, as do inextricable linkages 

with economic and social processes. Although this 

chapter primarily focuses on the pervasive challenges and opportunities facing the biophysical 

sciences, there are equally pervasive challenges and 

opportunities facing the social sciences. Moreover, 

many of the most interesting and important energy 

sustainability questions are at the interface of natural 

and human systems, requiring integrated research 

across these two domains. Previously, BER has not 

included human roles in much of its work, but these 

are important determinants of and vital contributors to energy sustainability. Future cross-agency 

partnerships may be a valuable consideration for 

addressing important social questions in coupled 

natural human energy systems.

Energy Sustainability Research 

Recommendations

Humanity clearly faces enormous challenges 

with respect to energy production, transmission, 

conversion, use, impacts, and sustainability. Many 

of these challenges and their associated research 

agendas can be categorized into the following three 

overarching recommendations.

4.1 Analyze and compare potential approaches to 

organize land use, water use, and energy systems in 

ways that achieve sustainable energy, food, biodiversity, and ecosystem functioning.

Sustainability is defined differently depending on 

various individuals’ perspectives, but a broad definition is the capacity and ability to maintain and endure. 

Given the interactions within and between relevant 

systems, sustainability is associated with the notion that 

providing resources and satisfying current needs in one 

arena should not overly inhibit the current or future 

functioning of another. Energy sustainability involves 

supplying and maintaining adequate, reliable, and 

affordable energy sources in ways that do not compromise the future ability to provide useful energy and 

energy services or the vitality of the associated systems. Image credit: U.S. Department of Agriculture 

Natural Resources Conservation Service.

What Is Sustainability?
40 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Four: Grand Challenges in Energy Sustainability

Which milestones have to be reached, and what 

must be accomplished to meet them in a reasonable 

time frame? Several related grand challenges are to:

• Develop the understanding needed to produce 

enough energy to support more people at a 

higher standard of living in ways that sustain 

growth and minimize negative environmental 

impacts. By 2100, the world’s population—

which will seek a higher standard of living—is 

projected to increase by 40% over today’s level. 

If society is bordering on or already has crossed 

into an unsustainable regime, how do we provide 

a standard of living that sustainably supports 

a much larger population? One path forward 

would be to triple the amount of degraded land 

on which perennial energy crops are planted in 

a manner that increases soil carbon storage and 

water quality (Tilman et al. 2009).

• Determine the means to double, over 20 years, 

the share of energy needs met by bioenergy in 

environmentally and economically sustainable 

ways. Such a pathway requires developing transformational bioenergy alternatives that deliver 

more energy, goods, and services than are available currently. Today, C4 plants most efficiently 

fix CO2, but can we develop sustainable and productive plants that more efficiently use sunlight, 

water, and other nutrients; remediate environmental toxins; provide other ecosystem services; 

and are ideally suited for bioenergy applications?

• Develop affordable and competitive options for 

energy supply and conversion that minimize 

negative direct, indirect, and life-cycle impacts 

on climatic, environmental, and ecological 

systems. Developing means, methods, and 

technologies that provide useful energy with 

minimal greenhouse gas (GHG) emissions and 

other impacts requires a substantial shift away 

from systems that emit CO2. It also involves 

understanding how new solutions can impact 

and alter different Earth system components.

Nascent and probably future technologies to 

extract useful hydrocarbons from resources that are 

increasingly lower quality (e.g., oil from tar sands) 

or still virtually unexploited (e.g., methane from 

hydrates) require methods that use fewer resources 

for harnessing present and future stocks. Future 

energy and resource (e.g., water) intensities of 

harvesting primary energy sources should be equal 

to or less than the present intensity of conventional activities producing these resources (e.g., 

exploration and production of oil and natural gas 

by drilling into conventionally located reservoirs). 

Science should strive to understand the consequences of exploring and exploiting these resources 

(e.g., mineral leaching, nutrient flow, and seafloor 

destabilization).

Current approaches to mitigate CO2 and other 

GHG emissions have non–emission related processes and impacts that also must be understood and 

either enhanced or mitigated. For example, understanding and enhancing the chemical, geochemical, 

and biogeochemical methods and processes for 

CO2 separation and geologic storage can provide 

cheap and secure means to mitigate CO2 emissions 

from existing and future facilities. Siting renewable 

energy facilities such as solar- and wind-generated 

electricity farms involves building new roads and 

establishing new facilities, the footprints of which 

impact ecosystems. At larger scales, concentrated 

solar power and biomass used for bioenergy can 

require significant water withdrawals, thereby 

affecting water quality. At even larger scales, wind 

turbines can impact climate and atmospheric patterns by increasing the roughness of Earth’s surface 

and extracting energy from blowing wind (Keith 

et al. 2004).

4.2 Identify and characterize potential Earth system 

drivers, feedbacks, and vulnerabilities to state 

changes so that their consequences and triggers 

might be avoided.

We lack sufficient understanding of Earth, environmental, and ecological systems and their vulnerability and resilience in a changing climate. Some 

associated grand challenges are to:

• Characterize the spatial and temporal variabilities of biological systems throughout Earth and 

be able to understand, predict, and manipulate 
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 41

Chapter 4

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

their rates of change. For example, what are the 

roles of subsurface hydrological and biogeochemical processes in climate change and GHG 

exchange? Similarly, we also must understand 

current microbial activities in permafrost 

(see sidebar, Potential Permafrost Loss Poses 

Growing Concern, p. 31). How and under what 

conditions might these permafrost activities 

change? How can these activities be mitigated? 

What is the potential for capturing and using the 

products of this activation (e.g., CH4)? What are 

the consequences for the climate, Earth system, 

and physical infrastructure of permafrost-loss 

areas? More broadly, we must understand how 

to identify, quantify, and model the mechanisms 

of present carbon sinks in plants and soils to 

increase their effectiveness at removing CO2 

from the atmosphere.

• Develop technologies to remotely monitor 

ecosystem services in real time and at landscape 

scales. We need to understand how rapidly 

species can migrate (e.g., by using dynamic 

vegetation models) and how individual and 

species-level responses translate into ecosystem-level change. Also needed is the ability to 

characterize and assess differential ecosystem 

sensitivity and the extent to which ecosystem 

vulnerability is determined by in situ responses 

or by potential species migration.

4.3 Develop unifying models and frameworks capable 

of testing and evaluating the significance of potential 

global change issues, including energy, land use, and 

water. The impacts these issues have on both society 

and the environment also must be tested and assessed.

Scientific developments related to energy have a 

long history of being hailed as the next solution to 

societal needs and problems. In the 1960s, nuclear 

energy from fission was touted as the next great 

technology to provide cheap and accessible electricity, and nuclear fusion always has been touted 

as just a decade or two away. More recently, the 

hydrogen economy was popular, and now biofuels, 

plug-in electric vehicles, and geoengineering are 

solutions being emphasized. We need to develop 

the means to put such potential solutions into 

context, where uncertainty is naturally incorporated to ultimately achieve a fully integrated and 

overarching assessment model. Some related grand 

challenges are to:

• Develop the scientific basis for life-cycle analysis 

and full-cost accounting of ecosystem services for 

extant and next-generation energy technologies.

• Advance the science to detect, understand, and 

mitigate the environmental and security issues 

associated with the potential resurgence of 

nuclear power.

• Advance the discipline of ecological informatics 

and incorporate social and policy elements into 

science-based sustainability research. Determining the role of biodiversity and human activity 

for ecosystem functioning is one direction this 

research could pursue.

• Develop the science perspective necessary to 

set target resolutions for the spatial and temporal dimensions underlying systems of interest 

(e.g., 1 km and 1 year, respectively, for climate 

models) and be able to convert global change 

estimations into model visualizations. At a 

smaller scale of interest, a significant challenge 

will be to develop (1) integrated approaches to 

model and understand biogeochemical systems 

in situ; (2) unified and transferable models of 

biogeochemical processes; and (3) the means 

and methods for multimodal microscopy, 

spectroscopy, and visualization and imaging of 

microbiological and environmental chemicals. 

Accurately modeling and analyzing global climate change will require developing and building more and better computing facilities. Such 

facilities are essential, given the computational 

complexity inherent in all systems of interest, 

their different spatial and temporal characteristics, the necessity of modeling a system at a 

level larger than the unit of interest, and the 

interactions of systems within and across their 

characteristic spatial and temporal scales.
42 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Four: Grand Challenges in Energy Sustainability

• Determine the degree to which future sustainable energy facilities should be centralized. Such 

an understanding integrates the characteristics 

of a technology and its inherent returns to a scale 

engendered by the cost functions of its installation, operation, and disposal; by environmental 

impacts throughout its life cycle; and by the 

land, water, and ecosystem resources necessary for and impacted by it. On one end of the 

spectrum, distributed electricity generation from 

renewable sources can bring those sources closer 

to the point of use and minimize their impacts 

on local ecosystems. However, such decentralization can cause coordination issues and impede 

certain activities. CO2 capture and storage, for 

example, cannot be applied cost effectively to 

diffuse and distributed CO2 sources.
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 43

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

Chapter 5

significantly greater, given the goals of improved 

models and anticipated further advancements in 

experimental techniques.

In climate science, the largest volume of data has 

come from the many environmental satellites in 

operation since 1979. Perhaps the more important 

climate science data relevant to DOE’s mission, 

however, are the data available from BER’s Atmospheric Radiation Measurement (ARM) Climate 

Research Facility archive. This program started collecting data more than 20 years ago (see sidebar, 

BER Contributions to Climate Research, p. 27), 

and the volume has now grown to 200 terabytes, 

with approximately 2 to 3 more terabytes added 

monthly. Data volume will increase by 4 to 7 times 

this amount in the near future. The key to using 

these data in climate model verification is an 

understanding of the location and time resolution 

of the data sample collection and how and whether 

these data can be adapted for comparison with 

climate model data.

A theme shared across all BER science is that as 

the research community develops common “models” of scientific understanding, the approaches 

and new knowledge then are captured in computational systems. These systems reflect with 

increasing accuracy detailed scientific understanding and, ultimately, the reality of natural systems 

As experimental systems become increasingly digital, more accurate, and efficient, the resultant datasets continue to grow 

exponentially and encompass an expanding 

diversity of data types. Examples relevant to the 

Department of Energy’s (DOE) Office of Biological and Environmental Research (BER) include 

measurements of greenhouse gas fluxes and 

subsurface contaminant concentrations, as well as 

climate simulation output. In biology, this enormous growth in information is especially acute 

as DNA sequencers transition to next-generation 

technologies. These new sequencers are expected 

to produce data volumes equivalent to a human 

genome (3 gigabases) for $15 by 2015. By comparison, the first human genome was completed 

in 2003 in a 13-year, $3 billion effort by the 

Human Genome Project. The exponential growth 

in genomic sequences over the last two decades 

will become super-exponential growth over the 

next 20 years. Furthermore, as the cost of obtaining these data dramatically declines, sequencing 

technologies will become more accurate and 

cost-effective replacements for microarrays in 

transcriptomic analyses. The increase in scientific 

data has been faster than anticipated over recent 

decades because of improvements in electronics, computing, and experimental systems. 

Growth over the next 20 years is expected to be 

 “Since at least Newton’s laws of motion in the 17th century, scientists have 

recognized experimental and theoretical science as the basic research paradigms for understanding nature. In recent decades, computer simulations have 

become an essential third paradigm... . As simulations and experiments yield 

ever more data, a fourth paradigm is emerging, consisting of the techniques 

and technologies needed to perform data-intensive science.”

— G. Bell, T. Hey, and A. Szalay. 2009. “Beyond the Data Deluge,” Science 323 (5919), 1297–98.

5 Grand Challenges in Computing for Biological and Environmental Research

Data Representation, Integration, and Knowledgebase Development 

to Support BER’s Long-Term Vision
44 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Five: Grand Challenges in Computing for Biological and Environmental Research

This approach is becoming critical because the 

increasing scale and complexity of data require computing for consistently and coherently managing 

and integrating large sets of data and information. 

Such a perspective drives our view of what computational or informational science is and the objectives that must be achieved over the next 20 years.

Grand Challenge Research 

Recommendations

5.1 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.

As data generation becomes exponentially more 

cost effective, the number of scientists will not 

increase proportionately. Current methods 

researchers use to analyze entire datasets (such 

as with Excel) will break down when applied to 

very large and complex datasets, and uniform 

manual curation of these data will be impractical. 

New paradigms thus are required to address these 

challenges. Improved computational methods and 

resources are becoming increasingly important to 

continued scientific progress. Since the volume and 

being studied and the experimental systems 

probing them. Such computational models also 

would be designed to simulate a natural system or 

its components and the associated experimental 

systems to provide predictions that could then 

be further validated. Currently, these models are 

elements such as analysis programs and climate 

models, database schema, and annotations and/

or metadata. Substantial increases in data—along 

with possible limits in the growth of computer 

processing speed—will demand a more focused 

approach to direct modeling of scientific concepts 

into computational systems (see box, Summary of 

Research Recommendations, this page). Similarly, 

experimental protocols and the reported data 

should accurately describe current scientific understanding and experimental results and provide the 

supporting ontological and semantic information 

to enable machine-readable support for subsequent searching, analysis, and reuse. Ultimately, 

this general concept of data modeling will be captured entirely in computational systems that then 

will provide an automated basis for data storage, 

integration, query, and retrieval. These systems will 

enable computational simulations and projections 

that allow further assessment and understanding, 

resulting in new directions for future experiments 

and model improvements.

5.1 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, opendevelopment data management infrastructure.

5.2 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.

5.3 Develop new computing paradigms capable of 

meeting the enormous parallel processing and 

data-intensive analysis needs now emerging 

for biological, climate, and environmental data.

5.4 Standardize experimental and computational 

protocols and methods to increase data integration, data usability, and system interoperability to improve research productivity.

5.5 Improve data usability and model accuracy by 

ensuring that appropriate data quality standards are created and stored with the accompanying data.

5.6 Design and build software solutions that provide researchers with better access to increasingly large, complex, and interrelated datasets.

5.7 Develop virtual laboratories and tools to more 

fully engage human cognitive faculties and 

provide richer opportunities for scientific 

collaborations.

Summary of Research Recommendations
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 45

Chapter 5

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

diversity of data will grow substantially beyond that 

which was typical in the past, new computational 

requirements will include capabilities for managing 

and processing very large datasets.

Although growth in computer disk storage space 

will be cost effective for the foreseeable future, 

computer processor speed and network bandwidth 

may not continue to grow at past rates (see recommendation 5.3, p. 47). Therefore, a likely computing paradigm in the future will be data-intensive 

science (Bell et al. 2009) that entails (1) developing 

ontologies as a basis for semantic representation 

of biological, climatological, and environmental 

systems science concepts; (2) establishing standards 

for experimental protocols and data exchange; and 

(3) building a data management infrastructure that 

is open access and open development in all aspects.

The ARM Climate Research Facility represents 

one such “open-access” model for data. Indeed, the 

development of this program’s “value-added products,” which combine the best observational data 

to yield new structures or products with high value 

for atmospheric science, is a data-provision model 

that also yields high value to scientists comparing 

the data with computer model output.

Increasingly, data will be preprocessed and compressed to make them more manageable. For example, an experimental system that produces image 

files, which typically are large, may preprocess them 

to extract important data and then discard the 

original image files. This effort saves disk space, but 

an improved image-analysis algorithm could render 

old data obsolete if the images are not still available 

to rerun. Tradeoffs depend on the costs of storage 

and re-running the experiment and on the probability that a new algorithm will be found.

Beyond basic file storage, data also can be organized in a relational database system, which allows 

structured queries to retrieve information quickly. 

New “query” paradigms are based on novel ways 

to organize data from XML and its derivatives 

into a software framework (e.g., MapReduce/

Hadoop) that supports applications for processing 

large amounts of data in parallel. Potentially, such 

a system could enable the testing of new, valueadded products that might become part of a data 

archive in the future. Although not suitable for all 

problems, these approaches are beginning to be 

developed and seem to hold promise for dealing 

with large-scale data processing in parallel implementations such as “cloud” computing.

“Google Maps” is a familiar example of an application that integrates data from different sources and 

scales and also processes queries quickly. We envision maps of cellular and subsurface systems or 

atmospheric data displayed similarly (see sidebar, 

“Google” Life, p. 46). These maps also could have 

browsable annotation. Because manual annotation is becoming increasingly prohibitive due to 

data growth, one idea is that future annotations 

would be generated automatically. However, this 

approach is recognized as not ideal. Informed users 

are encouraged to contribute manual annotation 

as they encounter opportunities to make improvements (such as browsing a Google Map of pathways). Such contributions would be vetted by a 

peer-review system and could be considered a type 

of mini-publication.

In 20 years, most researchers analyzing data often 

may not know how the data were generated or 

results derived. This problem is analogous today to 

commonly used systems like BLAST or the use of 

complex but accepted laboratory techniques. To 

conduct science, such analysis systems must be well 

documented in a generally accessible way so that 

interested researchers can learn how the system 

works and verify its integrity to their satisfaction. 

The computational community has the responsibility of making tools accessible, both in terms of 

providing detailed documentation of the system 

use, methods, uncertainty, and code, as well as guiding correct usage. Also critical is that such systems 

be assessed and accredited by authoritative external 

reviewers conducting an unbiased peer review. 

Without such characteristics, these systems do not 

meet the rigors of the scientific method.
46 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Five: Grand Challenges in Computing for Biological and Environmental Research

I

magine a computational resource allowing us to 

move seamlessly across biological or atmospheric 

scales. From an initial view of molecules inside a 

cell nucleus, we would continue outward to see the 

cellular components, then the whole cell, the cell 

in the context of a tissue interacting with adjacent 

cells, the tissue as part of an organism (e.g., a tree 

in a forest, the forest as part of a geographical 

region, and then as part of a continent), and finally 

to a view of the whole Earth. (A related example 

might be the rhizosphere associated with a tundra 

biome that has carbon and climate impacts.) For 

atmospheric science, we might envision seeing the 

data taken within a column above an Atmospheric 

Radiation Measurement Climate Research Facility 

site placed in context within the broader image of 

the region surrounding the site and its evolution 

over time.

Computational modeling at multiple scales is 

required for atmospheric systems and understanding biological systems within a bacterium or in the 

context of environmental and ecological interactions 

that ultimately impact the carbon cycle and climate. 

Whatever the scale of study, each discipline has existing models and will continue to refine and improve 

them as new experimental data become available 

and new analysis methods are developed.

Models could interact at each scale, but today, there 

is no continuity from models of microbial systems 

to those that predict global climate. Researchers in 

each discipline are focused on improving current scientific understanding in their domain and generally 

view today’s models as crude. As models become 

refined and increasingly precise at each scale, the 

question of how a model can interact meaningfully 

with those at adjacent scales will present opportunities to gain further insight.

“Google” Life: Computing Biological and Climatological Interactions Across Scales

DNA Protein
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 47

Chapter 5

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

An important scientific tenet is that publication of 

research implies that enough information is provided to allow the work to be replicated. As science 

has moved forward into a more data intensive, 

analytical world, we have lost sight of this tenet. 

Data provenance no longer is being captured sufficiently for results to be validated. This issue must 

be addressed and become a standard operating 

principle (SOP). Provenance includes, for example, information about who conducted the experiment and when, how it was done, and the software 

version used. In many environmental datasets, a 

“quality flag” is often added to the data to indicate 

that it has been quality-controlled in a way that 

meets the standard for that dataset.

In 20 years, all researchers will have easy access 

to a community knowledgebase that will include 

a repository of standard experimental data, an 

archive of analysis tools, and an ontology library. 

Each researcher will have a login that provides 

a private computational workspace that can be 

shared with collaborators. They also will be able to 

contribute to any of the knowledgebase elements 

in a manner similar to that of a peer-reviewed 

journal and ultimately publish components of their 

private work in this public space.

5.2 Create a new publishing paradigm that credits 

and rewards researchers for publishing peer-reviewed 

datasets or analytical methods in addition to conventional peer-reviewed journal articles.

Today, most experimental results are published in 

peer-reviewed journals. Looking forward, science 

could advance much more quickly if results were 

saved online in a machine-readable format connected with associated metadata, as well as ontologies and data values with their statistical properties. 

The experimental procedure also would be documented with an SOP. This approach would enable 

analyses that integrate and compare multiple independent experiments and that could be conducted 

in a community knowledgebase if the experimental 

data deposited therein were compatible with a 

particular standard.

Although “publishing” such data, analytical methods, or code in, for example, the DOE Systems 

Biology Knowledgebase (see genomicscience.

energy.gov/compbio/) or elsewhere beyond traditional journals would provide value to the research 

community, such contributions currently are 

neither commonly required by sponsors or publishers nor recognized as accomplishments for career 

advancement. Indeed, data within the ARM Climate 

Research Facility archive generally do not come 

with specific authorship after a specific experimental procedure has been accepted. Other examples 

are manual curation, or correction of automated 

annotation, and ontology development. Given the 

continuing need for developing electronic, machinereadable forms of experimental results and conclusions, new paradigms for peer-review accreditation 

must be an ongoing topic for consideration.

5.3 Develop new computing paradigms capable of 

meeting the enormous parallel processing and dataintensive analysis needs now emerging for biological, 

climate, and environmental data.

Moore’s Law generally has kept pace with the 

growth in both biological sequence data and environmental data. However, the increase in processor 

clock speed appears to be subsiding even as data 

volume is growing ever faster. Consequently, investigating alternative analysis methods is becoming 

critical. One new approach is parallelism based on 

cloud computing methods. Although traditional 

high-performance computing (HPC) works well 

on problems such as climate modeling and molecular dynamics, most data-intensive problems in 

biology are expected to parallelize most effectively 

in a cloud computing architecture. Such efforts are 

only just beginning and will require analysis and 

recoding methods.

Analysis systems necessarily become more complex as scientific models become more accurate 

or encompass more components. Additional 

layers of technical complexity usually come at 

the cost of adding more computing parameters. 

The inverse also is true. In general, a reduction in 
48 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Five: Grand Challenges in Computing for Biological and Environmental Research

data dimensionality will make computing more 

efficient and can reduce noise. However, along 

with this theoretical simplification, the model 

may become less accurate. As problems reach 

computability limits, this approach will allow us 

to use tools like mathematics to make simplifications that require fewer computational resources 

but usually result in less accurate models. An 

example would be using least squares to derive 

an equation that approximates measured data. 

The equation is a simplification that eliminates 

data noise, but the data might be represented 

more accurately by a more complex error model. 

The model, however, would require much greater 

computational resources.

“Google Translate” is an example of another kind 

of simplification. As an automatic translator, this 

program uses statistical learning techniques to 

build a translation model based on data analysis. 

Typical translation systems have been rule based 

and require predefined vocabularies and grammars. Google Translate instead has analyzed 

billions of words of text and aligned examples 

of human translations. When results of Google 

Translate are incorrect, a knowledgeable user can 

submit a better translation that then is added to the 

database. This is a type of manual curation.

Methods analyzing statistical co-occurrences of 

words are readily applicable to climate, environmental systems science, and biological and physical 

systems. Although such “machine learning” methods 

are not 100% accurate, they can be remarkably sufficient, if properly understood, and provide automated 

methods that may be necessary in the near future.

5.4 Standardize experimental and computational 

protocols and methods to increase data integration, 

data usability, and system interoperability to 

improve research productivity.

As the research community gains increasing access 

to new experimental technologies and large datasets, standardized experimental and computational 

protocols and methods will be required to enable 

comparative analyses that provide a true basis for 

scientific assessment and further investigation. 

Standards development can be a delicate issue. 

Although standards can be onerous and require 

substantial additional work to support a data submission, integrating and comparing experimental 

results become almost impossible without them. 

Initial focus for standards development should be 

on the largest and most significant experimental 

results having the greatest impact on the research 

community. This triage will continue for the 

foreseeable future, but standardization needs to 

become as serious a professional activity in biology, climate, and environmental systems science as 

it is in the telecommunications industry because 

research productivity will suffer without it.

5.5 Improve data usability and model accuracy by 

ensuring that appropriate data quality standards are 

created and stored with the accompanying data.

Data usability and model accuracy depend on data 

quality. The quality of data can be assessed by analyzing their statistical properties and comparing 

the data with independent validation. In general, 

most data are collected without careful consideration of these properties. The research community needs to establish and maintain a culture 

that recognizes the need for and value of quality 

assessments to properly conduct experiments and 

analyses. Without such an approach, results are of 

questionable value and undermine scientific credibility. Much of the data in databases today are of 

varying quality, yet user displays present the data 

as if their quality is uniform and presumably good. 

Because all downstream analyses depend on the 

quality of input data, including into the analyses 

quality measures that are appropriately propagated 

is important. Quality is related to the reproducibility of results, an important science objective. A 

related factor is provenance, the historical record 

of the sources of data and analytical methods. As 

computational models are built, their accuracy also 

depends on input quality. Differences between 

experimental measures and model predictions 

determine the next experiment. Poor input quality leads to erroneous conclusions and potential 
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 49

Chapter 5

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

experimental waste. Additionally, experimental 

consistency among laboratories must be assessed. 

Without progress on these issues, our ability to 

integrate multiscalar data of potentially varying 

quality will become increasingly compromised.

5.6 Design and build software solutions that provide 

researchers with better access to increasingly large, 

complex, and interrelated datasets.

The ultimate limit on scientific advances is not 

computers, but scientists. Software needs to be 

designed so that it does not interfere with what 

researchers are trying to do, but rather enhances 

their research experience and productivity. Consequently, the research community needs to be 

involved in designing and developing computational systems such as the DOE Systems Biology 

Knowledgebase. With the huge scaling up of 

experimental data, the future bottleneck will be 

computational data management and analysis. No 

researcher should be limited by lack of access to 

computational and analytical capabilities.

Many software systems used for analysis today 

were developed by individual scientists who 

created these tools to advance their own research 

but are not rewarded for making them easier to 

use. A shift therefore is needed toward an opencommunity environment that encourages code 

to be easier to use and provides further incentives based on usage and value to the research 

community. This development environment 

would provide rapid feedback and support for 

high-quality user interfaces for a range of tools—

from free-form data analytics to researcherdriven, semantic-based discovery. In this context, 

ease of use includes enabling researchers to 

discover and learn more about how a software 

system or tool works.

Future research activities will be conducted in an 

environment in which researchers who analyze 

the data possibly will not understand how the data 

were generated or what assumptions were made, 

all of which could lead to incorrect conclusions. 

Computational tools will need to be developed and 

hardened to allow noncomputational scientists to 

gain insights from large datasets. Ultimately, we 

might have to expand computational infrastructure—

computers, networks, and methods—to gain more 

scientific productivity.

5.7 Develop virtual laboratories and tools to more 

fully engage human cognitive faculties and provide 

richer opportunities for scientific collaborations.

What do scientists do when they are not physically conducting either a computational or 

observational experiment? Mostly, they click, 

type, read, talk, and think—all acts of nearly pure 

symbol manipulation. If mapped into virtual 

environments, human symbol manipulation 

can be given a much wider diversity of physical 

expression and more fully engage human cognitive faculties. For example, scientific objects and 

processes could be represented by virtual macroscopic objects and visible computational processes. Such virtual environments will be essential 

for future scientists, both to retain their interest 

and to greatly amplify cognition with computation and collaboration.

What could be represented in a virtual scientific 

environment? Almost anything if it is important 

enough to justify the initial software development 

effort. Genes, proteins, reactions, biomolecular 

networks, kinetic models, and diverse datasets 

from both atmospheric and biological systems 

are well on their way already. Observations, 

hypotheses, informal models, scientific notebooks, instruments, and entire replicable experiments represented as workflows could follow. 

For example, particularly for biological systems, 

many hypotheses could be represented using a 

diagrammatic, process-modeling language that 

can be manipulated like a circuit diagram drawn 

in a three-dimensional or hyperbolic space, 

permitting connections to other such process 

diagrams. Large-scale laboratory and computer 

experiments in progress could be represented 

using a virtual factory floor. Collaborative environments supporting remote interaction also may 

be among the drivers for virtualization. These 

developments may require applying current and 
50 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Five: Grand Challenges in Computing for Biological and Environmental Research

near-future artificial intelligence technologies, 

including machine learning and limited natural language understanding. Such technologies 

increasingly are applied in the nonscientific video 

games and virtual environments now being used 

by our future scientists. Labor cost savings from 

the successful transplantation and replication of 

experimental protocols alone could pay for the 

required software development for virtual scientific environments. 
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 51

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

Chapter 6

The grand challenges articulated in this 

document reveal sweeping changes in 

the language, methods, and conceptual 

frameworks that dominate research supported 

by the Department of Energy’s (DOE) Office of 

Biological and Environmental Research (BER), 

shifting from a focus on the parts to a quest 

for the whole. As noted in Chapter 2, tackling 

complexity really is THE grand challenge in 

biology. However, biological education has not 

switched to this new track. Biologists continue 

to teach discrete components—parts of the cell, 

individual molecules, separate organ systems, and 

striated taxa—rather than presenting a unified 

image of systems with emergent properties. For 

example, only recently have BER and the subsurface research community come to a consensus 

regarding the urgent need for new approaches 

to adequately detect, quantify, and interpret 

complex behavior in the subsurface. In this area, 

hydrological and biogeochemical processes 

interact within a heterogeneous framework over 

a hierarchy of scales ranging from subnanometer 

to kilometer, and one component process alone 

can exhibit complex behavior (U.S. DOE 2010). 

The imperative need is to educate our future 

workforce to think about properties of whole 

systems, which rarely can be explained simply as 

an accumulation of parts.

This new research framework necessitates reorienting education from teaching the known to 

exploring the unknown, an approach advocated 

vehemently by scholars and science educators 

to make systems-based biology an agent for the 

long-hailed overhaul of science education. Education research dictates that the teaching of biology 

should capture the nature of inquiry, presenting 

the science as a method of investigation rather 

than an extant body of knowledge. Inquiry-driven 

teaching impels instructors to use problem-based 

learning, open-ended laboratory exercises, and 

collaborative discussion—all techniques shown 

to foster higher-order thinking skills as well as 

retention of information (see box, Summary of 

Recommendations, p. 52).

BER has a long-standing commitment to science 

education and workforce development. This is 

demonstrated principally through the support 

of graduate students pursuing advanced degrees; 

postdoctoral students who work on research 

projects; and, to a lesser extent, hands-on research 

opportunities for undergraduate students and 

K–12 educators, as well as informal experiential 

learning opportunities for K–12 students. Many of 

these individuals use DOE research facilities and 

work alongside the scientific and technical staff at 

the national laboratories.

6 Grand Challenges in Education 

 and Workforce Training

“The key . . . to improving our health and well-being, to harnessing clean 

energy, to protecting our security, and succeeding in the global economy 

will be reaffirming and strengthening America’s role as the world’s engine of 

scientific discovery and technological innovation. And that leadership tomorrow depends on how we educate our students today, especially in those fields 

that hold the promise of producing future innovations and innovators. And 

that’s why education in math and science is so important.”

— President Barack Obama, November 23, 2009, on the “Educate to Innovate” campaign.
52 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Six: Grand Challenges in Education and Workforce Training

One example of BER education and workforce 

development efforts is the Global Change Education Program (GCEP), which promotes undergraduate and graduate education and training 

supportive of BER’s global change research activities. GCEP has two components: the Summer 

Undergraduate Research Experience (SURE) and 

the Graduate Research Environmental Fellowships 

(GREF). SURE’s primary goal is to involve undergraduate students at the end of their sophomore 

or junior year in BER-supported global change 

research, continuing this undergraduate experience during subsequent summer studies. To further improve the quality of emerging scientists in 

disciplines related to global change research, SURE 

students are encouraged to apply for GREF and 

graduate education opportunities. GREF’s primary 

goal is to support research designed and conducted 

collaboratively among graduate students, their 

faculty advisors, and DOE researchers at national 

laboratories or universities. GREF graduates are 

encouraged to continue climate change careers in 

postdoctoral and permanent positions in academia, government laboratories, and industry.

In addition, several of the research programs 

within BER offer their own science education 

portfolio. Examples are provided below.

• The DOE Environmental Molecular Sciences 

Laboratory (EMSL) offers two fellowship 

programs. The Wiley Visiting Scientist program 

is designed to enable distinguished scientists 

to spend extended periods of time at EMSL 

focusing on their research and helping to plan or 

develop EMSL capabilities. The William Wiley 

Post-Doctoral Fellowship gives highly qualified 

Ph.D. scientists the opportunity to conduct creative original research using EMSL capabilities.

• BER’s Genomic Science program provides 

resources for educators to enhance existing 

curricula (genomicscience.energy.gov). These 

include primers on biofuels and microbial 

genomics, an image gallery, and Genomic Science program reports.

• Each of the three DOE Bioenergy Research 

Centers has active outreach and educational 

programs. For example, the DOE BioEnergy 

Science Center has created a website devoted 

to K–12 education (bioenergycenter.org/

students-and-kids/). It provides teachers and 

students with information, games, and links to 

other websites that teach them how to make 

a difference in their own lives with energy 

conservation. Likewise, the DOE Great Lakes 

Bioenergy Research Center (GLBRC) has a 

significant education effort (www.glbrc.org/

education/). The mission of GLBRC Education 

and Outreach is to inform a variety of audiences 

about bioenergy research, energy concerns, and 

sustainability issues affecting Earth. Its goal is to 

broaden the understanding of current issues in 

bioenergy for the general public and for students 

and educators at the K–12, undergraduate, and 

graduate levels. Finally, the DOE Joint BioEnergy Institute ( JBEI) supports the training of 

high school teachers and students who can then 

transfer this training and knowledge back to their 

own classrooms. 

The above examples are effective educational 

efforts that seek to engage and excite K–12 students 

and teachers and support and encourage graduate and postgraduate research scientists. Yet we 

6.1 Engage science educators. 

6.2 Develop a centralized education mission.

6.3 Initiate interdisciplinary fellowships.

6.4 Enhance career development programs.

6.5 Support engineering education to address 

crucial shortages in engineering disciplines 

relevant to BER’s grand research challenges.

6.6 Install education experts at the national 

laboratories.

6.7 Develop collaborative teaching programs.

Summary of Recommendations
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 53

Chapter 6

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

must consider whether these disparate programs 

reflect the very best education pedagogy for training a workforce in the complex systems biology 

approaches required to meet the grand challenges 

proposed in this report. Even cursory examination 

of these grand challenges reveals that mathematics 

is a central theme of the science and must be, as 

biology has become, an information science.

Recognizing the widespread concern about science, technology, engineering, and mathematics 

(STEM) workforce development, the Secretary 

of Energy in 2006 commissioned a review by the 

Secretary of Energy Advisory Board. That review 

demonstrated a clear role for DOE in STEM education and concluded that partnerships should be 

the primary vehicle for achieving its goals.

Grand Challenge Education and 

Training Recommendations

BER clearly is poised to play a crucial role in 

enhancing STEM education. To begin, BER must 

develop a clear, unifying vision for education and 

workforce development, emphasizing the integration of mathematics and biology and tackling 

complexity through the use of multidisciplinary 

teams. Workshop participants made recommendations on ways BER might achieve this goal, and 

several are summarized below.

6.1 Engage science educators.

BER must work with professional science educators 

to inform and direct the development of its educational vision and resulting programs. Embracing the 

most compelling pedagogy available will require 

rigorous, routine evaluation to ensure programs 

meet BER’s changing workforce needs.

6.2 Develop a centralized education mission.

With numerous distinct educational efforts scattered among its various centers and programs, BER 

should leverage this diversity of resources and talent to create a coordinated educational approach. 

Emphasizing the informational aspect of biology 

and relying on team-based approaches, each program can then develop its own component in this 

overarching educational mission. All efforts should 

highlight the relevance of mathematics.

6.3 Initiate interdisciplinary fellowships.

BER should create a program of undergraduate and graduate interdisciplinary training that 

requires students to work with teams of scientists. One model could be an approach similar 

to that developed through the National Science 

Foundation’s Integrative Graduate Education 

and Research Traineeship program (IGERT), in 

which the host institutions create faculty teams to 

address complex research questions. An alternative 

model might focus on encouraging scientists from 

national laboratories, academia, and industry to 

forge new collaborations supported by graduate 

traineeships that involve students moving among 

the team’s institutions.

6.4 Enhance career development programs.

Continuing the existing early-career fellowship 

program, BER also should implement a program 

targeting postdoctoral scholars who wish to move 

between fields such as biology and mathematics.

6.5 Support engineering education to address crucial 

shortages in engineering disciplines relevant to BER’s 

grand research challenges.

To address crucial shortages in engineering disciplines that directly impact DOE- and BER-relevant 

missions, BER should enhance and support 

engineering education using approaches similar to 

those described above.

6.6 Install education experts at the national 

laboratories.

Education experts at the national laboratories 

could engage the research scientists more directly 

and increase their awareness of educational 

opportunities. These individuals could coordinate 

educational activities between BER programs and 

the laboratories and carry out the necessary evaluations of education programs.
54 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Chapter Six: Grand Challenges in Education and Workforce Training

I

magine every school in the nation 

involved in energy creation and 

conservation. In a manner similar to the 

U.S. Environmental Protection Agency’s 

Energy Challenge, students would take a 

pledge to measure their school’s baseline 

energy consumption and then identify 

and implement practices to reduce consumption by 10%. Different school grades 

would focus on different aspects of the 

energy challenge, ranging from identifying renewable and nonrenewable energy 

sources in their schools (elementary 

students) to measuring baseline energy 

consumption (middle school students) 

to creating and implementing alternative 

energy sources (high school students). 

Schools would create multidisciplinary 

teams to confront and help solve their 

school’s different energy challenges, with 

input from classes including mathematics, physics, 

biology, and geology.

BER would engage appropriate governmental 

agencies to develop K–12 curricula focused on 

energy conservation and alternative energy 

production. This educational program would have 

a direct and significant impact on STEM (science, 

technology, engineering, and mathematics) education and workforce development across the nation 

by (1) engaging students in activities that take 

science out of the classroom and into their daily 

lives, (2) encouraging multidisciplinary team-based 

investigation of problems and solutions, and 

(3) emphasizing the statistical and mathematical 

methods required to discuss energy consumption.

6.7 Develop collaborative teaching programs.

Interactions are needed among university scientists, 

K–12 teachers, national laboratory scientists, and 

students. An example of a Grand Challenge that 

would result in such interactions is provided in the 

sidebar, Energy and the Next Generation, this page.

Image credit: Greater New Bedford (Massachusetts) Regional 

Vocational Technical High School.

Energy and the Next Generation: Grand Challenge in Education
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 55

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

Appendices

Appendix 1: Charge Letter
56 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Appendix 1: Charge Letter
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 57

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

Appendices

Appendix 2: Grand Challenges Workshop Agenda

Gaithersburg, Maryland

March 2–5, 2010

March 2, 2010

Evening arrivals

March 3, 2010

8:30 a.m. Opening of meeting: Description of meeting goals and expected outcomes: 

 Anna Palmisano and Gary Stacey

9:00 a.m. Chris Field, Climate and Life 

9:30 a.m. Richard Murray, Synthetic Biology

10:00 a.m. Break

10:30 a.m. Virginia Dale, Sustainability

11:00 a.m. David Hill, Systems Biology

11:30 a.m. – 12:30 p.m. Lunch on site

12:30 p.m. – 3:00 p.m. Breakout groups I

1. Climate Change, chaired by David Randall

2. Systems Biology, chaired by Gary Stacey

3. Information and Synthetic Biology Systems Integration Framework, chaired 

by Gary Sayler

4. Research Framework for Energy Sustainability, chaired by Peg Riley

3:00 p.m. – 3:30 p.m. Refreshment break

3:30 p.m. – 4:30 p.m. Breakout groups I

1. Climate Change 

2. Systems Biology 

3. Information and Synthetic Biology Systems Integration Framework 

4. Research Framework for Energy Sustainability 

4:30 p.m. – 5:30 p.m. Reports of breakouts

5:30 p.m. Adjournment

March 4, 2010

8:00 a.m. Remarks by Undersecretary Steve Koonin

8:30 a.m. Leroy Hood, The Future of Systems Biology

9:00 a.m. Isaac Held, Climate Dynamics

9:30 a.m. Doug Landis, Sustainability
58 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Appendix 2: Grand Challenges Workshop Agenda

10:00 a.m. – 12:30 p.m. Breakout groups II

1. Understanding Systems across Temporal (Milliseconds to Millennia) and 

Spatial Scales (Microns to Ecosystems), chaired by Greg Petsko

2. Meeting the Workforce and Education Needs to Address the Grand 

Challenges, chaired by Jo Handelsman

3. Data Integration and Knowledgebase Development, chaired by Bob Cottingham

4. Novel Tools, Techniques and Probes, chaired by Joe Ecker

12:30 p.m. – 1:00 p.m. Lunch

1:30 p.m. – 3:00 p.m. Reports and discussion

3:30 p.m. Adjournment

March 4 evening and March 5 (until 5:00 p.m.)

Writing committee (selected members) stays over to complete draft of workshop report.
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 59

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

Appendices

Appendix 3: Grand Challenges Workshop Participants

Arkin, Adam (LBNL)

Bader, David (ORNL)

Baliga, Nitin (Institute for Systems Biology)

Bielicki, Jeffrey (ORNL)

Birdsey, Richard (USDA Forest Service)

Braam, Janet (Rice University)

Buford, Marilyn (USDA Forest Service)

Cessi, Paola (Scripps Institute of Oceanography)

Church, George (Harvard University)

Collins, William (LBNL)

Cottingham, Robert (ORNL)

Dale, Virginia (ORNL)

Denning, Scott (Colorado State University)

Dickinson, Robert (University of Texas, Austin)

Doney, Scott (Woods Hole Oceanographic Institution)

Easterling, David (NOAA)

Ecker, Joseph (The Salk Institute for Biological Studies)

Edmonds, James (PNNL)

Ehleringer, James (University of Utah)

Feddema, Johannes (University of Kansas)

Field, Christopher (Carnegie Institution of 

Washington)

Fowler, Joanna (BNL)

Freilich, Michael (NASA)

Fridlind, Ann (NASA GISS)

Fung, Inez (University of California, Berkeley)

Gilna, Paul (University of California, San Diego)

Goulden, Michael (University of California, Irvine)

Greenberg, E. Peter (University of Washington)

Greenberg, Jean (University of Chicago)

Handelsman, Jo (Yale University)

Hanson, Paul (ORNL)

Hecht, Alan (EPA)

Held, Isaac (NOAA GFDL)

Herzog, Howard (Massachusetts Institute of 

Technology)

Hill, David (Harvard Medical School)

Hood, Leroy (Institute for Systems Biology)

Hubbard, Susan (LBNL)

Hurtt, George (University of New Hampshire)

Jackson, Robert (Duke University)

Janetos, Anthony ( Joint Global Change Research 

Institute)

Joachimiak, Andrzej (ANL)

Landis, Doug (Michigan State University)

Large, William (NCAR)

Leung, Ruby (PNNL)

Liao, James (University of California, Los Angeles)

Loeffler, Frank (Georgia Institute of Technology)

Long, Stephen (University of Illinois, 

Urbana-Champaign)

Looger, Loren (Howard Hughes Medical Institute)

Lucas, Robert (University of Southern California)

Mace, Jay (University of Utah)

Marvin, Jonathan ( Janelia Farm Research Center)

Masiello, Carrie (Rice University)

Maslov, Sergei (BNL)

Meehl, Jerry (NCAR)

Meyerowitz, Elliot (California Institute of Technology)

Michalak, Anna (University of Michigan)

Murray, Richard (California Institute of Technology)

Ort, Donald (University of Illinois, 

Urbana-Champaign)

Otto-Bliesner, Bette (NCAR)

Padgette, Stephen (Monsanto Company)

Pakrasi, Himadri (Washington University in St. Louis)

Palsson, Bernard (University of California, San Diego)

Patrinos, Ari (Synthetic Genomics Inc.)

Penner, Joyce (University of Michigan)

Petsko, Gregory (Brandeis University)

Prather, Michael (University of California, Irvine)

Prince, Roger (ExxonMobil Biomedical Sciences, Inc.)

Randall, David (Colorado State University)

Remington, Karin (NIH)

Definitions of affiliation acronyms follow list of workshop participants.
60 Biological and Environmental Research Advisory Committee March 2010 Workshop December 2010

Appendix 3: Grand Challenges Workshop Participants

Riley, Margaret (University of Massachusetts, 

Amherst)

Rogers, Alistair (BNL)

Roth, Fritz (Harvard University)

Sayler, Gary (University of Tennessee, Knoxville)

Schneider, Edwin (Center for Ocean-LandAtmosphere Studies)

Simpson, Michael (ORNL)

Smith, Melinda (Yale University)

Stacey, Gary (University of Missouri)

Sussman, Michael (University of Wisconsin, Madison)

Thornton, Peter (ORNL)

Wall, Judy (University of Missouri)

Washington, Warren (NCAR)

Weinberger, Leor (University of California, San Diego)

Wilbanks, Thomas (ORNL)

Wildung, Raymond (PNNL)

Wiscombe, Warren (NASA GSFC)

Xu, Dong (University of Missouri)

Zachara, John (PNNL)

Zhang, Minghua (State University of New York at 

Stony Brook)

Affiliation Acronyms

ANL Argonne National Laboratory

BNL Brookhaven National Laboratory

EPA Environmental Protection Agency

LBNL Lawrence Berkeley National Laboratory

NASA National Aeronautics and Space Administration

NASA GISS NASA Goddard Institute for Space Studies

NASA GSFC NASA Goddard Space Flight Center

NCAR National Center for Atmospheric Research

NIH National Institutes of Health

NOAA National Oceanic and Atmospheric Administration

NOAA GFDL NOAA Geophysical Fluid Dynamics Laboratory

NREL National Renewable Energy Laboratory

ORNL Oak Ridge National Laboratory

PNNL Pacific Northwest National Laboratory

USDA U.S. Department of Agriculture
December 2010 Biological and Environmental Research Advisory Committee March 2010 Workshop 61

Grand Challenges for Biological and Environmental Research: A Long-Term Vision

Appendices

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