U.S. Department of Energy

Office of Science

Office of Biological and Environmental Research

Organizers

New Frontiers in Characterizing Biological Systems

Report from the May 2009 Workshop

Convened by

Suggested citation for this report: U.S. DOE. 2009. New Frontiers in Characterizing Biological Systems: Report from the May 2009 

Workshop, DOE/SC-0121, U.S. Department of Energy Office of Science (genomicscience.energy.gov/characterization/).

Arthur Katz 

arthur.katz@science.doe.gov

301.903.4932

www.science.doe.gov/Program_Offices/BER.htm

Dean Cole

dean.cole@science.doe.gov

301.903.3268

As part of the U.S. Department of Energy’s (DOE) Office of Science, the Office of Biological and Environmental 

Research (BER) supports fundamental research and technology development aimed at achieving predictive, 

systems-level understanding of complex biological and environmental systems to advance DOE missions in 

energy, climate, and environment.

To promote development of a new generation of characterization technologies, BER hosted the workshop, New 

Frontiers in Characterizing Biological Systems, in May 2009. Experts from scientific disciplines relevant to DOE 

missions and from the enabling technologies (e.g., optical spectroscopy, genomic sequencing technology, electrochemistry, electron microscopy, and mass spectrometry) met to determine the opportunities and requirements 

for identifying and developing new tools and analytical approaches for characterizing cellular- and multicellularlevel functions and processes that are essential to develop solutions for DOE missions in biofuels, carbon cycling 

and biosequestration, low dose radiation, and environmental stewardship. The intent of the workshop was to 

broadly explore future technology capabilities that are needed, not current technologies and their development. 

Discussion of specific technologies was intended to be illustrative of some promising approaches, but no attempt 

was made to be exhaustive. 

To define such capabilities, participants were organized into working groups based on areas of biological scale: 

cellular, multicellular, and interface processes. This report outlines the workshop’s findings and highlights key 

opportunities for establishing a framework for research activities.
i

New Frontiers in Characterizing 

Biological Systems

Report from the May 2009 Workshop

Workshop Dates: May 13–14, 2009

Publication Date: October 2009

Office of Biological and Environmental Research

This document is available at the DOE Genomic Science website (genomicscience.energy.gov/characterization/).

DOE/SC-0121
ii New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009
October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems iii

Executive Summary....................................................................................................................................................... v

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

2. Biological Challenges...............................................................................................................................................5

2.1 Understanding the Cell and Its Response to Chemical and Physical Perturbations...................6

2.2 Understanding Interactions Between Cells: From One to Many ........................................................8

2.3 Understanding Dynamic Biological Systems Across Multiple Scales 

 of Time and Distance........................................................................................................................................13

Summary of Key Biological Challenges............................................................................................................17

3. Technological and Capability Needs.................................................................................................................19

3.1 Expanding Global Characterization Capabilities....................................................................................19

3.2 Identifying and Measuring Rare Events, Molecules, and Cells Within Complex, 

 Heterogeneous Biological Systems.............................................................................................................23

3.3 Seeing It All: Simultaneous Measurements Across Multiple Dimensions.....................................26

3.4 Integrating Information...................................................................................................................................32

Summary of Key Technological and Capability Challenges......................................................................38

4. Path Forward .............................................................................................................................................................39

4.1 Technology: Meeting the Measurement Challenge..............................................................................40

4.2 Research Strategy: Linking Observations from Cells to Natural Communities...........................43

4.3 Knowledge Visualization: Overcoming Challenges of Data Integration 

 and Systems Analysis.......................................................................................................................................44

4.4 Integration: Teaming at Disciplinary Interfaces To Advance Frontiers 

 in Biological Research ......................................................................................................................................45

Appendix 1: Agenda for New Frontiers in Characterizing Biological Systems Workshop..................47

Appendix 2: Workshop Writing Team and Participants..................................................................................49

Appendix 3: Bibliography..........................................................................................................................................51

Appendix 4: Figure Credits........................................................................................................................................55

Acronym List, Reference Table of Numeric Prefixes...........................................................Inside back cover

Contents
iv New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

List of Sidebars

Chapter 2. Biological Challenges

Real-Time Chemical and Structural Analyses Are Needed To Improve Enzymatic Degradation 

of Lignocellulosic Biomass....................................................................................................................................................................... 9

Microbial Environmental Remediation of Chlorinated Solvents..............................................................................................10

Uncovering Key Interdependent Relationships Among Organisms To Leverage Beneficial Capabilities.................11

Working Toward Real-Time Measurements of Biological Responses: The Low Dose Radiation Challenge..............13

Targeting the Interfacial Region Between Microbial Cells and the Geosphere.......................................................... 14–15

Chapter 3. Technological and Capability Needs

Whole-Genome Amplification Holds Promise for Single-Cell Genome Drafting ..............................................................21

The Value of Understanding Rare Events and Cells......................................................................................................................24

Investigating Rare Microbes’ Function and Phylogeny at the Single-Cell Level Using EL-FISH NanoSIMS ..............25

Single-Molecule Measurements Reveal Phenotype Switching in Genetically Identical Cells.......................................27

Super Resolution Light Microscopies Enable the Imaging, Counting, and Localization 

of Single Proteins in Microbes..............................................................................................................................................................30

Challenges for Imaging Live Plant Cells............................................................................................................................................31

Integration of Proteomics and Genomics Reveals a Core Proteome......................................................................................34
October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems v

•	 Understand the Cell and Its Response to Chemical and Physical Perturbations. The characterization of genome sequences and their products has 

highlighted the need for identifying and characterizing the other parts that comprise the cell. Many 

of these components are difficult to identify or 

quantify. Completing the “parts list” of the cell and 

determining how cellular networks composed of 

these parts respond to local physical and chemical 

changes are priorities.

•	 Understand the Interactions Between Cells.

Many cellular interactions are poorly understood, 

such as how cells communicate, regulate their 

genetic information in response to other cells, and 

combine their capabilities for higher-order functions. A needed advance involves routinely interpreting how multiple cells, with similar or different 

genetic content, combine to process information, 

energy, and materials.

•	 Understand the Functioning of Biological 

Systems Across Multiple Scales of Time and 

Distance. A connection between the genome and 

biological function at physical scales as diverse as 

an individual cell and an ecosystem or at temporal 

scales as diverse as seconds and years can be apparent but difficult to define. The dynamic nature, 

spatial and temporal ordering, and nonlinearity of 

system responses confound interpretations at any 

level of inquiry. The ability to design experiments, 

identify and model appropriate system components, and predict function are challenges that must 

be addressed.

These primary knowledge gaps are relevant to understanding the processing of biomass into different 

chemical forms, the cycling of carbon, and the transformation of contaminants in the environment. They are 

fundamental challenges intrinsic to diverse biological 

and environmental concerns. Timely resolution of 

these problems will revolutionize our understanding 

of biological systems and significantly advance DOE 

mission science. Achieving these goals will depend on 

a transformation of current measurement capabilities. 

Numerous technological approaches can be considered. 

Understanding the relationship between the 

genome and functional processes is the 

most significant challenge and potentially 

enabling advancement that faces modern biology. 

Elucidating this connection presents opportunities 

for realizing sustainable energy solutions and responsible management of natural resources. Understanding the function of the genome is at the core of the 

Department of Energy’s (DOE) Genomic Science 

program and is central to realizing DOE’s mission 

goals in bioenergy research, carbon management, 

and environmental stewardship. Just as genomic 

science is central to these mission goals, technology 

advancements are central to genomic science and to 

unlocking the connections between the genome and 

functional processes occurring at cellular to global 

environmental scales. New developments in characterization technologies will be essential for driving 

advances in genomic science and in our understanding of the genomic bases of natural processes.

In May 2009, DOE’s Office of Biological and Environmental Research (BER) held the New Frontiers 

in Characterizing Biological Systems workshop to 

address the next generation of challenges in genomic 

science and its connection to functional systems. 

The workshop included a diverse array of scientists 

and engineers with expertise in the mission-relevant 

biological and environmental sciences and in the 

analytical and physical sciences. Working groups were 

focused on defining the challenges associated with 

studies at the cellular, multicellular, and interfacial 

levels. Common themes and priorities emerged from 

the different groups. There was universal agreement 

that appropriate advances in characterization technologies will first depend on articulation of the major 

challenges that face the biological and environmental 

science communities. To that end, this report—

rather than comprehensively discussing currently 

available technologies—highlights the major challenges and outlines the future technological capabilities required to meet them. Workshop participants 

identified numerous knowledge gaps that inhibit the 

understanding of biological systems, and these can be 

distilled into three major challenges:

Executive Summary
vi New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

Regardless of approach, the specific technical capabilities needed to fill these knowledge gaps include:

•	 Expand and Integrate Global Characterization 

Capabilities. Biological systems are composed of a 

wide array of differing molecular species. We need to 

“see it all” and be able to monitor dynamic changes 

at increased spatial resolution. Currently, we cannot 

probe many of the dynamic processes occurring 

within the cell at the required chemical, spatial, 

or temporal resolution nor can we measure the 

response of these processes to chemical and physical 

perturbations. A wide assortment of metabolites, 

lipids, carbohydrates, and other biochemicals simply 

cannot be identified or accurately measured. Understanding the interactions and fates of these materials 

is essential for understanding cell function. Combining global measurements and extending the ability 

to comprehensively characterize and manipulate 

any system component are needed advances. New 

technologies for completing the parts list of cellular 

components are essential.

•	 Identify and Measure Important Molecular 

Species, Events, and Cells. Biological systems are 

recognized as containing complex mixtures of different chemical and biological species. The relative 

importance of particular components to functional 

outcomes is difficult to assess. Even more difficult 

is associating rare events or minority components 

to functional outcomes. Current technologies have 

the ability to monitor single cells and detect single 

molecules. However, they are limited in their ability 

to do so in complex, heterogeneous environments, 

let alone in natural systems. Technologies are 

needed that can identify and detect single or small 

populations of molecules or cells amidst complex, 

heterogeneous backgrounds. These technologies 

will aid in understanding the effects of chemical 

and physical forces on the cell and the interactions 

among cells.

•	 Simultaneously Measure Many Chemical and 

Biological Species Across Broad Spatial and 

Temporal Ranges. Biological information is carried 

in a wide variety of molecules and is expressed across 

broad spatial and temporal ranges. Current tools 

often are appropriate for providing characterizations 

at only specific spatial and temporal scales, or they 

are limited in the number or type of species that 

can be measured. There is a strong need to bridge 

the discontinuities between different measurements and to address the gaps that occur as we span 

length and time scales. Multiple “dimensions” need 

to be added to biological measurements so that 

molecular events can be linked to cellular, multicellular, and environmental scales.

•	 Integrate and Interpret Diverse Information 

and Technology Platforms. Biological systems 

can be assessed at many levels ranging from the 

molecular to the cellular to the ecosystem scale. 

Beyond the challenges of how to measure and collect such biological information, critical challenges 

related to what to measure and how to interpret the 

information remain. Addressing these problems 

will depend on effective tools for integrating and 

interpreting the information. Useful databases and 

computational approaches are needed for integrating measurement information and for modeling 

systems at multiple scales. Currently, we do not 

know at which scale to measure or model biological 

system function. Effective focusing of measurement 

technologies will rely on an iterative relationship 

with computational modeling approaches. Models 

that are capable of dealing with the gradients and 

discontinuities of biological systems must be developed and integrated with experimental design.

Overcoming these technical challenges will facilitate 

basic understanding of biological processes, not just at 

a particular physical or temporal scale, but the linking 

and relating of such scales to genomic information. 

Focused advancements in characterization technologies will address critical knowledge gaps and support 

the realization of mission needs. These advancements 

will broadly impact the biological and environmental 

sciences in general and ultimately transform biology 

into a quantitative science.

Moving forward in these needed developments will 

require concerted efforts on several fronts. Key among 

these is investment in stimulating technology developments. These developments will need to proceed 

within the context of the primary biological challenges identified in this report. A priority should be 

the development of approaches for simultaneously 

Executive Summary
vii

Executive Summary

October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems

assessing multiple species at appropriate spatial and 

temporal resolution. This likely will proceed through 

combinations of different measurement techniques. 

Technology advancements also must commence with 

developing high-throughput parallel approaches for 

making sensitive measurements in heterogeneous 

environments. Small molecules within single cells 

and small populations must be tracked. Measurement 

techniques alone will not be sufficient. Rather, what is 

required to reveal function is the ability to manipulate 

relevant biological, chemical, and physical variables 

while tracking their effects on biological systems.

A second key focus should be on promoting analysis 

of the biological systems most relevant to DOE missions. We must move past the study of relatively simple 

model organisms and toward the study of organisms 

within their natural environmental setting (e.g., in 

planta and in terra). Initially, organism systems that are 

representative of the technological and environmental 

problems we wish to understand must be identified. 

Capabilities for culturing or studying organisms at the 

single-cell level need to improve along with the tools 

for manipulating and studying these organisms at the 

molecular level. Systems research will need to progress 

past the study of individual organisms in isolation and 

toward systems of increasing biological complexity, 

replete with the structuring and heterogeneity found 

in natural systems. Interrogating natural systems in situ

should be a long-term goal.

A third focus should be on integrating biological and 

technological developments through computational 

tools. Large, disparate datasets must be combined and 

analyzed to yield new insights into the function of 

biological systems across diverse scales. Iterative cycles 

of experimentation and modeling in concert with new 

theory will be needed to define the appropriate scales 

for measuring, modeling, and functionally understanding biological processes. This integration will need 

to capitalize on DOE BER traditions and success in 

integrating scientific disciplines and in solving grand 

challenge problems. Multidisciplinary teaming should 

be promoted and facilitated through integrative training opportunities, incentives for collaborative science, 

and facilitated access to high-end technologies.

Understanding the connections that link the genome 

to events at different scales promises to unravel many 

of the challenges facing the biological and environmental sciences. Such insight will enable effective 

routes to sustainable energy solutions and responsible 

stewardship of the environment. Analytical technology developments are key to sustaining progress 

toward these goals and addressing the challenges 

and knowledge gaps that emerge. The complexity, 

emergent properties, and multiple scales of biological 

systems present substantial obstacles. The tremendous 

progress in characterizing whole genomes, which only 

a few decades ago was considered a nearly intractable 

problem, was enabled by the focused integration of 

a biological problem with technological advances in 

analytical measurements and computation. Similarly, 

the seemingly daunting challenges that we now face 

can be addressed through focused developments and 

bold advances in characterization technologies.
viii New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009
October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems 1

Characteristics of Biological Systems

I

n the environment, plants and microorganisms 

perform key functions that sustain the biosphere. 

These functions include capturing and converting 

sunlight to chemical energy, cycling carbon and nutrients, and detoxifying and immobilizing natural and 

man-made pollutants. Living organisms collectively 

possess an astonishing array of capabilities that, if characterized and understood, could be leveraged to revolutionize approaches for solving many of the critical 

problems currently facing the nation and planet. For 

example, photosynthetic organisms use energy from 

sunlight to fix carbon dioxide and produce a myriad 

of carbon-based molecules needed for cell growth and 

other metabolic functions. Some organisms produce 

enzymes that break down complex biopolymers such 

as lignocellulose into individual component molecules 

that other organisms, in turn, can employ. Understanding the basis of these complex biological processes for 

capturing carbon dioxide, immobilizing environmental 

contaminants, and developing biofuels is of keen interest to the Department of Energy (DOE). Similarly, 

understanding the dynamic interactions between living 

organisms and the environment is critical to predicting 

and mitigating the impacts of energy production on 

the environment and human health.

Biological systems are extremely complex and thus 

challenging to understand (see box, Characteristics of 

Biological Systems, this page). This inherent complexity makes it difficult to predict and manipulate their 

behavior, for example, to produce biofuels or mitigate 

environmental contamination. The advent of molecular 

biology steered science toward “fundamental reductionism” (Woese 2004), which focused research on 

determining the structure and function of individual 

cellular parts. To develop a predictive understanding 

of these complex systems, today’s scientists are starting 

to build mechanistic models to learn how individual 

cellular components as well as whole cells and mixedorganism (heterogeneous) communities can give 

rise to emergent properties. The biological research 

paradigm thus is transitioning from understanding 

individual parts toward understanding biology at a 

systems level. The application of systems-based biological approaches will provide a basis for enhancing 

and manipulating “natural” processes for societal and 

environmental benefits.

Contributing to this rapid shift in approach are the 

availability of whole-genome sequences for an increasing number and variety of organisms and the technical 

advances that enable such high-throughput genomeWhy Biological Systems Are Difficult To Predict

•	 System components (often unknown, multifunctional, and heterogeneous) operate in crowded 

volumes.

•	 Seemingly minor system components (rare events 

and rare organisms) can have major functional 

influences.

•	 Functional systems involve multiple component 

levels (e.g., molecular, molecular assemblies, 

cells, tissues, communities, environments, and 

ecosystems).

•	 Functional systems can be hierarchically arranged, 

though connections between levels are poorly 

understood.

•	 Functional systems display emergent properties 

at all system levels, thus making extrapolations 

difficult.

•	 Functional systems exploit small thermodynamic 

gradients and stochastics.

•	 System operation 

ɡ Is dynamic and occurs across multiple spatial 

and temporal scales

ɡ Often depends on spatial and temporal ordering

ɡ Can be nonlinear and adaptive across a range 

of conditions.

Simplifying Features of Biological Systems

•	 Diverse systems’ sharing of a common molecular 

basis (e.g., nucleic acids, proteins, lipids, and carbohydrates) allows comparative analyses to elucidate their operation.

•	 Systems’ frequent modularity allows approximation of particular aspects.

•	 Systems’ frequent exploitation of similar feedback 

mechanisms allows motifs to be recognized and 

understood. 

1. Introduction
2 New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

1. Introduction

scale measurements. As a result, biological science is 

evolving from being merely descriptive to providing 

quantitative and mechanistic explanations of cellular 

processes. Ultimately, such a predictive science—and 

the crucial capabilities for collecting and using large 

amounts of biological data—will be required to 

develop and evaluate models of biological function. 

Understanding how different assemblies of molecules 

in cells give rise to functional outcomes is fundamental 

to systems and synthetic biology. The advancement of 

this new biology will require novel analytical and 

computational technologies and approaches to validate 

the predictions derived from system-level research. 

Varied methodologies will be needed to measure and 

track the temporal and spatial disposition of molecules 

that differ widely in structure and concentration in 

cells and to determine how networks of proteins and 

regulatory molecules are organized and give rise to 

specific activities at larger spatial scales. Moreover, such 

measurements are needed to characterize individual 

cells within populations as functions of space and time 

and varying environmental conditions.

To solve its mission challenges, the Office of Biological and Environmental Research (BER) within 

DOE’s Office of Science promotes the development of 

systems biology approaches—the major thrust of its 

Genomic Science program (formerly Genomics:GTL; 

see Fig. 1.1. Genomic Science Program Goal and 

Objectives, below). In a 2006 review of this program, 

the National Research Council (NRC) endorsed 

DOE’s systems biology research in plant and microbial 

biology. Furthermore, NRC stated that development of 

new technologies and methods was not only a logical 

pathway but an essential one for accomplishing the 

program’s goals. The major aim of BER’s Genomic Science program is to develop a predictive understanding 

of biological systems relevant to DOE mission challenges and to develop the commensurate tools (see 

Fig. 1.2. DOE Genomic Science Program, p. 3). To 

date, the program has emphasized system-level investigations of individual organisms and the generation of 

data for constructing and evaluating predictive mechanistic models. Longer-term goals of the program are to 

develop a predictive understanding of complex biological communities and to improve the current technical 

Fig. 1.1. Genomic Science Program Goal and Objectives.
October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems 3

1. Introduction

and scientific infrastructure to address these problems. 

These specific goals and infrastructure improvements 

have been described and reiterated in several reports 

(American Academy of Microbiology 2006) and 

Genomic Science roadmaps, including Breaking the 

Biological Barriers to Cellulosic Ethanol: A Joint Research 

Agenda (U.S. DOE 2006), Carbon Cycling and Biosequestration: Integrating Biology through Systems Science

(U.S. DOE 2008), and U.S. Department of Energy Office 

of Science Systems Biology Knowledgebase for a New Era 

in Biology (U.S. DOE 2009). These reports discuss 

the promise of a systems biology research approach in 

addressing critical DOE missions and complex problems of national and international interest, and they lay 

out the technical and scientific requirements for timely 

solution to such challenges.

As systems biology research is still at an early stage, 

emphasis in BER’s Genomic Science program has been

on the select tools and fundamental science required to 

conduct this research. Recent developments in several 

areas are bringing new opportunities to accelerate science. For example, maturing large-scale “omics” technologies are allowing cell responses to be monitored over 

increasing time scales. More targeted analytical techniques are being used to measure multiple responses at 

the single-cell level, and reconstituted multicellular systems are becoming more sophisticated. These and other 

advances are improving our understanding of organisms 

at a systems level, but many technological barriers still 

need to be overcome before significant progress is made 

in achieving a predictive, system-level understanding of 

plants and microbes.

Over the last several years, the Genomic Science program has made substantial investments in fundamental 

analytical technologies needed to support systems 

biology studies of plants and microbes, especially in 

the areas of proteomics and genome sequencing. Many 

hundreds of different organisms have been sequenced, 

and the metagenomes of numerous microbial communities have been analyzed. The program has laid the 

Fig. 1.2. DOE Genomic Science Program.
4 New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

1. Introduction

foundation for understanding the regulatory dynamics 

of individual model microorganisms such as Shewanella, 

Geobacter, and Desulfovibrio, as well as for deducing the 

gene and protein networks that control basic microbial 

functions. Techniques are being developed to identify protein complexes (Pelletier et al. 2008; Dong et 

al. 2008; Hura et al. 2009) and to track the position 

of molecules in complex systems at the cellular level 

(Vinson and Chin 2007; Blow 2009; Xie et al. 2008). 

Progress in these areas has resulted largely from the 

DOE emphasis on developing new and innovative tools 

to enable such research. Although current technologies, 

such as genome sequencing and high-throughput methods for characterizing proteins and protein complexes, 

have been and will continue to be critical to systems 

biology research, DOE recognizes that new tools and 

capabilities are essential for analyzing and understanding complex biological systems and validating their individual components. To that end, the objective of DOE’s 

New Frontiers in Characterizing Biological Systems 

workshop was to identify the most significant scientific 

challenges facing biology in areas relevant to DOE missions and to develop a vision for the new capabilities 

and tools needed to meet these challenges.
October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems 5

To develop a predictive understanding of 

biological systems, BER’s Genomic Science program has emphasized systems-level 

investigations of individual organisms, primarily 

genome-based studies of microbes. Although current 

research approaches have not achieved the ultimate 

goal of measuring and predicting the response of individual cells within a complex environment, they have 

revealed the challenges involved. These challenges 

must be met to realize the program’s longer-term 

goal of being able to predict the behavior of complex, 

natural communities of organisms from the behavior 

of their dynamic individual parts. To understand the 

connection between the genome and natural environmental systems, three interrelated grand challenges 

have been identified:

1. Understanding the cell and its response to chemical 

and physical perturbations.

2. Understanding interactions between cells.

3. Understanding dynamic biological systems across 

multiple scales of time and distance.

The key biological challenges described in this chapter 

are summarized on p. 17.

To achieve this understanding and address critical 

questions at the frontiers of biology, the scope and 

scale of current measurements must be expanded. In 

many cases, this involves combining systems-level 

physiological studies with more-focused measurements 

of individual components, an approach that requires 

greater spatial and temporal resolution. These studies 

will aid the construction and evaluation of mechanistic models that can predict the consequences of 

cellular and environmental changes to an organism’s 

regulatory network. 

Overcoming these challenges will allow better use 

and realization of the information contained in the 

genome and will enable the biological and environmental science communities to move forward in 

addressing concerns in energy production, carbon 

cycling and biosequestration, and environmental 

remediation (see Fig. 2.1. Cellular Systems for Diverse 

National Needs, this page). Details of these challenges 

and the technological measurements needed to overcome them are highlighted in the following sections. 

This is an exciting time in science; advances in biology and concomitant improvements in technology 

and information processing have led us to the point 

Shewanella oneidensis cells can reduce uranium and 

metals in contaminated environments.

Different cell types make up the various plant tissues in 

Populus trichocarpa (a poplar species), a model system 

for bioenergy and carbon cycling.

Fig. 2.1. Cellular Systems for Diverse National 

Needs. Guided by the biological information encoded 

within genome sequences, we can begin to identify, 

understand, re-engineer, and harness specific cellular 

systems for energy production, environmental remediation, and other national needs. [Image credits: 

Shewanella oneidensis image by Rizlan Bencheikh 

and Bruce Arey, Environmental Molecular Sciences 

Laboratory, DOE Pacific Northwest National Laboratory. Poplar image by DOE BioEnergy Science Center, 

DOE Oak Ridge National Laboratory.]

2. Biological Challenges
6

2. Biological Challenges

New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

where we can begin to address biological processes as 

they occur on a whole-systems scale. With appropriate coordination and efforts, the coming decade can 

see major advances in understanding how individual 

cellular properties give rise to community responses. In 

turn, this should lead to revolutionary advances in the 

application of biological solutions to societal needs.

2.1 Understanding the Cell and Its Response 

to Chemical and Physical Perturbations

The cell is the basic building block of life, and yet our 

understanding of systems’ architecture and information flow in even the simplest cells is rudimentary. 

Methods to probe many of the dynamic processes 

occurring within the cell with the required chemical, 

temporal, and spatial resolutions simply do not exist. 

Additionally, the ability to assess cellular response to 

extrinsic chemical and physical perturbations is limited. Ultimately, understanding the full range of cellular 

phenomena will aid in interpreting a cell’s niche within 

its community and its relationship to other organisms.

Even in the postgenomic age, the “parts list” of cellular components is incomplete. Prior efforts in the 

Genomic Science program have aided in defining 

genetic-based components and many mechanistic 

relationships. However, to date, our understanding 

of small molecules and the various modifications, 

interactions, and functions of proteins is far from 

complete (see Fig. 2.2. A Glimpse into the Cellular 

World, p. 7). Thus, there is a pressing need to develop 

new tools for defining the functional role of cellular 

constituents and to characterize the full suite of interactions occurring within model cells. These efforts 

will require innovative engineering and measurement 

platforms as well as knowledge about cellular function 

and fate. With completion of the dynamic parts list, an 

important goal will be to characterize and validate the 

subcellular and cellular networks that regulate energy 

and material flows. For example, the key pathways and 

genes for lignin and carbohydrate monomer synthesis 

are known, but the full suite of genes and pathways 

for polymerization, crosslinking, and structure is not. 

Ideally, such efforts will allow probing a cell within its 

environment, thereby gaining the information needed 

to influence microbial and plant systems for specific 

purposes such as maximizing usable energy output.

In the quest for predictive science, the question is: 

once we have the parts list of the cell and understand 

how it changes over time in response to environmental factors, can we build realistic models of cells that 

capture and simulate this information? The greater 

the number of responses that can be measured simultaneously, the greater the specificity of the models 

that can be constructed. However, not all cells within 

a population respond equally, so simultaneously 

measuring multiple activities in single cells will be 

required. In the near term, this capability is likely to 

be achieved by measuring the expression of multiple 

genes at the individual cell level and, with future 

advances, measuring the activity of multiple signaling pathways or mapping the locations and activities 

of proteins and protein complexes.

Performing nondestructive single-cell measurements 

presents a considerable challenge. Current labeling technologies for live cell imaging are limited to 

only a handful of reagents; expanding this reagent 

set will be essential for understanding how cellular 

parts assemble and interact over time. Advances are 

needed in the development of “nonperturbing” labels 

or tags that provide the means for directly detecting 

specific molecules. Measuring force, temperature, 

pressure, shape, and electrical potential within the 

cell is surprisingly difficult, and yet these parameters 

must be characterized and correlated to the genome 

and molecular characteristics, cellular activity, and 

cellular fate to understand how cells operate. Additionally, the effects of chemical and nonchemical changes, 

such as mechanical and electrical forces and physical 

parameters, must be understood. Such information is 

necessary to comprehend how cells respond to stress 

and environmental changes.

The greatest cellular measurement need is to develop 

methods that operate on wide spatial, chemical, and 

temporal scales and that provide data that are global in 

nature. There is a plethora of existing tools for characterizing well-defined individual parameters within 

cells, but most of these measurements are limited to a 

single dimension, either spatial or temporal. These 

current measurement approaches require careful 

selection and optimization of the probe for each 

parameter being measured. Moreover, they lack the 

ability to be adequately multiplexed to provide more 
7

2. Biological Challenges

October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems

Fig. 2.2. A Glimpse into the Cellular World. Molecules and structures within cells largely determine cellular behavior 

and characteristics. Although a single cell is one of the most chemically complicated systems known, just a few basic 

types of small molecules give rise to the extraordinary chemical diversity of life. Small molecules—typically containing 

30 or fewer carbon atoms—are linked to assemble complexes and structures of increasing size and complexity. We know 

the genome provides instructions for building nucleic acids from nucleotides and proteins from amino acids, but much 

less is known about how the cell directs the assembly of macromolecular structures derived from sugars, fatty acids, 

and other small molecules or how the cell builds even larger cellular structures from macromolecules. The figure shows 

spatial scales associated with several molecules and structures in plant and microbial cells.
8

2. Biological Challenges

New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

global information. For example, although the interaction between a few specific molecules can be probed 

using microscopy approaches such as fluorescence 

resonance energy transfer, this technique does not 

support the simultaneous measurement of many 

interactions. While mass spectrometry does allow the 

global characterization of cellular contents, it is not yet 

possible to measure the levels of a large number of 

different proteins in a dynamic fashion at the single-cell 

level. Increases in the wavelength range and intensity of 

spectroscopic approaches could provide improvements 

in spatial and temporal resolution, but, without 

multiplexed probes, they will be unable to acquire the 

information most needed to understand complex 

cellular responses.

New tools to characterize and quantify the characteristics and molecular parts of individual cells and 

their regulatory networks as they respond to chemical and physical perturbations will enable the understanding, prediction, and manipulation not possible 

today. Such capabilities will illuminate the fate and 

transport of contaminants, cellular responses to 

climatic change, nutrient bioavailability, carbon 

biosequestration, how cells live in extreme environments, and processes for biomass degradation (see 

sidebar, Real-Time Chemical and Structural Analyses Are Needed To Improve Enzymatic Degradation 

of Lignocellulosic Biomass, p. 9). Many of these 

topic areas cannot be addressed with existing technologies and computational resources. Development 

and advancement of the next generation of imaging, 

spectroscopic, and high-throughput characterization 

tools will aid in understanding cellular processes and 

lead to capabilities for manipulating and optimizing 

a cell’s output for desired applications. An improved 

understanding of cellular components, their spatial 

and temporal arrangements into networks, and their 

responses to chemical and physical changes will 

prove to be transformative for the biological sciences. Without a detailed understanding of cells—

the building blocks of organisms and environmental 

communities—our ability to model and harvest 

their output will remain limited.

2.2 Understanding Interactions Between Cells: 

From One to Many

Whether found in a multicellular organism or surrounded 

by other cells within a heterogeneous community, a cell 

rarely acts independently from others. Rather, cells act 

as part of a community, tissue, or ecosystem. Biological 

function is critically dependent on such interactions. 

Elucidating how cells interact with one another is necessary for predicting how natural processes arise from 

the collective function of individual cells.

By understanding and manipulating populations of 

cells, cellular communities, and complex organisms, 

the research community stands ready to make the leap 

to understanding cellular function in the context of 

the surrounding physical and chemical environment. 

The activities of individual cells give rise to collective 

phenomena that have tremendous impact at macroscopic scales. However, even within populations 

considered to be clonal, there are extensive cell-to-cell 

variations in properties and behavior that may be due 

to stochastic variations in gene expression, exposure 

to variable microenvironmental conditions, or other 

factors. A well-known example is the differentiation in 

Anabaena, a type of cyanobacteria in which only some 

cells form nitrogen-fixing heterocysts. In heterogeneous populations, cell-to-cell variations typically are 

even more pronounced. This heterogeneity impacts 

the net function of a population of cells and is what 

makes functional measurements difficult. An example 

of collective phenomena arising from the activities of 

heterogeneous cells is the decomposition of structurally and chemically complex organic material by 

microbial communities, as seen in the degradation of 

lignocellulose in the termite hindgut by a microbial 

consortium (Warnecke et al. 2007). Another example 

is the cleanup of groundwater contaminants by the 

rare microbial species Dehalococcoides (see sidebar, 

Microbial Environmental Remediation of Chlorinated 

Solvents, p. 10). There is a critical need to develop 

global measurement approaches that do not average 

large populations of cells but characterize the composition and activity of the individual cells making up 

the population or community. Among other challenges, the global approaches for cell characterization 

described in Section 2.1, p. 6, need to be deployed in 

high-throughput mode.
9

2. Biological Challenges

October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems

Real-Time Chemical and Structural Analyses Are Needed To Improve 

Enzymatic Degradation of Lignocellulosic Biomass 

the reaction? Does the presence of lignin crosslinking “stall” the hydrolysis progress?

•	 When using different glycosyl hydrolases, what 

are the role and mode of synergy that will improve 

degradation rate and extent? Do different enzymes 

help process different biomass cross-linkages?

To address these challenges, dynamic chemical and 

structural measurements need to be made at the 

enzyme-lignocellulose interface as enzyme catalysis 

proceeds. Essentially, nanometer-scale resolution 

of chemical and structural information needs to be 

coupled with dynamic information that spans from 

milliseconds to minutes.

Understanding functional cellular processes will be greatly facilitated by better 

insight into the component parts, their orchestration, and the multiple length and time 

scales in which they function. An illustrative 

example is microbial degradation of lignocellulosic biomass that occurs at microbial-plant 

interfaces. Understanding this process is critical to improving both plant biomass conversion to biofuels and the carbon cycling models 

used in climate projections.

Various extracellular enzymes, such as glycosyl hydrolases (i.e., cellulases), are tethered to 

the microbe’s surface using a protein scaffold. 

The cellulases may be “sitting” from tens to 

a hundred nanometers from the cell surface 

while interfacing with their lignocellulosic 

substrate. A more complex example is the 

cellulosome of Clostridium thermocellum. 

This cellulosome, analogous to a Swiss Army 

knife, is a multienzyme complex with multiple glycosyl hydrolases bound to a common 

flexible scaffoldin protein. The cellulosome is 

bound to the biomass substrate by a carbohydrate binding domain (CBD) and is tethered 

to the host microbe (see figure, this page).

Despite years of study focused on glycosyl 

hydrolases, a more detailed understanding is 

needed of how the molecular parts are orchestrated in space and time. Cellulases are known to be 

slow degraders, and the reaction is often incomplete. 

Current questions whose answers may improve the 

action of hydrolytic enzymes on biomass include:

•	 Is the maximum potential turnover rate limited by 

the intrinsic kinetics of action on a solid substrate?

•	 Regarding the mode of enzyme binding and 

initiation of the reaction, how does the polysaccharide thread into the active site?

•	 What are the effects of the heterogeneity of the 

lignocellulosic biomass surface on the extent of 

A

Microbial 

Cellulosomes.

(A) Simplified 

schematic depicting components 

of the cellulosome 

of Clostridium 

thermocellum. 

[Adapted from 

Bayer, Shoham, 

and Lamed 2006 

and used with kind permission from Springer Science and Business 

Media.] (B) Electron tomography image showing a cross-section of 

a cellulosome (green) tethered to the cell surface of C. cellulolyticum (blue). [Image courtesy of Donohoe and Haas, National 

Renewable Energy Laboratory, 2009, unpublished data.]

B
10

2. Biological Challenges

New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

Microbial Environmental Remediation of Chlorinated Solvents

Low-abundant Dehalococcoides species carry 

out the complete environmental remediation 

of the chlorinated solvents tetrachloroethene and 

trichloroethene (TCE) as well as other chlorinated 

ethenes. TCE compounds—the most abundant 

groundwater contaminants in the United States—are 

stepwise reductively dehalogenated to ethenes, via 

cis-dichloroethene and vinyl chloride (VC) as intermediates. These reductive dehalogenation reactions 

represent the sole energy-conserving pathway, known 

as organohalide respiration, in Dehalococcoides. Key 

enzymes in organohalide respiration are cobalamineand Fe-S clusters containing reductive dehalogenases and hydrogenases. Dehalococcoides are strict 

anaerobic, H2

-consuming bacteria highly adapted 

to this organohalide respiration niche. Despite 

a streamlined genome of ~1.4 million base pairs 

(among the smallest of any free-living microorganism), Dehalococcoides show great diversity in reductive dehalogenases, 36 of which are found in strain 

VS. Interestingly, most of this important diversity in 

reductive dehalogenases—including those responsible for TCE and VC reduction—did not arise 

by gene duplication but were recently acquired by 

lateral gene transfer. In particular, transfer-messenger 

RNA—a bacterial RNA molecule with dual tRNAlike and mRNA-like properties—is emerging as a 

preferred integration site for the acquisition of new 

reductive dehalogenase genes.

Although a free-living microbe, the ecology of 

Dehalococcoides is firmly embedded into a complex 

metabolic network influenced by other community 

members. For example, the hydrogen substrate for 

organohalide respiration is provided in situ only via 

a complex anaerobic food web encompassing several 

other species. Moreover, Dehalococcoides cannot synthesize de novo the important cobalamine coenzyme 

of catabolic reductive dehalogenases, which thus 

must be acquired from members of the surrounding 

microbial community.

In noncontaminated environments, Dehalococcoides

species are extremely rare and postulated to exist by 

respiration of low-abundant organohalides produced 

at low rates by innate organisms. This combination 

of high niche specialization of low-abundant compounds is believed to account for the natural low 

abundance and low growth rate of this population in 

complex communities. Yet the target of environmental remediation engineering efforts is this important, 

niche-specialized activity of rare Dehalococcoides sp.

different materials and with the polymeric organic or inorganic materials to which the cells are interfaced. To follow 

material flow, concentrations of different substrates need 

to be mapped onto the cell population and the fluxes of 

various substrates through individual cells determined. 

Although such a dataset is important, acquiring it is well 

beyond our current technical capabilities.

Even within the field of genomics, which uses analytical 

technologies that are extremely sensitive, most measurements require large populations of cells. This size is necessary regardless of whether the purpose is to determine 

genome sequence or measure the products of genome 

expression (i.e., transcriptome or proteome). However, 

building accurate models of the collective behavior of 

cells requires the capability to measure properties and 

responses of the individual cells within a community. 

Important components of global approaches are those 

that illuminate the interface between cells and their 

environment and how the interface facilitates the flow of 

chemical and physical information (see sidebar,Uncovering Key Interdependent Relationships Among Organisms 

To Leverage Beneficial Capabilities, p. 11). Learning how 

individual cells regulate these processes and how they 

sense and respond to their environment represents a 

particular challenge. In addition, there is the need to 

identify and characterize critical molecular interactions, 

including those that constitute communication pathways 

between cells, attachments between cells and the extracellular matrix, and autocrine signaling such as quorum 

sensing. The types of information that need to be elucidated include the substrate(s) for each cell type, the 

product(s) it produces, and the spatial relationship both 

between the various cells consuming and producing 
11

2. Biological Challenges

October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems

Uncovering Key Interdependent Relationships Among Organisms 

To Leverage Beneficial Capabilities

Prior efforts and investments in characterizing 

genome sequences and the functional activities 

of gene products have focused primarily on the role of 

these molecules in the context of individual organisms. 

Increasingly apparent, however, is that an organism’s 

environment, including neighboring species, strongly 

influences how that organism uses its genetic-based 

information.

The relationship between plants and microbes exemplifies such a complex, multiorganism system, dependent 

on both the organisms involved and the environmental 

forces acting upon them (Bisseling, Dangl, and SchulzeLefert 2009). Understanding this relationship is critical 

to exploiting natural routes to environmental remediation processes, the terrestrial cycling of carbon, and the 

development and management of renewable energy 

sources (see figure, this page). Microorganisms are 

intimately associated with various plant tissues, producing short- and long-term effects on plant growth and 

development. Short-term effects are especially important for improving plant establishment on marginal 

soils. Microbes can accelerate plant root development, 

thereby providing the plant with better access to nutrients and water and reducing the need for irrigation and 

soil amendments. These better-developed root systems 

also increase the belowground flux of carbon. Exploiting this property can improve belowground storage of 

recalcitrant carbon forms and enhance soil quality for 

increasing biomass production in which more labile 

carbon is released. Additionally, microbes can facilitate 

fast initial growth of a particular plant, allowing it to 

out-compete others for available resources. Over the 

long term, microbes improve plant growth, health, 

and survival. They also can counteract stress responses 

caused by drought or the presence of contamination 

when plants are grown on marginal soils or are used for 

managing and remediating contaminated sites. Furthermore, microbes can protect a plant against pathogens 

and directly assist the host plant by producing antimicrobial compounds.

Effective extrapolation of such beneficial interactions 

requires understanding the relationships between the 

genetic characteristics of the organisms involved and 

how they proportion and exchange information, energy, 

and materials among themselves. Specific knowledge 

gaps that need to be addressed include:

•	 Determining the mechanisms for recognition and 

colonization of plants by beneficial microorganisms.

•	 Understanding the temporal and spatial variations in 

plant colonization by beneficial microorganisms.

•	 Determining microbial community structure in the 

rhizosphere.

•	 Establishing how important endophytic and 

rhizospheric microbes are in fostering carbon and 

nutrient sinks, and how the size and recalcitrance 

of these sinks might be influenced to affect belowground carbon biosequestration and aboveground 

biomass production.

•	 Determining how the observed microbial stimulation 

in plant growth and development might be controlled 

to tailor biomass composition to less-recalcitrant 

forms better suited for biofuel production.

Plant-Microbe Interactions.
12

2. Biological Challenges

New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

living there are perhaps not cultivatable. Thus, different strategies are needed for performing manipulative 

experiments. Along with patient and careful “conventional” approaches, new techniques are needed to 

capture as-yet unidentified metabolic capabilities for 

study under carefully defined conditions in the lab.

Other challenges include linking different types of 

data. As one example, how do we link genomic-based 

information on individual cells to different levels of 

molecular information, such as the transcriptome, 

proteome, metabolome, regulatory networks, and 

even the environmental niche? Another critical unanswered question is whether or how a cell’s genetic 

program is coordinated with that of neighboring 

organisms. We also do not know how the phenotype 

of a population arises from the functions of individual 

cell types. Some methods show promise for monitoring activity at the level of single microbial cells within 

complex mixtures; these methods include the use of 

stable isotope–labeled compounds in combination 

with nanoSIMS, microautoradiography–fluorescence 

in situ hybridization (MAR-FISH), and Raman-FISH 

(Neufeld, Wagner, and Murrell 2007). New methods 

are needed to accurately measure a greater variety of 

biomolecules—ranging from proteins, nucleic acids, 

carbohydrates, and lipids to low-molecular-weight 

metabolites for selected cells in the community or 

population. Single-cell transcriptomic, proteomic, 

and metabolomic measurements are needed, but 

whether such measurements are possible for most 

molecules is unclear. Nevertheless, it is essential to 

understand the flux of metabolites through particular 

metabolic pathways and the responses of regulatory 

networks in individual cells under relevant conditions. This information would move us away from the 

current approach of modeling populations of cells 

based on the potential of their genomes and into the 

area of modeling the “configuration” of individual 

cells within a given environment. Such capabilities 

would significantly advance the ability to translate 

genomic data into phenotype. In combination with 

improved capabilities for data integration, this would 

greatly enhance our ability to predict the response of 

heterogeneous biological communities.

This difference between the level at which we can make 

measurements and the level at which we need understanding is both a conceptual and a technical challenge.

Another particular challenge is the ability to dynamically monitor a selected individual living cell in a 

complex environment in the presence of other live 

cells. Electron microscopy and other current technologies exhibiting excellent spatial resolution often 

damage cells because they require special preparative 

techniques that “fix” and stain cells to maintain their 

structure under high vacuum and to provide contrast. 

To truly understand the fundamental mechanisms of 

cellular responses, noninvasive (or minimally invasive) 

methods such as fluorescence microscopy will be 

needed to measure metabolism and other key cellular 

properties in these selected cells within the population. These properties include expression of specific 

genes or networks of genes or proteins in a specific 

metabolic pathway. Because many cellular signaling 

and metabolic pathways act on time scales of milliseconds to seconds, there also is a critical need for 

single-cell measurement techniques with similar time 

resolution. Thus, techniques are needed not only for 

spatially resolving measurements in specific cells, but 

also for temporally resolving them.

Biologists are beginning to address the question of 

“who is present” using rapid and sensitive nucleic acid 

technologies to determine phylogeny, but measuring function is much more difficult and represents 

an emerging challenge. Current capabilities can 

provide insight into microbial community structure 

but cannot detect activity at the single-cell level. The 

traditional approach of measuring microbial activities under a narrow set of optimal abiotic conditions 

in the laboratory does not provide a sufficient basis 

for predicting community responses under the wide 

spatiotemporal variability of abiotic conditions in 

natural environments. Thus, approaches such as stable 

isotope probing are needed for detecting the microbes 

that are active (e.g., respiring, performing maintenance, producing proteins or other cell constituents, 

or replicating) under a wide range of conditions and 

revealing their functions in real time (Dumont and 

Murrell 2005). We also need to be able to enrich 

and isolate microbes involved in key processes in the 

environment, but the vast majority of microorganisms 
13

2. Biological Challenges

October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems

2.3 Understanding Dynamic Biological Systems 

Across Multiple Scales of Time and Distance

Understanding and modeling cell dynamics represent 

two of the most significant challenges for the postgenomic era. A current limitation to understanding biology 

is the tendency to model bioprocesses as hierarchical, 

linear responses rather than combinatorial, recursive networks. A major challenge associated with the 

increased throughput of molecular analysis is managing 

large sets of data and translating them into predictive 

models. New modeling approaches will be required 

to represent the nonlinearity and adaptive behavior 

of biological systems. These approaches could involve 

modeling the responses as assets of a biological network 

rather than simply the programmed responses of cells. 

To advance these efforts, new approaches should bring 

together tools from such diverse disciplines as engineering, biology, and computational sciences. Systems 

biology approaches could be used to address the 

multiscale nature of a phenotype and then be applied 

to understand how the behavior of multiple cell types 

is coordinated physiologically or in response to external stimuli (see sidebar, Working Toward Real-Time 

Measurements of Biological Responses: The Low Dose 

Radiation Challenge, this page).

Most biological problems are multiscale and thus 

complex. Understanding the nature of this complexity 

is critical because microscale biological and chemical 

interactions in large part are moderated by signaling between cells and organisms that, in turn, influence macroscale ecosystem compositional dynamics. 

There also is a need to adequately define “complexity,” 

especially as it pertains to microbes and interfaces 

or eukaryotic cells in their microenvironments (see 

sidebar, Targeting the Interfacial Region Between 

Microbial Cells and the Geosphere, pp. 14–15). 

Working Toward Real-Time Measurements of Biological Responses:

The Low Dose Radiation Challenge

Low dose radiation research seeks to determine 

when and why the physical attributes of radiation 

(i.e., dose and dose rate, volume, and quality) impact 

human health. The physical interactions of radiation 

energy with biological macromolecules are very well 

defined on the femto to millisecond time scales, and 

in the last decade so too are the immediate biological 

responses (i.e., damage recognition, repair, and cellfate determination). However, the ensuing biological 

changes lose definition over the course of days. How 

can detailed mechanistic information about energy 

deposition be used to identify significant cellular 

or molecular events? How can this information in 

populations be linked across cell types and long time 

scales? These low dose radiation problems reflect 

common issues of data integration and analysis that 

span Department of Energy research problems.

Understanding the biological bases for health 

effects caused by exposure to low doses of radiation 

requires interrogating multiple scales of information (from micro to macro) over sometimes very 

long time scales (from minutes to years). Non- or 

minimally invasive interrogation of multiple critical 

events in vivo is needed. A research framework might 

be initiated to a first approximation by analyzing 

fixed specimens across relevant time scales to generate a statistical model of the interactions between 

irradiated cells, tissues, and organs. The second 

generation might use complex in vitro models, but 

the ultimate goal is to analyze the evolution of interactions in an organism in real time.

Radiation exposure has a finite, dose-dependent 

probability of generating stochastic genetic change 

(e.g., mutation). However, the biological response to 

radiation is both nonlinear and deterministic, and it 

is the interaction of these two types of processes that 

can produce cancer. Thus, the fundamental problem 

is to identify and track rare events (aberrant cells) 

and characterize their dynamic interactions within 

a tissue environment. Besides the technical breakthroughs necessary for multiplex analyses at multiple 

scales, a critical need is to develop the computational 

models that identify watershed events, pathways, 

hubs, or processes and to mechanistically link them 

across time scales.
14

2. Biological Challenges

New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

Targeting the Interfacial Region Between Microbial Cells and the Geosphere

Microbial life has been closely intertwined with 

the geosphere throughout Earth’s history. 

Microorganisms, because of their small size, high 

ratio of surface area to volume, and incredibly diverse 

metabolism, have a tremendous influence on their 

environment through the transfer of energy and materials across complex biologic-solvent-solid interfaces. 

These microbes are agents of rock and mineral weathering as well as catalysts for their formation. Although 

the products of such microbial “sculpting” of the geosphere are often evident at large scales over the surface 

of the planet, the interplay between microbes and 

geological materials is dominated by processes at the 

molecular and microscopic scales (see figure, Interfaces Between Microbial Cells and Reactive Solids, 

from Landscape to Microscopic Scales, next page). 

The microbe-mineral interface is a prime example 

of this interplay and represents a complex, relatively 

unexplored domain that has important implications 

for carbon cycling and biosequestration, environmental remediation, and bioenergy. This interfacial region 

between microorganisms and minerals is dynamic, 

with chemistry and structure determined by interplay 

and response. The properties of reactive surfaces, cells, 

and adjacent regions alike are difficult to characterize 

because they are dynamic and occur at small (i.e., nm) 

scales. In 2000, the American Academy of Microbiology sponsored a colloquium, Geobiology: Exploring 

the Interface between the Biosphere and the Geosphere, and participants concluded that significant 

and critical events in geobiology happen at the level 

of individual cells or groups of cells. The ensuing 

report (Nealson and Ghiorse 2001) emphasized that 

the details of such processes would be revealed only 

by observations and measurements made at small 

scales—that of individual cells and their surrounding 

environment. This cell-scale research would seek to 

answer, for example, the following questions:

•	 How do microbial cells adjust their cell-surface 

molecular properties to facilitate the formation, dissolution, and weathering of minerals? Do cells similarly 

adjust their metabolism to alter the local chemical 

environment and thereby influence geobiological 

reactions? Do cells sense and respond to the precipitation of solids in association with their cell surfaces? If 

so, what are the environmental signals and the mechanisms of signal transduction?

•	 What are the mechanisms by which microbial cells 

catalyze CaCO3

 precipitation in advection-dominated 

systems?

•	 How do microorganisms engage with mineral 

surfaces and exchange electrons? How are electrons transferred across the outer membrane of 

gram-negative bacteria to minerals, metal ions, and 

electrodes?

•	 How do microscale biological and chemical interactions control and influence macroscale processes 

such as the formation and dissolution of minerals?
15

2. Biological Challenges

October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems

Interfaces Between Microbial Cells and Reactive Solids, from Landscape 

to Microscopic Scales. [Angel Terrace, Mammoth Hot Springs, Yellowstone 

National Park, USA. Images courtesy of Bruce Fouke.] 

(A) A travertine terracette formed as groundwater emerges at 73°C and 6 pH, 

rapidly cooling and then degassing CO2 to produce distinct covarying assemblages of microbial communities and CaCO3

 mineral precipitates. Aragonite 

(a polymorph of CaCO3) crystal growths composing the travertine grew at 

a rate of 5 mm/day to create the 1-m-thick travertine terracette in less than 

9 months. Controlled field experiments have demonstrated that microbial 

communities catalyze precipitation of the travertine CaCO3 (Kandianis et al. 

2008). [Image used with permission from the Society for Sedimentary Geology. From Veysey et al. 2008.]

(B) Field photograph of filamentous thermophilic microbes encrusted by 

CaCO3

 travertine precipitation. The spring water moves from right to left as 

a shallow (<3 cm deep) turbulent sheet flow that drives rapid CO2 degassing. The large (2 mm in diameter and 10 cm in length) individual microbial 

filaments are widely spaced and excrete draped sheets of extracellular polymeric substance (EPS) that contain abundant elliptical holes caused by water 

turbulence, gravity stretching, and gas-bubble release. The travertine directly 

encrusts and thus mimics the morphology of these filamentous microbial mats 

that then become well preserved in the geological record (Veysey et al. 2008). 

The 16S rRNA clone libraries, T-RFLP*, and metagenomic analyses indicate 

that these large filaments are Sulfurihydrogenibium.

(C) A microcomputed tomography scan (left) and a three-dimensional 

X-radiograph rendering of Sulfurihydrogenibium filaments (right). 

(D) A Sulfurihydrogenibium filament, captured by an environmental scanning electron microscope (ESEM), showing that it is composed of small microbial filaments interwoven to create a larger filament. 

(E) An ESEM micrograph showing aragonite (CaCO3) precipitation growing 

on a Sulfurihydrogenibium filament at 71°C and 6.2 pH in a field experiment. 

Larger aragonite crystals surround densely packed smaller crystals coating a 

bacterial filament; significantly larger crystals are precipitating on EPS, filling 

the void spaced between microbial filaments. These crystal fabrics are evidence 

of microbial catalysis of aragonite crystal growth. [Image used with permission 

from the Geological Society of America. From Kandianis et al. 2008.]

_____________

See also Fouke et al. 2000 and Fouke et al. 2003.

*Terminal restriction fragment length polymorphism.

A

C

B

D

E
16

2. Biological Challenges

New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

Related to these challenges are questions concerning 

the “reducibility” of complex systems. For example, 

can the complex behavior of an observed system (e.g., 

a microbial interface) be subdivided into functional 

modules that can be fully understood in terms of their 

interactions? Or are there synergistic effects that make 

the system irreducible? The reducibility of a system 

has direct implications for determining how to treat 

it (i.e., microscopically or macroscopically) and at 

which scale to measure it. Another question related to 

complex systems involves “downward causation.” Can 

global indicators and objectives (e.g., climate change, 

contaminant remediation, and biofuel production 

rate) influence the functional level from which the 

complex system must be understood? Knowing this is 

important for defining the level of granularity needed 

for constructing predictive systems-level models.

The success of new research to address these challenges will require integrated application of both 

reductionism and systems approaches. As a first 

step, meaningful systems-level interfaces need to be 

identified for study. One approach is to model cells 

as “input-output” systems in which the input of one 

or more pieces of information is translated into an 

appropriate cellular response. Currently, the most 

powerful (e.g., sensitive, specific, and diverse) methods to measure cellular responses are biochemical 

and molecular assays, whose use typically is practical only with relatively homogeneous populations 

of cells. Nevertheless, as technologies improve, data 

eventually will be available on systematic rather than 

representative responses of individual cells within 

a community. Incorporating these data will require 

building multiscale community models that will provide a framework for mapping the information derived 

from individual cells to the population’s behavior. 

These models should encompass dynamic modeling 

approaches that include multidimensional chemical and physical data generated by a variety of techniques—the various “omics,” microscopy, detection of 

activity, and detection of metabolic products. Community models should be capable of representing both 

extracellular and intracellular networks; predicting 

system stability under a range of conditions; highlighting areas of uncertainty; and elucidating feedbacks, 

thresholds, and nonlinearities.

Successful advancement of multiscale models for 

understanding biological systems will be facilitated by sophisticated new approaches to create 

model systems. Experimental design should include 

(1) observing, mapping, and characterizing molecular events across interfaces in the relevant natural 

environment; (2) controlled experimentation in the 

laboratory; and (3) controlled experimentation in the 

natural environment to test hypotheses derived from 

field mapping and lab experimentation. In addition, 

these controlled experimental systems must be able 

to incorporate advanced characterization technologies. Coupling these technologies with experimental 

systems typically has been achieved by developing 

some type of controlled experimental chamber. Such 

approaches involve moving the system and environment of interest to the analytical device. Determining 

how to conduct analyses in situ would be a significant 

advantage. Likewise, if data synthesis and modeling 

activities could be conducted simultaneously with 

experimentation and data collection, computational 

simulations could become a mechanism for informing and altering the ongoing experimentation rather 

than an exercise performed only after data have been 

collected and the experiment completed. For example, 

metabolic network modeling could exploit early 

metatranscriptomic experimental results showing the 

enzymes that are actually expressed by the system. 

This would narrow the scope of metabolic possibilities 

that otherwise would have to be established through 

labor-intensive experiments. However, certain aspects 

of biological systems and interfaces are currently only 

addressable by simulation and modeling. For example, 

water activity near a surface with cellulose or metal 

oxide crystals can be estimated best with molecular 

dynamic simulations. Thus, improved computational 

models and strategies are greatly needed for increasing 

the usability of this process for different problems.

Parameter-driven mathematical and physical models 

can describe many systems and processes, both natural 

and artificial. These models typically are used in a forward fashion, meaning that a certain set of values for 

model parameters yields a specific outcome. Defining 

an optimal outcome of the model is usually straightforward. A far more difficult, if not impossible, task 

is determining the corresponding parameter values 
17

2. Biological Challenges

October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems

Summary of Key Biological Challenges

2.1 Understanding the Cell and Its Response 

to Chemical and Physical Perturbations

•	 Complete the cellular “parts list.” Identify and 

functionally characterize the full suite of cellular 

constituents and determine how specific subsets 

mediate the energy and material flows within cells. 

•	 Measure cellular and subcellular environments. 

Comprehensively determine the physical and 

chemical factors that define the internal and external environments of individual cells. 

•	 Expand the functionality and availability of tools 

and reagents for live-cell imaging. Advance the 

development of nonperturbing tools and methods 

for simultaneous measurements of multiple factors affecting cellular activity.

2.2 Understanding Interactions Between Cells: 

From One to Many

•	 Characterize the activities of individual cells that 

collectively impact macroscopic phenomena. 

Identify cellular characteristics and microenvironmental conditions responsible for cell-to-cell 

variations observed in clonal and heterogeneous 

populations.

•	 Explore the interface between cells and their 

microenvironments. Investigate the spatial 

relationships, physical connections, and chemical 

exchanges that facilitate the flow of information 

and materials between cells. 

•	 Dynamically monitor selected individual cells 

within a population. Advance beyond average 

measurements of large populations of cells to targeted, real-time measurements of genome expression, metabolism, and other activities for selected 

cells under a wide range of conditions. 

2.3 Understanding Dynamic Biological Systems 

Across Multiple Scales of Time and Distance

•	 Develop new modeling approaches that address 

the multiscale nature of phenotype. Establish new 

methods for modeling the emergent properties of 

nonlinear biological systems that are influenced by 

interactions among smaller-scale system components as well as larger-scale external phenomena.

•	 Support new model-driven approaches to 

experimental design that integrate reductionist and 

whole-system investigations. Insights from global 

analyses of environmental samples could drive 

laboratory-based experimentation and modeling 

that generate hypotheses for controlled field-based 

studies in natural environments.

that, when applied to the model, yield the appropriate 

outcome. Frequently, more than one set of parameter 

values yields the same desired outcome, indicating 

that the model is degenerate. One way to determine 

optimal parameter values is analytically inverting the 

models. In many cases, this is analytically or practically infeasible because of model-related complexity 

and a high degree of nonlinearity (Fink 2008). It is 

unclear whether the optimal structure of a biological 

model can be defined for a given output, but such a 

development would transform this area of research. 

Understanding and modeling dynamic biological 

systems across multiple scales of time and distance 

will require the simultaneous development of new 

experimental approaches for real-time measurements 

of individual cells within their natural microenvironments as well as new modeling approaches that can 

handle the nonlinear and adaptive nature of biological systems. It will be particularly important to 

develop realistic models of biological pathways as an 

important link between molecular data and higherlevel biological functions.
18

2. Biological Challenges

New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009
October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems 19

questions concerning energy production, carbon 

cycling and biosequestration, and environmental remediation. Continued engagement among the physical, 

computational, and biological science communities—

as facilitated by DOE’s Genomic Science program—

will place these measurement challenges within reach 

and result in forthcoming revolutionary advances.

3.1 Expanding Global Characterization 

Capabilities

Genomics, transcriptomics, proteomics, and metabolomics (collectively described as “omics” analyses) are 

approaches used to identify and comprehensively measure the molecular species produced by an organism or 

community under defined environmental conditions 

at specific points in time. Global analyses of RNA 

transcripts, proteins, metabolites, and other cellular 

constituents provide insights into the functions and 

physiological status of a biological system and indicate 

which parts of the genome are activated and translated 

into functional molecules as organisms and communities develop or respond to their environments.

3.1.1 Genomics

Sequencing the entire genome of an organism—once 

only a dream among scientists—is now routine. By 

enabling us to sequence organisms at an astonishing 

rate and for an affordable cost, current technologies 

have transformed genomics into the most accessible 

global characterization modality. This accessibility 

has been achieved by the decreasing cost and increasing throughput of sequencing over the past decade, as 

highlighted in Table 3.1. Advances in Sequencing, p. 20. 

This trend does not appear to be slowing. In fact, new 

technologies are emerging from efforts to lower costs 

for sequencing the human genome to $1000 (Pushkarev, Neff, and Quake 2009).

High-throughput sequencing also allows microbial communities to be investigated en masse, thus 

giving rise to metagenomic analyses. DOE has 

played a critical and pioneering role in these studies, which are far beyond what, until only recently, 

was deemed possible. The newest generation of 

Technological developments are a hallmark 

of BER’s Genomic Science program (see 

Fig. 1.2. DOE Genomic Science Program, 

p. 3). Progress in developing analytical measurement 

techniques and imaging capabilities has enabled 

genomic-scale characterizations and detailed assessments of biological systems at the organism level. 

Continued advancements, however, are essential for 

addressing the biological challenges outlined in this 

report. To gain a predictive understanding of systems, 

information on a large number of different chemical 

and biological species must be measured and understood across diverse length and time scales. This challenge is further complicated by the need to conduct 

these measurements on heterogeneous mixtures of 

biological components in natural settings. Currently, 

many tools are unable to obtain the required chemical, physical, and biological measurements occurring within complex, heterogeneous environments. 

Other needed capabilities simply do not exist. Several 

significant technological advancements, therefore, are 

necessary to measure, mechanistically understand, 

and predict the response of individual cells and their 

ultimate impact on environmental systems. These 

advancements include the following:

1. Expanding and integrating global characterization 

capabilities.

2. Identifying and measuring rare events and rare 

molecular species and cells, such as those within 

complex, heterogeneous biological systems.

3. Simultaneously measuring many chemical and 

biological species across broad spatial and temporal ranges.

4. Integrating and interpreting diverse information to 

create mechanistic models.

The key technological capabilities described in this 

chapter are summarized on p. 38.

Developing these capabilities, in the context of the 

biological challenges described previously, will facilitate our understanding of the connection between the 

genome and natural environmental systems. In turn, 

this understanding will allow us to address pressing 

3. Technological and Capability Needs
20

3. Technological and Capability Needs

New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

technologies enables the sequencing of a significant 

fraction of genomes in a simple microbial community. However, more-complex communities and rare 

genomes within a community still are not amenable 

to systematic characterization. In addition, capturing 

metagenomic profiles at multiple time points during 

environmental transitions is critical for adequately 

monitoring the genomic changes associated with 

an environmental perturbation. As the wealth of 

omics data grows, better tools are needed for data 

management, analysis, and integration (see Fig. 3.1. 

Categories of Global Omics Measurements, below, 

and sidebar, Whole-Genome Amplification Holds 

Promise for Single-Cell Genome Drafting, p. 21). 

Although improvements in sequencing and analytical technologies can reveal the presence of lowabundance organisms, these capabilities need to be 

extended to the single-cell level. 

Table 3.1. Advances in Sequencing*

Platform

Million Base 

Pairs per 

Run

Cost per 

Base 

(U.S. ¢)

Average 

Read Length 

(Base Pairs)

Dye-terminator (ABI 3730xl) 0.07 0.1 700

454-Roche pyrosequencing 

(GS FLX titanium)

400 0.003 400

Illumina sequencing (GAii) 2000 0.0007 35

*From Hugenholtz and Tyson 2008. Reprinted with permission from 

Macmillan Publishers Ltd: Nature.

3.1.2 Transcriptomics

Although advances in DNA sequencing are impressive, 

they represent only the first step in global characterization of biological communities. A widely recognized 

need involves moving beyond potential functionality 

(DNA) to the world of actuation (RNAs, proteins, and 

small molecules). Global, high-throughput analyses 

of these molecules are conducted using ancillary 

techniques (metatranscriptomics, metaproteomics, 

and metabolomics) having well-known terminologies 

and concepts. None of these methods, however, is as 

universally applied and unbiased as genomics. Metatranscriptomics by de novo cDNA sequencing recently 

has been shown to be a viable approach to map both 

protein (Frias-Lopez et al. 2008) and functional RNA 

(Shi, Tyson, and DeLong 2009) expression. Another 

valuable advance would be single-cell transcriptomics, a method that could greatly improve our understanding of biological variability. Improvements in 

microfluidics-based sample processing 

eventually could lead to such measurements. As costs continue to fall, de novo

sequencing likely will replace microarray 

approaches because of its high reproducibility, discovery potential, and elimination 

of the need to generate organism-specific 

microarrays. Regardless of approach, these 

applications currently face several challenges, including the development of an 

amplification method, the ability to detect 

Fig. 3.1. Categories of Global Omics Measurements. A cell has one genome and a dynamic transcriptome and 

proteome that all impact its metabolome. Metagenomic studies are global characterizations of heterogeneous microbial 

communities. Currently, our characterization capabilities decrease during the transition from genome to metabolome. 
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3. Technological and Capability Needs

October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems

small regulatory RNAs such as miRNAs or nanoRNAs, 

and finer-scale measurements (see Section 3.1.6, From 

Bulk to Finer-Scale Characterization, p. 23).

3.1.3 Proteomics

Significant technical advancements in proteomic measurements have been achieved during the last decade. 

New generations of mass spectrometers and advanced 

separation technologies now allow the measuring of 

thousands of proteins (the product of mRNAs) in a 

population of cells and hundreds of post-translational 

modifications. Applying these techniques to microbial 

communities (metaproteomics) has provided promising and unexpectedly high resolution results, such as 

the ability to discriminate proteins from orthologous 

genes belonging to closely related strains (VerBerkmoes 

et al. 2009). However, current technologies are resource 

intensive and still do not offer a complete characterization of the proteome. Membrane proteins are particularly difficult to measure, in part because of their 

resistance to most proteases used in sample preparation. 

Until rare proteins can be measured, proteomics will 

continue to provide an incomplete picture of the cellular 

proteome. Proteomic analyses of environmental samples 

also have been constrained by challenges in extracting 

and purifying proteins from their environmental matrices. Peptide identification for proteins from complex 

mixed microbial communities presents another challenge because most members of such communities have 

yet to be identified and included in genomic databases. 

Further development of informatics approaches thus 

is required for identifying the function and origin of 

Whole-Genome Amplification Holds Promise for Single-Cell Genome Drafting

The Great Flat Hope: Effect of Normalization of MDA Product from a Single Bacterial Cell. Without normalization, genome coverage is highly skewed with peaks having >500× coverage and the average coverage being <1. 

After normalization, peaks are suppressed and the average coverage rises to ~60× for the same amount of sequencing. This greatly facilitates de novo assembly of the sequence data. [Plots courtesy of Tanja Woyke, DOE Joint 

Genome Institute.]

Finer-scale sampling of microbial communities 

is essential to bring sample size in line with the 

size of the organisms being studied. However, reducing sample size unavoidably limits the amount of 

macromolecules available for analysis. In the case of 

nucleic acids, whole-genome amplification (WGA) 

methods provide the microgram quantities of DNA 

needed for sequencing from as little as femtogram 

quantities of starting material (the amount of DNA 

in an average microbial cell). There are several WGA 

approaches based on polymerase chain reaction 

(PCR), but unfortunately all result in incomplete 

coverage of the genomic template and are susceptible 

to biases. Ideally, WGA products should be random, 

complete representations of the starting material. 

Approaching this ideal are methods not based on 

PCR, including multiple displacement amplification (MDA). In practice, MDA produces a skewed 

coverage of DNA templates, particularly from very 

small amounts of starting template. Postamplification normalization using duplex-specific nuclease 

(DSN) appears to be the most promising approach 

to correcting biased coverage. Denatured MDAs are 

allowed to partially renature, with the most common 

(over-represented) regions of the genome renaturing first. DSN then is used to destroy the common 

double-stranded DNA, thus normalizing the MDA. 

The net result is a more complete coverage of the 

target genome, even from single-cell templates. With 

such capabilities, we may be on the verge of producing cost-effective, high-quality draft genomes from 

single cells.
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3. Technological and Capability Needs

New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

proteins in uncultured microbial taxa. Other urgent 

needs include technology developments to increase the 

speed, sensitivity, quantification, and dynamic range of 

proteomic measurements and approaches to decrease 

the bias in protein coverage. Also needed are new innovative techniques that can provide additional insight into 

function, such as activity-based proteomic profiling.

3.1.4 Metabolomics

Although perhaps the oldest of the global omics technologies, metabolomics is the least developed, partially because of the large diversity of small molecules 

present in cells. Other challenges are the inability of 

current technologies to provide information on the 

spatial and temporal dynamics of metabolites and 

the problems in interpreting metabolomic data. For 

example, significant differences have been observed 

in the metabolomes of identical-appearing cells following specific environmental perturbations. The 

source of this variation is unclear. Further complicating efforts is the fact that only a small fraction of the 

metabolome can be characterized despite advances in 

mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy over the past 20 years. 

This limitation largely results from the lack of robust 

techniques for structurally characterizing metabolites 

identified by MS and other technologies. However, 

inadequate measurement sensitivity also constrains 

metabolomic characterization. For example, as a sample size decreases from tens of cells to even a single 

cell, the metabolomic depth of coverage decreases 

to only a few of the most abundant metabolites. 

However, the power of metabolomic measurements 

could be increased significantly by new approaches 

for combining multiple characterization techniques 

(e.g., NMR and MS) and the construction of specific 

metabolite libraries.

Measuring metabolic flux networks is critical for understanding and manipulating metabolic phenotypes in 

plants (Ratcliffe and Shachar-Hill 2006) and microbes 

(Feng et al. 2009). By analyzing the mass spectra of 

different fragmentations in proteogenic amino acids 

derived from various pathways, the active fluxes of 

intracellular biosynthetic pathways can be determined. 

Similar approaches allow estimates for small metabolites but are based on static measurements of low 

concentrations. While this approach is reasonably well 

established for microorganisms, several challenges must 

be addressed in applications for plants. These include 

(1) improving data collection by conducting moresensitive NMR and MS analyses in conjunction with 

new subcellular fractionation approaches, (2) accelerating flux analysis so the method can be high throughput, 

(3) dealing with dynamic range issues, and (4) developing fully predictive metabolic network models rather 

than simply generating flux maps for the specific system 

and condition of interest (Kruger and Ratcliffe 2009).

Global characterization techniques are poorly developed for several molecular classes. For example, globally 

elucidating the identity and structure of unknown 

carbohydrates and glycosylated proteins (glycomics) 

is currently impossible, and even though technologies 

now allow lipid characterizations, they do not provide 

comprehensive information on lipid content. Therefore, developing global characterization technologies 

for these classes of molecules is important. Lipids and 

carbohydrates play crucial roles in cell-environment 

interactions and are essential components of membranes, driving the complex chemistry occurring within 

and near them. Without understanding the changing 

lipids and carbohydrates in a membrane, our knowledge 

and progress will lag.

3.1.5 From Snapshot to Spatial and Temporal 

Sample Series

Due to the high cost of molecular characterization, 

the majority of functional genomic studies to date 

have been snapshots of microbial communities. Just as 

watching a film is more informative than looking at a 

single frame from that film, kinetic series of molecular 

data hold great promise for advancing the understanding of biological systems. Because of the falling cost and 

increasing throughput in sequencing, the production 

of temporal and spatial molecular data series is now 

becoming possible. An example of research being conducted in this area is community profiling that allows 

comparison of hundreds of microbial samples using 

conserved marker genes, notably ribosomal RNA genes 

(Tringe and Hugenholtz 2008). Kinetic data are particularly informative if the sampling time is short relative to the characteristic time of the process of interest. 

For fast processes, such as stress responses or metabolite turnover, producing a data series could require 

very rapid sampling, which necessitates technological 
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3. Technological and Capability Needs

October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems

advances in sample processing and increases in sample 

throughput. Multiple samples also can provide information on spatial patterns of gene and protein expression, 

particularly if taken across conspicuous physicochemical gradients. Since increasing the resolution of spatial 

sampling decreases sample size, greater sensitivity is 

required for the global measurement technology being 

used. A significant advantage of genomics technologies 

for characterizing multiple samples is that the changing 

composition of a cell community can be inferred from 

the resulting data.

3.1.6 From Bulk to Finer-Scale Characterization

For all omic methods, a major challenge is fine-scale 

resolution. In characterizations of microbial communities, gram or milliliter quantities of an environmental sample typically are used as starting points. At the 

organism scale, this is akin to trying to determine the 

global function of a rainforest community by blending 

together species occupying the forest floor, understory, canopy, and emergent layers and then trying to 

piece together functionality based on the combined 

data. Analysis and interpretation could be greatly 

enhanced if cell populations could be separated into 

more-refined spatial niches (Bergeron et al. 2007), 

particularly across distinct physicochemical gradients 

(e.g., Kunin et al. 2008). Although microscopic imaging can help delineate meaningful spatial niches in 

microbial communities, it thus far has not been used 

extensively to guide molecular characterization.

The finest scale for community characterization is at 

the level of individual cells. Significant inroads have 

been made in applying molecular technologies at this 

scale, largely because of whole-genome amplification 

technologies. Unfortunately, this technique requires 

physically separating a cell from its ecosystem and 

destroying it to obtain its molecular information. 

Though these are acceptable technical caveats for 

DNA-level characterization, for molecules strongly 

modulated by the cellular environment (RNA, protein, and metabolites), the isolation and destruction 

processes could alter the observed molecules significantly. Changes in expression profiles can occur very 

quickly (often on the millisecond scale), and these 

fluxes thus are unlikely to be representative of the cell 

in its native setting. Although currently intractable, 

the ability to characterize and relate transcriptome, 

proteome, and metabolome fluxes in selected single 

cells in terra remains an extremely important area for 

technology development in the coming decade.

3.2 Identifying and Measuring Rare Events, 

Molecules, and Cells Within Complex, 

Heterogeneous Biological Systems 

Many of the biological issues relevant to DOE science 

missions—such as contaminant fate and transport, 

bioenergy, carbon cycling and biosequestration, and 

low dose radiation responses—require understanding a complex web of interactions. These interactions 

and responses occur between biomolecules within 

cells, between individual cells, between populations 

of cells, and between communities of cells and their 

environment. In many instances, the interactions 

are associated with rare events or with numerically 

rare (low-abundance) molecules and cell types (see 

sidebar, The Value of Understanding Rare Events 

and Cells, p. 24). In cell biology, single-cell dynamic 

measurements have been used to detect rare signaling 

events with important biological consequences that 

likely would have been missed or averaged out using 

bulk measurements (Faley et al. 2008).

A current challenge involves determining how to 

investigate the physiology of rare microbes within 

populations that cannot be readily cultivated. New 

technologies are needed to quantitatively analyze the 

physiology and genomics of low-abundance cells. For 

example, state-of-the-art cell-sorting capabilities, using 

innovative microfluidics platforms, could be coupled 

with sensitive instrumentation to measure individual 

rare cells. Although in their infancy, single-cell measurements already are making significant contributions 

to microbial biology (Behrens et al. 2008; Chan et al. 

2007; Yu et al. 2006; Elf, Li, and Xie 2007; Huang et 

al. 2007). Additional technical advances in this area 

undoubtedly would have tremendous scientific impact 

by enabling scientists to simultaneously determine the 

identity of cells and measure metabolites, transcripts, 

proteins, and signaling molecules at sufficient temporal 

resolution to capture their dynamics. For example, ELFISH nanoSIMS can be used to simultaneously image 

physiological processes and phylogeny at single-cell 

resolution in microbial communities (see sidebar, Investigating Rare Microbes’ Function and Phylogeny at the 
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3. Technological and Capability Needs

New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

The Value of Understanding Rare Events and Cells

Single-Cell Level Using EL-FISH NanoSIMs, p. 25 ). 

Ambient ionization mass spectrometry that involves 

ionization of untreated samples, such as microorganisms or plant and animal tissue in the ambient environment, also may be useful for resolving molecules and 

processes at the single-cell level. Moreover, identifying 

rare events at the single-cell level will require developing new tags and affinity reagents with high specificity 

and more-sensitive detection methods. Functionalized 

nanoparticles (e.g., TiO2

) show considerable promise 

for such applications (Rozhkova et al. 2009). In combination with new high-resolution imaging techniques 

like computed tomography, X-ray nanoprobes, and spin 

imaging, detection of rare events at the nanoscale can 

be achieved.

The effectiveness of environmental remediation can 

be dependent on the activities of rare, low-abundance 

organisms. For example, Dehalococcoides sp.—a lowabundance, slow-growing microbial species—is a primary agent in the remediation of chlorinated solvents. 

To enable the long-term management of slow-growing, 

rare populations in complex communities, we need 

the ability to measure their physiology and functional 

genomics in natural systems. We do not know whether 

the rare organisms found at environmental remediation 

sites are present because of recent adaptive evolution or 

because of sudden, favorable growth conditions due to 

the release of the environmental contaminant. To what 

extent is evolution (horizontal gene transfer, mutations, 

recombination) occurring in situ and resembling the 

Below are two examples of situations involving rare events or organisms with significant 

biological implications. The first is a rare event leading to DNA mutations and cancer, and the second 

is a low-abundance (rare) population of microbial 

cells. Though rare, the importance of both warrants 

further characterization.

In the first case, given the radiation biology paradigm 

of mutation driving malignancy, radiation exposure 

has a finite, dose-dependent probability of generating 

stochastic genetic change. The biological response to 

radiation, however, is both nonlinear and deterministic, and it is the interaction of these two types of 

responses that can produce cancer. Thus, a fundamental challenge involves identifying and tracking 

rare events (mutant cells) and characterizing their 

dynamic interactions with a heterogeneous environment. Breakthroughs could be achieved with new 

tools and technologies that would enable non- or 

minimally invasive interrogation of multiple critical 

events in vivo, leading to the ultimate goal of analyzing the evolution of interactions within an organism 

in real time. Also critical is the development of computational models that identify “watershed events” 

and mechanistically predict their functional outcome 

over long time scales.

Second, low-abundance (rare) populations can 

account for significant amounts of phylogenetic 

diversity of microorganisms observed in many 

environments. By using massively parallel sequencing approaches, such populations have now been 

identified in a variety of environments, but their 

physiological and genomic characterization remains 

challenging. Although cell culture has enabled the 

characterization of some rare microbes and metagenomics is providing glimpses into their possible 

functions, the majority of these organisms are yet 

uncharacterized. New tools and approaches are 

needed to identify which populations are contributing to a particular process of interest. For environmentally relevant microorganisms, determining 

the distinctiveness of “isogenic” populations is 

important. Also critical is establishing the extent of 

variation (e.g., epigenetics, bistability, and persister 

cells) within the population to determine the mechanism of phenotypic heterogeneity within it. A poor 

understanding of the extent of genetic and physiologic diversity within populations prevents us from 

determining the number of individual cells that must 

be sampled and analyzed to gain insights into the 

functional diversity of complex populations, communities, and ecosystems. Current data suggest that 

such diversity is extensive in many environments.
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3. Technological and Capability Needs

October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems

Two fundamental technological challenges in 

biological characterization 

are identifying rare, uncultured microorganisms and 

determining their ecological 

function and their adaptive 

and evolutionary potential. 

Because they are rare, physiological and phylogenetic 

information for such microbes 

likely is limited. A crucial 

first step, therefore, is obtaining some insights into these 

microbes’ ecological function 

in their current environment.

Single-cell sorting by 

microfluidics and subsequent single-cell genome 

amplification and sequencing comprise a promising 

technology platform to 

study uncultured microbes. 

Currently, this method 

relies predominantly on 

visual selection of the cell of 

interest, which can be accomplished with fluorescence in situ hybridization (FISH). However, at 

present, no physiological or metabolic property 

can be considered in the cell-sorting and isolation 

process. Recent progress in nanoSIMS technology 

has enabled scientists to link microbial phylogeny 

to function while maintaining a structured environment. This technique, which combines nanoSIMS 

and elemental labeling–FISH (EL-FISH), might 

provide a basis for one path forward. NanoSIMS is 

emerging as a powerful technique to image biological systems’ elemental composition, including 

stable isotope distribution, at 50-nanometer resolution. EL-FISH is an advancement of catalyzed 

reporter deposition (CARD)-FISH and exploits 

the chemical coupling of a specific element or 

isotope to a specific phylogenetic oligonucleotide 

EL-FISH nanoSIMS Analysis of a Microbial Consortium. Fluorescence 

and nanoSIMS images show a microbial consortium consisting of filamentous 

cyanobacteria (Anabaena sp. strain SSM-00) and α-proteobacteria (Rhizobium sp. strain WH2K) attached to heterocysts. Images taken after a 24-hour 

incubation with 13C-bicarbonate and 15N-dinitrogen. (A) Fluorescence image of 

the microbial consortium after EL-FISH with probe ALF968. (B) NanoSIMS 

secondary-electron image corresponding to panels C to E. (C) Localization of 

fluorine relative to carbon after EL-FISH with ALF968. (D) Distribution of 15N-nitrogen enrichment. (E) Distribution of 13C-carbon enrichment. Color bars 

indicate relative fluorine abundance (C) and isotope enrichment (D and E) in 

the image. [Key: Het, heterocyst; Veg, vegetative cell; Epi, epibiont; unatt Epi, 

Epibiont cells not attached to heterocysts. Image used with permission from the 

American Society of Microbiology. From Behrens et al. 2008.]

probe; the isotope then can be subsequently visualized by nanoSIMS at high spatial resolution. 

Thus, EL-FISH nanoSIMS simultaneously images 

physiological processes and phylogenetic identity 

at single-cell resolution within microbial communities while maintaining a structured environment. This technique has been used to identify 

the metabolic basis of a specific interaction of an 

α-proteobacterial epibiont with the heterocyst of 

an Anabaena species (see figure above). To functionally examine rare, individual microorganisms 

in a complex environment, advances are needed 

in nanoSIMS, EL-FISH, and EL-FISH nanoSIMS. 

Such advances should enable high-throughput, 

semiautomated analyses of microbial samples from 

diverse environments.

Investigating Rare Microbes’ Function and Phylogeny at the Single-Cell Level 

Using EL-FISH NanoSIMS
26

3. Technological and Capability Needs

New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

rate-limiting step for biotransformation? To answer 

these and other questions, new capabilities are required 

for detecting low-abundance microbes and genomes 

and for measuring their in situ activities—analogous to 

finding a needle in a hay stack. Tools also are needed for 

studying the biology of slow-growing microbes at the 

single-cell level and for taking rapid metabolomic and 

genomic measurements of microbial communities, ideally with high spatial and temporal resolutions. Imaging 

methodologies are the most powerful current technologies for identifying rare cell types in situ, but they still 

are limited in throughput and the types of parameters 

they can detect. Improvements in the speed, sensitivity, 

and automation of cell imaging are urgently needed, 

as are significant advancements in detection technologies. If available, these tools would greatly facilitate 

the monitoring of in situ ecological and evolutionary 

dynamics and provide essential data for constructing 

predictive models.

To develop a predictive, mechanistic understanding of 

plant and microbial processes using systems biology 

approaches, performing noninvasive or minimally 

invasive molecular measurements within individual, 

intact cells is critical. Even within clonal populations 

of Escherichia coli grown under highly controlled cultivation conditions, there is considerable cell-to-cell 

heterogeneity in gene expression because of both the 

inherent stochasticity in expression and the fluctuations in other cell components (Elowitz et al. 2002; 

Choi et al. 2008; see sidebar, Single-Molecule Measurements Reveal Phenotype Switching in Genetically 

Identical Cells, p. 27). High-throughput analytical 

procedures currently use extracts from large populations of cells to measure proteins, mRNA, metabolites, 

signaling molecules, and other types of biomolecules 

in a population of cells. These approaches provide, at 

best, an average over the entire population and thus 

are unlikely to detect rare events within a population 

or different responses of subpopulations of cells. Nevertheless, understanding the role of stochastic events 

is a challenge that must be addressed.

Another related challenge is the observation of lowlevel events within individual cells. For example, 

receptors within many cell types operate effectively 

when only a small fraction of them are activated. This 

sensitivity allows cells to detect changes in only a 

few molecules in the environment. Although initial 

detection events might generate extremely small biochemical signals, they eventually are amplified by the 

cellular machinery into physiological responses. However, unless initial detection events are observable, 

understanding the resultant response mechanisms 

can be extremely difficult. The biochemical processes 

regulating many cellular responses, such as transcription, chemoattraction, and stress adaptation, are 

usually below the limit of detectability using current 

technologies. Urgent needs, therefore, include novel 

approaches for amplifying the biochemical signature 

of these processes in situ, coupled to more-sensitive 

detection technologies.

3.3 Seeing It All: Simultaneous Measurements 

Across Multiple Dimensions

Addressing the critical challenges faced by genomicsbased science will require continued progress in and 

new approaches to applying and developing analytical 

technologies. As highlighted earlier, identifying and 

tracking molecular species and processes within and 

between cells present unique measurement challenges 

because of the multitude of complex interactions and 

dimensions involved. Not only is there a large diversity in the types of molecules present, the spatial and 

temporal dynamics of these molecules often dictate 

their function.

Molecular interactions can extend over many orders 

of magnitude in length scales—from the subcellular 

(nanometers) to the cellular (microns) to biofilms 

and plant leaves and roots (milli- to centimeter) and 

even to larger organisms and ecological communities 

(meter to tens of meters). The relevant time scales of 

these interactions also can extend from milliseconds 

for enzymatic reactions to the multiyear responses 

seen in carcinogenesis or microbial community evolution. Investigating systems-level processes requires 

decisions on the types of information to be collected 

as well as the granularity of sampling in time and 

space. Enormous difficulties lie in extending the global 

characterization approaches described in Section 3.1, 

p. 19, to the tracking of molecules across many cells, 

across interfaces to disparate materials and environments, and throughout an ecosystem. However, an 

important objective is to understand how microscale 
27

3. Technological and Capability Needs

October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems

Genetically identical cells in the same environment 

can exhibit different phenotypes, and a single cell 

can switch between distinct phenotypes in a stochastic 

manner. In the classic example of lactose metabolism in 

Escherichia coli, the lac genes are fully expressed for every 

cell in a population under high extracellular concentrations of inducers, such as the lactose analog TMG. However, at moderate inducer concentrations, the lac genes are 

highly expressed in only a fraction of a population, which 

may confer a fitness advantage for the entire population. 

Recent advances in single-molecule imaging have revealed 

the detailed molecular mechanism controlling the stochastic phenotype switching of a single cell.

Lactose metabolism is controlled by the lac operon, which 

consists of three genes including lactose permease. Expression of the operon is regulated by the lactose repressor that 

dissociates from its specific binding sequences 

of DNA, the lac operators, in the presence of 

the inducer to allow transcription. The production of the permease increases inducer influx, 

resulting in positive feedback on expression of 

the lac operon. When the permease is labeled 

with a yellow fluorescent protein, two different 

phenotypes are observed in an isogenic population of cells, with the fluorescence intensity 

histogram of the cells exhibiting a bimodal 

distribution (see A and B portions of figure at 

right). Interestingly, the low-fluorescence cells 

have a few individual permease molecules, 

suggesting that one permease molecule is not 

enough to induce the transition from the lowto high-fluorescence phenotype. Scientists 

have determined that 200 to 300 permease 

molecules, corresponding to a big burst of gene 

expression, are needed for this transition.

The tetrameric lactose repressor simultaneously can bind to two operators to form a 

DNA loop. Under low inducer concentrations, 

the repressor cannot be pulled off the DNA 

by the inducer. Rather, spontaneous, partial 

dissociations of the repressor result in transcription of one mRNA and a small burst of 

proteins. However, infrequent events of complete dissociation of the repressor result in large bursts of permease 

expression (200 to 300 copies) that trigger induction of 

the lac operon. This change in phenotype is the result of 

the stochastic, full dissociation of the tetrameric repressor 

from all of its binding sites. The time-lapse sequence (see 

C in figure) captures such a phenotype-switching event. 

This illustrates that a stochastic, single-molecule event 

of the complete dissociation of the tetrameric repressor 

determines a cell’s phenotype, and that a DNA loop is 

crucial in maintaining phenotype stability. This is a clear 

example that a rare event of a single molecule can have 

significant biological consequences.

__________

*From Choi et al. 2008. Portions of text and figure reprinted 

with permission from AAAS.

Single-Molecule Measurements Reveal Phenotype Switching

in Genetically Identical Cells*

Expression of Lactose Permease in E. coli. (A) In the presence of moderate amounts of inducer (lactose analog TMG), an E. coli strain expressing 

lactose permease fused to yellow fluorescent protein (LacY-YFP) exhibits 

two phenotypes, all-or-none fluorescence in a fluorescence-phase contrast 

overlay image. Fluorescence imaging with high sensitivity reveals single 

molecules of LacY-YFP in the uninduced cells. (B) Bimodal fluorescence distributions show that the cells exist either in an uninduced or induced state, 

with the relative fractions depending on the inducer concentration. (C) A 

time-lapse sequence captures a phenotype-switching event. One cell switches 

phenotype to express many LacY-YFP molecules (yellow fluorescence overlay), but the other cells do not.
28

3. Technological and Capability Needs

New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

biological and chemical interactions control and 

influence the dynamics of ecosystem composition on 

the macroscale. To realize this goal, characterization 

tools must be able to measure and monitor an array of 

molecular details across diverse length and time scales.

Approaches are needed that combine multiple techniques to map structural, chemical, and physiological 

changes in biological systems as functions of external 

stress and environmental conditions. For example, 

coupling multiple spectroscopic approaches can enable 

characterization of responses to environmental stress, 

including elevated temperatures, pressures, and chemical treatment. Measurements using optical, X-ray, 

infrared, and neutron scattering can enable structural, 

functional, and chemical analysis of molecular complexes and assemblies under differing environmental 

conditions. Such a multiplexed measurement platform 

could allow the tracking, visualization, and interpretation of real-time changes in microstructure assembly 

and organization—initially in vitro, then eventually 

in situ and in vivo. Also needed are new techniques 

for interrogating biological interactions on time and 

distance scales ranging from the level of biomolecular 

interactions (nanometer and picosecond scales) to 

intercellular communication (millimeter to centimeter 

and second to minute scales). To achieve these ambitious goals, advances are needed in imaging techniques, 

chemical probes (contrasting agents), and computational methods.

The ability to see it all requires characterizing the 

distribution of specific proteins, nucleotide sequences, 

lipids, polysaccharides, and other cellular components 

(see Fig. 3.2. Examples of Characterization Techniques 

and Their Ranges of Resolving Power, p. 29). Several 

promising advances include recent light microscopy 

techniques allowing the identification of microbial 

proteins (see sidebar, Super-Resolution Light Microscopies Enable the Imaging, Counting, and Localization of Single Proteins in Microbes, p. 30). At the 

most fundamental level, knowing the elemental composition of individual cells is important. Also critical is 

knowing the relevant chemical form over time. These 

types of information remain difficult to obtain even 

in controlled laboratory situations. The most general 

ways to assess elemental distributions in samples are 

X-ray absorption or fluorescence and in the case of 

imaging modalities, X-ray microscopy or tomography. These techniques can be used to characterize the 

distribution of nearly every element, from beryllium 

to plutonium. If the chemistry of particular elements 

is important, then different spectral techniques can 

provide this information. These technologies typically 

require the brightest sources of tunable X-rays, and 

thus synchrotron radiation sources are where advances 

in these approaches are likely to be made. Extending 

such measurements to field studies presents a considerable challenge.

One of the most powerful ways to characterize a complex biological system is to acquire multiple types of 

information about cell functioning at different spatial 

and temporal scales. Biological systems have evolved 

elegant pathways, molecular structures, and complex 

machinery at the nanoscale to power sophisticated cell 

functioning. However, current tools usually do not 

measure these entities directly but infer their presence 

using prior knowledge of their presence. Manipulating a living organism’s biomolecules, supramolecular 

entities, and cell metabolites can provide important 

insights into their complex functioning. These types 

of manipulations also require extensive prior knowledge about the physical and chemical properties 

of molecules and assemblies within the cell. Thus, 

investigations of complex systems usually need to be 

preceded by extensive characterization. Development 

of unbiased analytical approaches that do not require 

prior knowledge of cell composition or function 

would greatly accelerate the investigation of previously 

unknown and uncharacterized biological systems.

The most important aspects of analytical measurement systems are those related to information content 

and resolution (whether spatial, chemical, or temporal) and those related to sensitivity, detection limit, 

and dynamic range. Unfortunately, these two important categories usually are in opposition to each other. 

For example, spatial resolution comes at the expense 

of temporal range and vice versa. As a result, imaging tools that are modified to work at smaller-length 

scales gather less chemical information or sacrifice 

dynamic range. This frustrates the study of important 

events, such as the cellular interface, which require 

sensitivity and resolution at the molecular scale but 

also a spatial range that allows coverage within a large 
29

3. Technological and Capability Needs

October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems

area. Illustrating this challenge is the light microscope through which observing a wide area requires 

a low-resolution objective with a considerable field of 

view. Detailed studies require a high-power objective, which intrinsically has a narrow field of view. 

Although one cannot simultaneously have it both 

ways, multiple objectives can be used sequentially, 

followed by multiscale integration. Alternatively, a 

plethora of measurements can be made using a highpowered objective followed by reconstruction of a 

wide field of view by image fusion. However, analyzing complex systems by sequential analysis creates an 

additional challenge with respect to observing living 

systems in real time. Perturbations to the system by 

measurement technologies are potentially disruptive 

to system functions and can generate unanticipated 

outcomes and prevent sequential analyses. Therefore, 

measurement technologies should be noninvasive 

or minimally invasive, allowing the system under 

study to progress and then be observed over time. 

For example, such techniques would benefit investigations of molecule and solute transport in plant 

cells, which are extremely sensitive to environmental 

perturbations (see sidebar, Challenges for Imaging 

Live Plant Cells, p. 31).

Fig. 3.2. Examples of Characterization Techniques and Their Ranges of Resolving Power. The interactions 

of different chemical and biological species must be measured and understood across diverse spatial scales—from the 

environment and community levels to the cellular, subcellular, and molecular levels. Shown in the figure is a sample 

of different functional levels and characteristic sizes of representative components. Also listed are some of the analytical measurement tools that operate at these different length scales (also see sidebar, p. 30). A major need is to connect 

measurements and information at these different length scales. The types and variety of information involved complicate facile collection of relevant data, as does the need to monitor temporal changes in these systems. Typically, current 

tools are appropriate for providing characterizations at only specific spatial and temporal scales, or they are limited in 

the number or type of information that can be measured. Multiple “dimensions” need to be added to biological measurements so that molecular events can be linked to cellular, multicellular, and environmental scales.
30

3. Technological and Capability Needs

New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

Over the past 3 years, a revolution in light microscopy has emerged with the potential to transform our understanding of microbial function. Prior 

to 2006, light microscopy largely was restricted to 

resolutions of about 200 nanometers by the diffraction 

limit, although some technologies already had begun 

to breach this limit, including stimulated emission 

depletion (STED; Hell 2003; Shaner, Patterson, and 

Davidson 2007) and structured-illumination microscopy (Gustafsson 2005). This meant the main tools 

for studying the mesoscopic (2 to 200 nanometers) 

organization of microbes were electron microscopy 

(EM) and atomic force microscopy (AFM). In 2006, 

three new techniques were reported: photoactivated 

localization microscopy (PALM; Betzing et al. 2006), 

fluorescence PALM (Hess, Girirajan, and Mason 

2006), and stochastic optical reconstruction microscopy (STORM; Rust, Bates, and Zhuang 2006). These 

techniques allow single proteins to be visualized, 

counted, and localized with nanometer precision even 

when they are very densely packed (tens of thousands 

of tagged proteins per square micron). PALM and 

STORM—especially when used in conjunction with 

STED, EM, and AFM—may allow creation of comprehensive, nanometer-precision atlases of prokaryotic 

proteins and protein complexes. 

In PALM, target proteins are genetically labeled with 

photoactivatable proteins, thus rendering them nonfluorescent until activated by near-UV light. By using 

near-UV light of sufficiently low intensity, only one protein per diffraction-limited region (~250 nanometers) 

is activated at a time. Following activation, each protein 

is then excited and imaged. Since only one protein is 

imaged at a time in each diffraction-limited region, the 

center of each molecular point spread function indicates 

the location of each protein. Serial cycles of activation 

and excitation are repeated until all fusion proteins are 

bleached. Because individual proteins are imaged, these 

techniques enable researchers to count the proteins 

and computationally assemble their locations into 

a composite, high-resolution image. The location of 

each protein can be determined to a precision of 2 to 

25 nanometers, or ~10 to 100 times better than the 

diffraction limit. The localization error for each protein 

depends on the number of photons collected for that 

protein as well as background noise, pixel size, sample 

drift, and whether cells are live or chemically fixed. The 

highest optical resolution is achieved with chemically 

fixed cells, such as was done to obtain the figure below, 

which shows two views of a single Escherichia coli bacterium, wherein thousands of single chemotaxis receptor 

proteins have been “magnified” about one million times. 

PALM also can be used to follow the spatial and temporal dynamics of proteins in living cells (e.g., Shroff et al. 

2008), although with very limited temporal resolution. 

Finally, PALM and STORM are able to image relatively 

deep inside tissues (Huang et al. 2008) 

using methods such as two-photon temporal focusing (Vaziri et al. 2008).

PALM combines high precision with the 

high specificity of genetically encoded 

fluorophores, whereas STORM allows 

the use of conventional organic fluorophores coupled to antibodies or to 

synaptosome-associated protein 

moieties. Such capabilities are important in systems that are either genetically 

intractable or anaerobic, characteristics 

that typically prevent genetically 

encoded fluorophores from fluorescing.

See also: Bahatyrova et al. 2004; Sener 

et al. 2007; Kirchhoff, Mukherjee, and 

Galla 2002.

Super-Resolution Light Microscopies Enable the Imaging, Counting, 

and Localization of Single Proteins in Microbes

1 μm

Comprehensive Imaging of Densely Packed 

Transmembrane 

Proteins Using PALM.

Tar chemotaxis receptors 

fused to a photoactivatable protein, monomer 

Eos, expressed in E. 

coli. Proteins displayed 

in blue represent those 

studied in a total internal 

reflection fluorescence 

(TIRF) geometry, and 

those displayed in red 

were studied in an epi 

geometry. [Images from 

Greenfield et al. 2009.]
31

3. Technological and Capability Needs

October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems

Transport in plant cells encompasses various 

types of cellular activity—from the shifting of molecules and solutes to the movement of 

proteins, lipids, and carbohydrates to the constant 

motion of intact organelles caused by several 

forces, including cytoplasmic streaming. Imaging this transport is a fascinating yet challenging 

endeavor requiring advancements in existing techniques and the development of new capabilities.

Technologies for visualizing both intracellular and 

intercellular transport are needed because plant 

cells are connected by specialized channels called 

plasmodesmata that pass through cell walls. To 

perceive and respond to external signals and to 

regulate growth, plants use hormones and solutes 

that require finely tuned, short-range transport 

into and out of cells and long-range transport among 

cells of several tissues. As sessile organisms, plants have 

evolved an extreme sensitivity to environmental cues 

and, in response, can subtly manipulate signaling networks and other cellular activities. Imaging technologies thus need to be noninvasive and nonperturbing to 

the cellular environment. 

Although advances in X-ray imaging techniques have 

aided the study of ion and metabolite transport in 

plants, in vivo analyses could be facilitated by the development of protein-based nanosensors. Such sensors 

may enable us to determine cytosolic and subcellular 

metabolite levels in real time using fluorescence-based 

microscopy. Other techniques for examining metabolite transport and resource distribution throughout 

plants include positron emission tomography (PET) 

and nuclear magnetic resonance imaging (MRI).

Another major challenge is imaging fluid transport in 

intact plants, which is important for understanding the 

regulation of plant growth and stress response. Caged 

probes present a promising approach that could address 

this challenge and enhance the study of plant cell-to-cell 

communication. Using diffusion rather than microinjection, caged probes can be noninvasively preloaded into 

tissues where the caged molecules are activated in a 

spatially controlled manner. After photoactivation, 

movement of the uncaged fluorochrome can be followed 

in time and direction by fluorescence microscopy. 

The widespread availability of digital cameras and 

digital and optical imaging has improved the imaging of proteins and other important macromolecules, 

enabling the visualization of organelles or cellular 

domains that contain such molecules. Although 

plants present several unique challenges to imaging—

background fluorescence and the effects of other 

parameters on the fluorescence signal (i.e., ionic 

strength, pH, and redox potential of organelles)—

advanced imaging methods based on fluorescent 

protein technology have greatly enhanced study of 

the dynamic structure of plant cells (see figure, this 

page). This technology has overcome fundamental 

problems in plant imaging, yet challenges remain, 

especially for nonprotein molecules such as lipids. 

Because of the variation in cell membrane composition among kingdoms, probes developed for imaging 

lipids in nonplant species often are inadequate for 

plant-based investigations.

One of the major technological challenges in the next 

decade will be increasing the resolution of imaging 

techniques in a physiological context. Resolution is a 

critical factor for studies that must separate cell constituents in an unperturbed environment and determine the in vivo concentration of imaged molecules. A 

solution to such challenges may involve a combination 

of adequate statistics in single cells for analyzingnoisy 

and partially correlated imaging data.

Challenges for Imaging Live Plant Cells

Fluorescent Imaging of 

Transport Proteins in 

Arabidopsis Root Tip.

Red and green indicate 

locations of two different 

types of auxin-transporting proteins. Yellow 

indicates where the two 

proteins work together. 

Auxin is a hormone that 

regulates the development 

of various plant tissues 

and structures important 

to bioenergy crop production and carbon partitioning in plants. 

[Image reproduced with permission of American Society of 

Plant Biologists © 2007. From Blakeslee et al. 2007.]
32

3. Technological and Capability Needs

New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

The problem of interrogating multicellular processes 

and systems is, at least in part, one of molecular analysis 

of complex mixtures (e.g., organics and biologicals). 

Next-generation analytical tools will tackle increasingly 

complex systems, enabling characterization of whole 

molecular machines and interacting protein networks. 

Most critically, these tools should push capabilities into 

the dynamic and kinetic regimes, allowing real-time 

analysis of interacting protein and macromolecular 

assemblies in solution, at interfaces, and across cellular 

and subcellular domains and membranes.

How do we achieve these capabilities? Significant 

progress could be made by developing approaches to 

measure selected cellular responses. The greater the 

number of cellular responses and dimensions that can 

be measured simultaneously, the greater the specificity 

of the model that can be built. Currently, the most powerful (e.g., sensitive, specific, and diverse) methods to 

measure cellular responses are biochemical and molecular assays. However, these types of assays typically are 

practical to use only with populations of cells, and the 

specific parameters being measured need to be known 

in advance, characteristics that prevent them from being 

global or unbiased in nature. Assuming that all cells 

in homogeneous populations respond identically is 

inherently problematic but is unsupportable for heterogeneous populations. Building realistic models of cell 

interactions thus requires the ability to either measure 

cellular responses at the single-cell level in communities 

or to know how to interpret population-based measurements in terms of the responses of individual cell types.

For example, understanding how a microbial community breaks down complex organic material requires 

knowing (1) the effective substrate for each cell type, 

(2) the products each produces, (3) which cell in turn 

uses those products, (4) and the spatial relationship 

among the cells. To follow this material flow, the concentrations and fluxes of different substrates on the cell 

population must be mapped and the ability of each cell 

to both use and produce different substrates must be 

identified. To accomplish this, we need to know how to 

build multiscale models of cellular communities that 

provide frameworks for mapping information derived 

from the population. The model should be able to represent both intercellular and intracellular networks.

3.4 Integrating Information

Living systems are extremely complex, and thus investigating their properties requires multiple experimental tools. Integrating diverse types of data generated 

by these different tools represents a major challenge in 

efforts to understand and predict biological function. 

Further complicating this challenge is the advent of 

high-throughput technologies, such as transcriptional 

profiling and MS–based proteomics, that can generate enormous amounts of data. Storing and managing 

these high-throughput data continue to be significant 

issues, but a more pressing and difficult problem 

involves interpreting and integrating the data with 

current biological knowledge. Much of this problem 

arises because knowledge in a given field is dispersed 

among individual investigators or found within a generally unstructured literature. Biology, unlike mathematics, lacks a formal language to describe and codify 

biological understanding and relationships. Therefore, most of our knowledge of biological systems is 

descriptive and qualitative in nature—a level of understanding insufficient for building predictive models 

of biological processes. Effective approaches for data 

integration and analysis need to be developed to successfully exploit biological systems to address energy 

and environmental challenges. DOE’s Genomic Science program is beginning to address these needs with 

its Systems Biology Knowledgebase (U.S. DOE 2009; 

see Fig. 3.3. Phases in Knowledgebase Development 

and Functionality, p. 33).

Future analytical technologies for characterizing biological systems will generate additional data requiring 

further integration and analysis. Moreover, because 

biology is becoming an increasingly distributed science in which collaborative studies are conducted 

at universities and institutions across the world, 

data standards are needed to facilitate information 

exchange and integration. As the volume of research 

data continues to explode, bringing together the 

appropriate experts to solve a systems-level problem 

will become more difficult. For example, large-scale 

projects might involve dozens or hundreds of laboratories. Making the data of interest available to each 

lab is impractical. Instead, data storage, retrieval, and 

processing likely will occur at a few sites, with domain 
33

3. Technological and Capability Needs

October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems

Increasing performance

Increasing knowledgebase technical capabilities

Systems

SolutionsIncreasing integration

Increasing content and tools

Phases in Knowledgebase Development

and Functionality

Phase II

• Comprehensive data integration

• Metabolic reconstructions

• Regulatory network reconstruction

• Extension to eukaryotes and 

microbial communities

• Laboratory partnerships

Phase III

• Automated data acquisition 

and analysis

• Support for organism 

engineering and design

• GKB fully integrated into 

experimental protocols

Database Technical Makeup I

• Standards and improved vocabularies

• Access, integration, and 

communications

• Coupling to LIMS

• Web- and Web services-based queries

• Distributed analyses support

Database Technical Makeup II

• Increasing levels of integration 

and automation

• Complex queries

• Complex operations

• Improved online tool sets

• Automatic conflict identification 

and resolution

Database Technical Makeup III

• Automated generation of microbial 

models from genomes

• Tools for eukaryotic model development

• Standards-based extensible environment

• Flexible user interface

• Links to experimental protocols

• Image data extraction tools

• Visualizaton tools for comparative 

analysis and prediction

• Computer-aided design (CAD) 

technologies

Database Entities and Tools III

• Integrated cellular models

• Prototype dynamical models

• Improved quantitative predictions

• CAD tools for organism engineering

Database Entities and Tools II

• Regulons and transcription factors

• Metabolic models

• Phenotype datasets

• Expression datasets

• Qualitative phenotype predictions Database Entities and Tools I

• Genomics, annotations

• High-throughput analytical data

• Improved metadata, provenance

• Comparative analysis

Phase I 

• Initial data integration

• Microbial genomics and 

metagenomics focus

• Distributed curation environment

• Beginning of laboratory data flows

Fig. 3.3. Phases in Knowledgebase Development and Functionality.

specialists interacting with and interpreting data in a 

distributed fashion. As an example, genomic and very 

large scale proteomic datasets from many individual 

projects were effectively integrated at DOE’s Environmental Molecular Sciences Laboratory to provide 

insight into the “core proteome” of bacterial species 

(see sidebar, Integration of Proteomics and Genomics 

Reveals a Core Proteome, p. 34).

To facilitate distributed biological research, several 

infrastructural problems must be solved, such as 

capturing experimental data and metadata and storing 

them in standard formats. Unfortunately, universal 

standards for biological data are unlikely to be implemented in the near future, so establishing general 

methods for translating data among multiple sources 

will be essential. Also needed are biologist-friendly 

software tools that handle heterogeneous datasets and 

support data analysis, simulation, concept building, 

and multi-investigator collaborations. A few such 

programs (e.g., Cytoscape) already are available on the 

web for analyzing genomic and protein-interaction 

data, but tools for integrating multiple data types 

are lacking. In particular, there is an urgent need for 

software that will integrate molecular, chemical, and 

structural information with spatial, temporal, and 

functional information. Providing scientists with 

training and support for any newly developed software 

tools also will be important.
34

3. Technological and Capability Needs

New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

I

nvestigation of a universal set of proteins 

expressed among 17 environmental and pathogenic bacteria was enabled by a proteome database 

encompassing about 967,000 experimentally determined unique peptides linked to specific protein 

information and genome sequences from the DOE 

Joint Genome Institute and other data sources (Callister et al. 2008). Bacteria selected for this investigation included the metabolically diverse organisms 

Rhodobacter sphaeroides, Shewanella oneidensis, and 

Synechocystis sp. PCC6803, as well as pathogens 

such as Yersinia pestis and Salmonella typhimurium. 

Facilitated by collaborations that have made samples 

available for proteomic analysis, this study represents 

the gathering and evaluation of proteomic measurements taken over the course of 6 years.

Genomic comparisons among the 17 bacteria predicted the existence of a core genome composed of 

144 genes (see figure, this page). Proteins from 74% 

of these genes were observed within the database, 

with each protein identified by two unique peptides. 

A functional analysis revealed that a majority of core 

proteome proteins (~55%) have functions related 

to protein synthesis, not surprising considering 

that the ability of a bacterial cell to make proteins 

for cell maintenance and growth is a vital function. 

However, what did surprise researchers was the 

observation of proteins (~7%) having very little or 

no functional characterization (e.g., the iojap-like 

protein). This gene was predicted to be homologous 

across all 17 bacteria and also is found outside the 

bacterial domain. Yet, little is known about this 

protein’s function in the bacterial cell (Galperin and 

Koonin 2004). The universal expression of these 

relatively uncharacterized proteins suggests they 

are critical for life. This study also demonstrates the 

novel insights that can arise from large-scale data 

integration studies.

Integration of Proteomics and Genomics Reveals a Core Proteome

The Core 

Proteome for 

17 Bacteria. 

Genomic comparisons identified 

genes common 

to two or more 

bacteria (orange), 

resulting in a core 

genome of 144 

genes. Proteomic 

measurements were 

used to verify the 

existence of these 

genes as proteins 

(red), resulting in 

the identification 

of a core proteome 

(arrow). 
35

3. Technological and Capability Needs

October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems

The ultimate aim of systems biology is creating realistic 

and predictive models, but representing inherent 

complexities is a major challenge. Though such models 

will greatly facilitate the useful manipulation and 

modification of biological systems, a realistic simulation of even the simplest cell at the molecular level is 

well beyond the capabilities of even the most sophisticated computer in the foreseeable future. Thus, some 

level of abstraction and simplification is necessary. Less 

clear is the optimal detail or “granularity” for modeling 

biological systems at different scales, although defining 

core pathways in cells has proven to be a powerful 

concept. For multicell systems, an effective strategy 

could be to first obtain information at the single-cell 

level and use network approaches to extrapolate 

single-cell responses to multiple cells. However, this 

approach would require understanding the sources of 

response heterogeneity in cell populations as well as 

the feedback systems that operate among the different 

cells. Defining these and other types of information 

needed for multiscale modeling is an essential prerequisite for determining the types of data that analytical 

technologies should gather.

There is unlikely to be a single “best” approach for 

modeling complex biological systems. Instead, the 

most effective methodologies probably will link 

analytical measurements and data analysis to models 

of a particular biological response. Building a predictive model requires the ability to measure effects 

and compare them to simulated results. This linkage 

between theory and experiment is technically difficult 

to achieve and has been most successful with relatively simple biological systems. As models increase 

in scale and complexity, however, their output will 

become correspondingly more complex. This will 

necessitate gathering and integrating even more types 

of data to test and validate these models. In the future, 

therefore, the types of data gathered on biological systems will be driven increasingly by those required for 

predictive modeling.

3.4.1 Data Integration and Predictive Modeling

One of the greatest challenges over the next decade 

will be integrating disparate data types (e.g., molecular 

and image) to extract quantitative predictive models of ecosystem function (McMahon et al. 2007). 

Classical modeling of metabolic reactions typically 

includes reaction kinetics that become untenable for 

global characterization of the thousands of reactions 

occurring in a single species, let alone in a community of organisms. One approach for bypassing this 

bottleneck is to ignore individual rate kinetics and 

conduct a so-called metabolic flux analysis (MFA), in 

which the overall flux of a system is modeled (Tang et 

al. 2008; Price et al. 2003; Llaneras and Picó 2008). 

MFA does not require kinetic parameters for the reactions involved and can be scaled to deal with complete 

genomes and, potentially, with metagenomes. A 

particular kind of MFA is flux balance analysis (FBA). 

It has been shown to provide accurate predictions 

of single phenotypic properties—such as growth 

and substrate-uptake rates—for pure cultures of, for 

example, Escherichia coli (Fong and Palsson 2004; 

Edwards, Ibarra, and Palsson 2001) and Helicobacter 

pylori (Schilling et al. 2002). Such global modeling 

applications still are in their infancy, especially for ecosystem modeling (Stolyar et al. 2007), and will require 

concerted research efforts and resources to mature.

Despite enormous advances in high-throughput 

methods, measuring all characteristics of all biological 

macromolecules in all cell types under all conditions 

at all time scales will not be possible. Thus, there is 

a need for further development and applications of 

machine-learning methods, broadly termed “active 

learning,” in which cycles of data-driven predictive 

modeling are followed by estimation of which subsequent experiments would best improve the model.

3.4.2 Informatics for Image Data

Much of the current effort in bioinformatics is directed 

toward using gene sequence information as a common framework for integrating molecular-level data. 

Integrating imaging data with other data types presents 

an entirely different kind of challenge, especially when 

attempting to link cellular images with compositional 

data. Challenges stem both from the complexity of the 

images themselves and from the need to relate these 

images to a common reference framework.

While extensive progress has been made on automated 

analysis of some types of biological images (Glory and 

Murphy 2007; Peng 2008), there is an urgent need 

for improved methods for data mining of images, with 
36

3. Technological and Capability Needs

New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

subsequent feature-detection capabilities for obtaining 

image statistics and measurements of properties such as 

cell size, cell-wall thickness, cell morphology, and label 

concentration. Unfortunately, the intrinsic variability 

of biological specimens complicates efforts to map 

spatial distributions observed in a single sample to a 

standard set of reference coordinates. New approaches 

are needed for linking images and their associated data 

at multiple scales using a type of co-registration scheme 

similar to a geographic information system (e.g., the 

Cell Centered Database, ccdb.ucsd.edu). A standard coregistration scheme could greatly facilitate the mapping 

of different cell types or cellular structures to data such 

as gene sequences, chemical compositions, environmental responses, and metabolic capabilities.

As in all data management efforts, the minimum metadata that should be associated with an image must be 

defined. Image data and associated metadata then need 

to be linked to experimental or systems-level data so 

that all data can be integrated with molecular-level characterization and image analysis and query. A framework 

allowing this type of data linkage across different scales 

and data types would greatly facilitate the ability to productively use imaging information in DOE’s Systems 

Biology Knowledgebase (U.S. DOE 2009).

3.4.3 Modeling Scale

Since current computational resources do not capture 

the vast complexity of biology, defining an appropriate 

level of resolution and scale is critical for practical 

modeling of any biological process. Akin to quantum 

mechanical description at small scales and a mechanical 

description at larger scales is the importance of judging 

the level of granularity needed for understanding the 

phenomena being investigated. This determination 

directly impacts which computational resources or 

methods need to be employed: workstation, cluster 

computers, supercomputers, distributed computing, 

grid computing, lattice computations, or simple 

simulation tools such as Mathematica and Matlab.

Many biological systems will be associated with steep 

gradients in various important physical and chemical 

parameters (e.g., the oxygen gradient in a biofilm). 

This factor poses a significant challenge for solving 

continuum-based simulations but is less of an issue 

for stochastic simulations. However, except for the 

simplest systems, current stochastic simulations are 

very inefficient because they require an enormous 

number of simulation runs to adequately explore 

parameter space. For mesoscopic simulations, too 

many particle parameters preclude the use of stochastic simulations, but a continuum approach cannot be 

used either because of granularity and discontinuities 

(e.g., steep gradients). Nevertheless, connecting the 

microscale to the macroscale requires dealing with 

the mesoscale. New approaches, therefore, need to be 

developed, such as rule-based simulations or transfer 

function models. However, the adequacy of any new 

modeling approach must be defined in terms of its 

ability to capture and predict biological phenomena.

3.4.4 Heterogeneity

Heterogeneity—a fundamental aspect of living 

organisms and natural ecosystems—is critical to both 

the stability and evoluability of systems. One of at 

least three types of heterogeneity relates to physical 

aspects of the environment, such as the patchiness 

of mineralization or structure in substrates used as 

nutrient sources. At the systems level, another type 

of heterogeneity can arise from genetic variation in 

a population of organisms or from stochastic aspects 

of cellular functions (e.g., gene expression). This heterogeneity can be reflected by changes in the average 

behavior of cell populations or complex organisms. 

At the chemical level, variations in the structure of 

chemically identical molecules represent another 

kind of heterogeneity. This is the basis of a phenomenon in which multiple products can be synthesized 

from chemically identical substrates by enzymes that 

catalyze reactions at different rates. Accounting for 

heterogeneity introduces substantial complexity into 

models, but mechanistic models must include the 

basis of their behavior.

3.4.5 Validation of Multiscale Models

Building multiscale models requires linking and 

integrating models that operate at different temporal 

and spatial levels. Not only must existing methods for 

linking simulation schemes be improved to represent 

complex biological processes, but the sources of error 

contained in individual models must be identified 

as well. Representing rare events in complex models 

also is quite difficult because of the simplifications 
37

3. Technological and Capability Needs

October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems

used to make them computationally tractable. Thus, 

erroneous model results can arise from both incorrect 

structures and parameters, underscoring the need for 

methods to distinguish between the problems caused 

by each. Also needed is the ability to assess the sensitivity of the overall model to changes in characteristics 

of individual submodels.

To improve multiscale modeling of data, new and 

existing strategies for coupling micro- and macrolevel models must be developed and tested. The 

information integration in these coupled, multiscale 

models possesses an inherent uncertainty that needs 

to be addressed by setting standards for data analysis. 

Uncertainty can be controlled by using numerical 

simulation techniques to approximate with quantitative error indicators on different scales—from isolated 

single-scale approaches to multiscale numerical simulation environments. This approach should take into 

account the inevitable and inherent errors in parameter estimates resulting from the limitations of instruments and analytical methodologies. Current model 

validation and verification efforts are constrained in 

part because of inadequate multiscale and upscaling 

methods. However, an even greater obstacle is the lack 

of realistic multiscale experimental models on which 

to test, calibrate, and validate the proposed methods. 

To calibrate any multiscale method, a model’s measurable quantities must be assessed before a method can 

be established to define its datasets’ hidden quantities. 

Once the models have been produced, specific assessment schemes will have to be developed to guarantee 

consistency of the resulting estimates.
38

3. Technological and Capability Needs

New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

Summary of Key Technological and Capability Challenges

3.1 Expanding Global Characterization Capabilities

•	 Advance microbial community genomic 

sequencing by increasing the coverage of more 

complex communities, analyzing metagenomic 

profiles over multiple time points, and developing single-cell sequencing and transcriptomics to 

investigate low-abundance organisms.

•	 Develop amplification methods for de novo

cDNA sequencing and capabilities for detecting 

small regulatory RNAs.

•	 Provide more complete proteome characterizations by improving measurements of membrane 

and low-abundance proteins and by decreasing 

bias in protein coverage.

•	 Combine multiple characterization techniques 

(e.g., NMR and MS) to expand metabolome 

coverage, increase throughput for metabolic flux 

analysis, develop global measurements for lipids 

and carbohydrates, and address dynamic range 

and measurement sensitivity issues—especially 

for complex plant metabolomes.

•	 Obtain data on kinetics and changes in spatial 

patterns by processing smaller samples taken at 

multiple time points throughout the duration of 

particular biological activities.

•	 Concurrently measure kinetic data and chemical 

changes in situ.

•	 Enhance fine-scale resolution of omics methods 

to enable the ultimate goal of relating transcriptomic, proteomic, and metabolomic flux 

measurements for selected cells from natural 

environments.

3.2 Identifying and Measuring Rare Events, 

Molecules, and Cells Within Complex, 

Heterogeneous Biological Systems

•	 Improve the speed, sensitivity, and automation 

of cell imaging to enable the detection of rare 

cell types.

•	 Develop methods for detecting extremely small 

molecular and biochemical signals that regulate 

cellular responses in situ.

3.3 Seeing It All: Simultaneous Measurements 

Across Multiple Dimensions

•	 Design new multiplexed measurement platforms 

that combine multiple techniques for tracking, 

visualizing, and interpreting real-time changes 

in the structure, organization, and activities of 

biological systems spanning multiple scales in 

space (nanometer to kilometer) and time (milliseconds to years).

•	 Simultaneously conduct molecular-scale studies 

that focus on small areas within a larger scale system to obtain detailed molecular information spanning a larger spatial area (e.g., the cellular interface).

3.4 Integrating Information

•	 Understand the sources of heterogeneity in cell 

populations so that network approaches can be 

used to extrapolate single-cell responses to multiple cells.

•	 To overcome the bottleneck of dealing with individual rate kinetics for thousands of reactions, 

scale up metabolic flux analysis approaches to 

model overall biochemical flux for genomes and 

potentially metagenomes.

•	 Define the minimum metadata needed for each 

image type and develop a standard co-registration 

scheme for mapping images of molecular and 

cellular structures and cells to other data (e.g., 

DNA sequences, chemical composition, and 

environmental conditions).

•	 Automate image analysis and feature-detection 

capabilities that describe and measure the characteristics (e.g., cell size and cell-wall thickness) 

of objects and processes captured in each image.

•	 Improve the ability of continuum-based simulations to address steep gradients in biological systems (e.g., oxygen gradient in a biofilm).

•	 Increase the efficiency of stochastic simulations 

by establishing new methods that decrease the 

number of simulation runs needed to adequately 

explore parameter space.

•	 Develop and test new strategies for coupling 

micro- and macro-level models, understand the 

sources of error in individual models, and assess 

the sensitivity of the overall model to changes in 

individual submodels.
October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems 39

We now have an unprecedented opportunity to greatly expand the dimensions of 

biological measurements. Technologies 

must be challenged and boundaries pushed to enable 

characterization of multiple species within complex, 

heterogeneous cellular systems with increasing temporal and spatial resolution. Biology will be significantly 

advanced by integrating multiple measurements and 

placing them in the true physical and temporal context 

of the system. The major challenge, however, is measuring processes with minimal perturbation and in as 

natural a setting as possible (i.e., in terra, in planta, and 

in vivo). Meeting this challenge represents an exciting prospect to obtain a qualitatively different level of 

insight about complex biological processes.

Although still in its infancy relative to more-established 

sciences such as chemistry and physics, quantitative 

biology has advanced tremendously during the past 

decade, largely because of technological developments 

in DNA sequencing, proteomics, and other highthroughput genomics-driven technologies. Biologists, 

however, still lack a predictive understanding of cells, 

organisms, and communities—knowledge essential for 

their rational design and manipulation. These prediction and manipulation capabilities are challenged by 

the inherently complex, dynamic, and heterogeneous 

nature of living organisms. By making strategic investments in a new generation of technologies, our ability 

to understand complex biological systems will advance 

significantly (see box below). Such investments would 

Adding Dimensions to Biological Measurements 

To Capture Function

•	 Mass spectrometry approaches that provide both 

chemical and spatiotemporal information

•	 Single-cell metabolomic and proteomic 

measurements

•	 Microfluidic approaches for cell isolation and 

analysis

•	 Imaging approaches that provide both spatial and

chemical information

•	 New nongenetic fluorescence probes and sets of 

multiplex probes

•	 Rapid, multiresolution, multidimensional imaging technologies

Expanding the Ability To Measure

and Manipulate Biological Components

•	 Super-resolution optical spectroscopy at the 

nanometer scale

•	 Electrochemical imaging at the cellular level

•	 Novel isotope technologies including subcellular 

tracer studies

•	 Improved isolation and identification of gene 

products from natural samples

•	 Nuclear magnetic resonance to distinguish

subtle biological molecular variation (e.g., among 

carbohydrates)

•	 Atomic force microscopy at the molecular scale 

for soft biology

•	 Higher-resolution electron microscopy to use 

with hydrated samples

Identifying Important Events 

in Heterogeneous Environments

•	 Secondary ion mass spectrometry at the nanoscale

•	 Nondestructive imaging for dynamics across 

long time scales

•	 Synchrotron-based approaches including infrared, X-ray fluorescence, and tomography for

in situ analysis

Modeling Complex Systems

•	 Improved mechanistic models of intercellular 

and intracellular networks

•	 New approaches to modularize complex networks

•	 Multicellular models

•	 Hybrid models to link multiple temporal and 

spatial scales

•	 Models representing long-term outcomes of 

rare events

Examples of Needed Technology Advancements

4. Path Forward
40 New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

4. Path Forward

continue DOE’s long history of facilitating important 

technological breakthroughs in the biological sciences and would improve the scientific foundation for 

solving the nation’s significant energy and environmental challenges.

As detailed in Chapter 2, beginning on p. 5, workshop 

participants identified several critical biological challenges that would benefit greatly from investments in 

technologies to enable their timely achievement. With 

predictive biology as the ultimate goal, these technology investments should be guided by the need to 

answer specific scientific questions, not simply to stimulate the development of technology for technology’s 

sake. Answering these questions should be of highest 

priority and will require integrated developments on a 

number of fronts.

Existing techniques can begin to address some of 

these issues, but almost all have limitations such as 

being static, able to measure only a single component, 

or restricted in spatial resolution. The challenge of 

simultaneously making global, dynamic, and multiscale measurements might be solved by improving 

these techniques or creating new ones. However, a 

single technology or related suite of technologies 

unlikely will be able to answer all of the complex, 

systems-level questions that need to be addressed. 

Thus, many different technical approaches should be 

explored simultaneously.

Technologies cannot be developed or validated in the 

absence of an appropriate experimental model, and it is 

unclear whether suitable experimental model systems 

currently exist for addressing all the questions discussed 

in this report. A reasonable path forward thus involves 

not only identifying technologies that will have the 

greatest impact on answering important biological 

questions, but also identifying or developing the most 

appropriate experimental models. Clearly needed are 

well-characterized and tractable model systems—on 

the organismal and multiorganismal scale—that more 

closely approximate natural settings and are relevant 

to DOE scientific mission areas. Development of such 

model systems is best done concurrently with technology development and application.

Collecting and integrating information that results from 

new technologies also will be critical for eventually 

predicting and manipulating biological function. If information can be effectively integrated, capability advancements in one technical area can synergize with those in 

others, potentially resulting in revolutionary progress in 

understanding complex biological systems.

4.1 Technology: Meeting the Measurement 

Challenge

The technologies needed to characterize a system 

ultimately will depend on the particulars of the system 

being examined and the research objectives being carried out. The broad technological challenges outlined 

in Chapter 3, beginning on p. 19, should be addressed 

in the context of specific model systems, with the aim 

of solving particular biological problems. As multicellular model systems are developed and natural systems 

more thoroughly characterized, technologies will need 

to be subsequently adapted and changed. Moreover, 

there are a number of important capabilities whose 

development is likely to be foundational to understanding complex biological systems and thus probably will require continuous improvement.

In the short term, development efforts should focus 

on adapting current technologies for tackling morecomplex, heterogeneous systems and on adding 

dynamic measurements to static techniques. Such 

improvements can be accomplished most effectively 

by increasing the speed and resolution of current 

technologies. For example, the slow speed of switchable fluorescent probes and image acquisition currently restricts the use of subdiffraction-limited optical 

imaging to fixed samples. Similarly, the slow speed of 

proteomics measurements limits their use in kinetic 

studies. In the medium term (5 to 10 years), current 

technologies should be combined or enhanced to 

enable multimodal, multidimensional analyses. In the 

long term (10 years or more), analyses will need to 

be expanded to allow measurement of complex living 

systems and their surrounding environment at appropriate length and time scales. Making comprehensive 

biological and environmental measurements will not 

always be practical. Therefore, it is critical to define 

the key measurements necessary for understanding 

and predicting system behavior and to determine the 

required scale and density of information. In addition, technology developments should be closely tied 
October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems 41

4. Path Forward

to improvements in data analysis and computational 

modeling to anticipate and deliver the needed measurement tools.

4.1.1 Adding Dimensions to Biological Measurements 

To Capture Function

A key finding from the workshop was the clear need 

for simultaneously assessing multiple molecular 

species with appropriate temporal and spatial resolution. In the near term, this will entail developing 

new labeling or probing technologies to spatially 

map the activity of multiple cellular processes in situ. 

Fluorescence-based labeling technologies and related 

nanoprobes were identified as critical areas in which 

immediate expansion is needed. In addition, microfluidic approaches for single-cell sorting and analysis 

should be pursued simultaneously with new genome 

amplification technologies to enable full genome 

sequencing of single cells. Highly sensitive mass spectrometry techniques also need to be developed for 

single-cell metabolite and proteomic measurements.

Capabilities for making multimodal measurements 

with spatial and temporal registration should be 

addressed in the near term, but significant technical 

challenges must be overcome to speed their development. For example, the current tradeoff between 

spatial and temporal measurements will hinder 

new developments until measurement rates can be 

increased appreciably. Emphasis thus is needed on 

developing faster detectors and brighter or more portable light sources that will enable new approaches to 

multiplexed measurements. Also needed are advanced 

nanoSIMS and other technologies for single-cell 

isotope imaging. Such advancements will increase the 

number of measurable parameters in complex systems 

and should be especially applicable to investigating 

multicellular systems.

Linking cellular, multicellular, and environmental 

scales is an ultimate goal requiring long-term investments in the development of approaches to simultaneously and noninvasively measure cellular states (e.g., 

the proteome, metabolome, and transcriptome). This 

linking also must be conducted for more-complex 

natural systems, necessitating the development of 

advanced imaging systems capable of interrogating 

large areas in three dimensions at high resolution. 

The extent of resolution needed for these technologies should be established by advanced modeling 

approaches addressing the particular biological question being pursued. Because of their widespread use 

and enabling nature, multiresolution and multidimensional imaging technologies should be priorities for 

continuous development and enhancement.

4.1.2 Expanding the Ability To Measure 

and Manipulate Biological Components

The types of studies that can be done with cells are 

restricted by limitations in what we can actually 

measure. Our ability to detect and quantify fundamental components such as proteins and nucleic acids 

is relatively advanced, but many varieties of different 

molecules are currently difficult to detect. Technologies need to be developed for completing the “parts 

list” of cellular components (e.g., metabolites and 

carbohydrates) that currently are invisible or poorly 

characterized. In the near term, the focus should be on 

expanding the number of different metabolites, ions, 

and well-defined products of enzymatic pathways that 

can be monitored. Especially important is detecting 

species that indicate the activity of cellular and intercellular pathways. Again, these technologies will be 

most informative if they can be applied at the level of 

the individual cell in a high-throughput manner.

In the midterm, measuring dynamics and, in particular, the flux of specific components within multicellular biological and natural systems will be critical. 

Approaches to image the spatial distribution and 

activity of less-characterized components also will 

be needed. A longer-term goal is developing general 

approaches for identifying complex extracellular molecules produced by cells or detecting the modification 

of the extracellular space by organisms. Such insight 

will be necessary to understand how cells dynamically 

alter their microenvironment and how this subsequently changes their physiological responses.

Simply cataloging components will be insufficient for 

understanding their function. Progress in this area will 

require developing approaches to specifically manipulate and measure the activity of these poorly characterized components. For example, chelators have been 

very useful for understanding the role of specific ions 

in cellular function, as have targeted inhibitors to 
42 New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

4.1.4 Modeling Complex Systems

Understanding which measurements are most informative for characterizing a complex biological system will 

require significant advances in computational modeling. Deterministic, ordinary differential equation–

based models are effective when applied to relatively 

simple biological systems, but they involve too many 

constraints, specified parameters, and computational 

resources to be generally useful for large-scale networks. 

Nonmechanistic approaches, such as Bayesian networks, 

lack the specificity needed to constrain experimental 

measurements. The disparity between the current scale 

of biological modeling and analytical technologies is 

one of the most severe bottlenecks in systems biology. 

However, applying systems biology to problems relevant 

to DOE will require developing scalable approaches that 

are applicable to multicellular systems.

In the near term, computational models should 

focus on describing physical relationships that can be 

experimentally measured (e.g., a model of a metabolic 

pathway should represent a physically measurable 

relationship). Likewise, parameters needed to constrain 

the model also should be measurable, such as protein 

abundance and stoichiometries. This will require modifying current models to align them to the experimental 

systems and biological challenges outlined in Chapter 2, 

beginning on p. 5. These models should focus on fundamental pathways and regulatory networks in cells. This 

level of representation is likely to reveal important and 

universal principles of cellular design.

In the midterm, new modeling approaches should be 

developed for representing characteristics of multicellular systems, such as the relationships between different 

cell types and the flux of energy and materials through 

the system. These capabilities likely will require 

improved mathematical representations, such as rulebased modeling and new computational approaches. 

Including heterogeneity in the models likely will be 

very challenging but nevertheless essential for accurately re-creating the behavior of cellular communities 

from knowledge of their individual members.

In the long term, we should strive to develop predictive models with an appropriate level of granularity. 

This will require coordinating analytical and modeling 

efforts and developing practical approaches for building 

4. Path Forward

enzymes. Activity-based protein profiling techniques 

are being developed to characterize enzyme function, 

and such approaches should be extended to measure 

other components. Also needed are general techniques to inhibit or increase the availability or activity 

of poorly understood components, such as modified 

lipids. Manipulating the spatial distribution and activity of components would be a particularly powerful approach for understanding their physiological 

function in the normal cellular context. Experimental 

manipulation of complex system variables, at the 

molecular level, will be necessary for understanding 

the functional processes from the organismal to the 

environmental scale. A long-term goal is making such 

manipulations routine, while simultaneously measuring their effects on system processes.

4.1.3 Identifying Important Events in Heterogeneous 

Environments

Measuring rare events or minority components and 

relating them to functional outcomes are critical longterm research goals that first require an improved ability to detect such events and cell types. As described in 

Section 3.2, p. 23, a necessary foundation for achieving 

these objectives is advancements in single-molecule 

and single-cell measurement technologies. These 

technologies then need to be enhanced to identify 

and detect single or small populations of molecules 

or cells within complex heterogeneous backgrounds. 

Better measurements of single molecules or cells subsequently should improve our ability to detect small 

differences in a population’s composition or activity.

While sensitive detection technologies will be critical, 

equally important is the need for these technologies 

to be high throughput and parallel. Measurements on 

a single cell can be insightful, but much greater value 

could be obtained from the comparative measurements of hundreds, thousands, or tens of thousands 

of cells. This comparative capability could allow rare 

events to be reliably identified. To understand the 

outcome of these rare events, however, we need to 

be able to follow the same population over long time 

periods using nondestructive methods. Simultaneous 

improvements in the development of model systems 

and analytical technologies should be directed toward 

achieving this aim.
October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems 43

scalable models. Creating such models involves developing hybrid models or more-effective mathematical 

approaches for representing multidimensional models. 

The accuracy of the models should be improved to 

enable evaluation of the effects of rare events. Such 

advancement is likely to require far more powerful 

computers than currently available or novel approaches 

for distributed computing.

4.2 Research Strategy: Linking Observations 

from Cells to Natural Communities

Most traditional biological models were chosen for 

their convenience and simplicity rather than relevance 

for solving particular biological problems. Addressing many of the problems relevant to DOE’s energy 

and environmental missions, however, will require 

understanding how biological systems function in situ. 

Such a goal is beyond our current technical capabilities in many cases, and thus appropriate experimental 

systems need to be established in the laboratory to 

serve as a bridge to reach this goal. This is particularly 

the case for microbial communities whose inherent complexity challenges efforts to understand the 

mechanisms of their interactions. Several of the technologies described in Chapter 3, beginning on p. 19, 

have the potential to address the challenges associated 

with natural biological systems, but unless investigators work toward defining and validating workable 

experimental models, the development of these technologies will lag.

Because they are relatively simple and well characterized, current experimental model systems are likely to 

be the starting points for any new technology development efforts. In some cases, relatively simple plant and 

microbial species relevant to various DOE missions 

already have been identified and initially characterized. For example, microalgal systems proposed as a 

platform for biofuel development also would be excellent systems for understanding basic cellular processes. 

The initial systems must be relatively simple so that 

they are tractable using current technologies, but they 

also should be suitable for investigating the questions 

posed in this report. It would be best if the scientific 

community identified a limited set of model organisms 

to facilitate collaborative efforts, but it is recognized 

that sometimes a particular biological problem is best 

approached by the use of a unique model system. For 

cases in which the choice of a particular strain or species of organism is not critical, every effort should be 

made to use DOE-relevant model systems and modify 

current technologies to work with them.

Tools for manipulating and probing current model 

organisms must be improved, but new classes of 

methodologies likely are needed to perform manipulative experiments on more-advanced model systems. 

There is an urgent need to develop these advanced systems, particularly those that involve the co-cultivation 

of multiple organisms. Ideally, these multiorganism systems should be capable of being spatially defined and 

should support the development of multidimensional 

analytical technologies. These technologies should be 

developed in conjunction with the experimental systems, and the limitations of both should be defined.

In the medium term, model biological systems need 

to transition toward those with increasing complexity representative of natural systems. This transition 

will require an improved understanding of natural 

biological systems and the factors controlling their 

stability. Needed are better approaches to identify 

and characterize the species in natural systems, as well 

as improvements made in defining their spatial and 

temporal organization and functional capabilities. 

The data obtained from natural systems then should 

be used as a basis for creating simplified models in 

the lab. Creating mimics of natural systems likely will 

require a new generation of growth chambers compatible with multiple analytical devices and probes. In the 

longer term, investigators should be able to conduct 

complex analytical measurements in the field.

Eventually, multicellular model systems will be used to 

explore a variety of different processes, such as energy 

flow through communities in a controlled environment. These model systems also should support the 

development of more-advanced technologies, especially 

multimodal approaches for simultaneously measuring 

intercellular and extracellular environments. A critical focus should be not only on measuring relevant 

characteristics, but also on precisely manipulating these 

characteristics to learn how information, materials, and 

energy flow through the system. Necessary capabilities 

will include selectively analyzing individual cells within 

4. Path Forward
44 New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

complex mixtures of organisms and within heterogeneous chemical and physical environments. Attaining 

these goals and realizing a predictive understanding of 

flux through these systems will require the iterative testing of models that, in turn, must be supported by effective collection and integration of measurement data.

The long-term goal is integrating information from 

model systems with data from the direct interrogation of natural communities in situ. Ideally, this should 

be possible at a field site, but some technologies are 

likely to be restricted to the laboratory for the foreseeable future. Thus, transplantation of natural systems 

into the laboratory setting likely will be necessary. 

This will require improvements in the controlled 

environmental chambers that can be used with various imaging techniques. These should move beyond 

state-of-the-art environmental chambers, such as 

those that are beginning to allow electron microscopy 

to be conducted with hydrated samples, toward those 

that include microfluidics to allow direct experimental 

manipulation during analysis.

4.3 Knowledge Visualization: Overcoming 

Challenges of Data Integration 

and Systems Analysis

Development of characterization technologies for 

multicellular systems should take into consideration the idea that such technologies must enable 

experimentation at a much larger scale than that of 

traditional biological research while retaining the necessary detail and resolution. As the focus shifts from 

characterizing the average behavior of populations to 

enumerating the specific behavior of their constituent 

members, the magnitude of data that must be gathered, processed, and understood will expand greatly. 

This information explosion must be anticipated and 

managed up front. The experience of the Internet 

has shown that data accessible from creation is far 

easier to manage and manipulate than data that reside 

in legacy repositories. Early integration between 

experimental biologists, technology developers, and 

computational biologists is essential.

Data volume also can be managed by restricting the 

scale of measurements to that which is needed to 

adequately reflect the biological system. This first will 

require advancements in computational modeling 

technologies to understand the level of granularity 

needed to represent a given biological process. Nevertheless, there should be some early effort to define 

“recommended scales of measurements for biological 

systems” that could be used as guidelines or references 

for the types and amounts of measurements needed to 

predict, model, and quantify specific systems.

Even if efforts are made to restrict data volume, the 

amount that will be gathered and managed is still 

likely to be enormous. Multimodal data that will be 

generated over the next few years will present an 

entirely new challenge. Thus, necessary in the medium 

term will be development of tools for integrating and 

interpreting complex datasets. The ultimate aim of 

systems biology is predictive modeling of biological systems, which will require an iterative cycle of 

experiment-simulation-experiment. Because of the 

necessity of comparing the results of the models with 

those of the experiments, measurement technologies 

need to be developed in parallel with the models so 

that outputs of both approaches are comparable. This 

will ensure the generation of useful databases and 

computational approaches for integrating measurements and models at multiple scales.

Another important medium-term goal should be 

defining the modularity, or systems-level rules that 

could be inferred using novel approaches for analyzing 

complex datasets. This first will require the storage and 

accessibility of the data, as described above. Making sense of complex datasets also will require new 

approaches for visualizing multidimensional data. 

Attaining these visualization capabilities will require 

investing in the development of computational tools 

that enable the integrated graphic display of multidimensional cellular parameters (e.g., genome, proteome, metabolome, transcriptome, and bibliome). 

Such advances need to be coordinated with the development of new analytical tools because of the current 

paucity of ways to productively query and comparatively display such data. Automated or semiautomated 

data mining likely will play an important role as well.

In the long term, sophisticated computational tools 

should be able to take data acquired from highthroughput instruments and automatically build a 

predictive model of the system. These “virtualization” technologies should allow for rapid cycling of 

4. Path Forward
October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems 45

experimentation and modeling, thus greatly 

assisting in the rational exploration of complex 

biological systems.

4.4 Integration: Teaming at Disciplinary 

Interfaces To Advance Frontiers 

in Biological Research

Clearly, there is a strong need to integrate the biological sciences with analytical and computational technologies. Simply stimulating development in the area 

of measurement technology is insufficient. Moving 

the science forward requires integration, assimilation, 

and understanding. This will not be accomplished by 

technology, but by people. The exponential increase 

in the speed and scale of biological technologies 

has created the need for teaming in biology as never 

before. DOE BER has a tradition of driving multidisciplinary collaborations in the physical and biological 

sciences in concert with technology development and 

is ideally positioned to continue such efforts. In support of advancing the frontiers in biological research, 

BER should foster integration across biology, physics, 

mathematics, chemistry, computation, engineering, 

and materials science. In particular, it should facilitate 

the integrating of “omics” technologies with more 

traditional measurement modalities.

In the near term, DOE could help achieve the goal of 

increasing interdisciplinary science by supporting

•	 Integrative training for graduates, post graduates, 

and principal investigators

•	 Collaborative incentives to individuals and 

institutions

•	 Facilitated access to high-end technologies through 

more accessible user facilities that also provide 

adequate technical support for data interpretation

•	 Development of high-capability instruments at 

affordable prices (spread technology and opportunities to do good science across laboratories).

DOE also should strive to enhance communication 

within and across fields by

•	 Defining research and measurement standards across 

disciplines that are necessary to allow data exchange

•	 Generating large public domains for data exchange

•	 Identifying common overarching multiscale problems between fields

•	 Integrating experimental tools and design across 

BER programs.

The complexity of the biological challenges described 

in this workshop report requires scientific collaboration, which should be the expectation of this type of 

research. An important goal of any program addressing 

the frontiers of biological research should be to stimulate greater interactions among experimentalists, technology developers, computer scientists, and biological 

modelers. One way this can be done is to support the 

creation and development of integrated teams of biologists, physicists, chemists, material scientists, computation scientists, and engineers who work under one roof 

to tackle biological and technical challenges.

Just as the genome era ushered in the current biological revolution, emerging technological advances are 

poised to reveal untold biological mysteries. It was 

not so long ago that we could not quite conceive of 

sequencing an entire microbial genome, and now 

sequencing a human genome is increasingly commonplace. DOE was the first to take up the challenge 

of genome sequencing and has opened up an entirely 

new area of biological research. It now has the opportunity once again to change the future of biology. We 

do not know all of the discoveries that will ensue from 

advancing the frontiers of biological knowledge, but 

history indicates that many opportunities await us.

4. Path Forward
46 New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

4. Path Forward
October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems 47

New Frontiers in Characterizing Biological Systems Workshop

Agenda

Bethesda Marriott Hotel

May 13–14, 2009

May 12

7:00 – 9:00 p.m. Dinner Meeting with Working Session Chairs

May 13

7:30 – 8:30 a.m. Registration Sign-In and Continental Breakfast

8:30 – 9:45 a.m. Welcome and Opening Remarks – Anna Palmisano and Sharlene Weatherwax

8:45 – 9:00 a.m. Objective of Workshop – Arthur Katz

9:00 – 10:30 a.m. Key Issues, Challenges, and Technology Needs

•	 Biofuels: Brian Davison

•	 Carbon Cycle: Colin Murrell

•	 Environmental Remediation: Alfred Spormann

10:30 – 11:00 a.m. Break

11:00 –11:30 p.m. Parallel Breakout Sessions Technology Overview

Mark Ellisman (Technology Perspective)

11:30 – 1:00 p.m. Buffet –Working Lunch; Eat in Breakout Sessions

1:00 – 3:00 p.m. Breakout Session Continues

3:00 – 3:30 p.m. Break

3:30 – 4:30 p.m. Plenary Session

4:30 – 5:30 p.m. Plenary Session Continues - Interim Report from Each Breakout Group

6:30 – 7:30 p.m. Dinner Meeting with Working Session Chairs

•	 Update on the Progress and Issues from Each Group

•	 Draft Outline for Group Report

May 14

7:30 – 8:30 a.m. Breakfast

8:30 – 10:00a.m. Parallel Breakout Session Wrap-Up

10:00 – 10:30 a.m. Break

10:30 – 12:00 p.m. Plenary Session – Final Report from Breakout Sessions

12:00 – 1:00 p.m. Working Lunch (Meeting with Session Chairs)

1:00 – 3:00 p.m. Plenary Session Round-Table Discussion with Session Chairs

3:00 – 3:15 p.m. Workshop Concludes – DOE Concluding Remarks

3:15 – 5:30 p.m. Writing Team Leaders Meeting

•	 Complete Breakout Session Reports

•	 Plans and Timeline for the Workshop Report

Appendix 1
48 New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009
October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems 49

New Frontiers in Characterizing Biological Systems Workshop

Barcellos-Hoff, Mary Helen*

New York University School of 

Medicine

Brandizzi, Federica*

Michigan State University

Davison, Brian

Chief Scientist, Genomic Science 

program (beginning Oct. 1, 2009)

Oak Ridge National Laboratory

Writing Team

______________

*Cochair, Multicellular Session. **Cochair, Interface Session. †Cochair, Cellular Session.

Additional contributors: Wolfgang Fink, Bruce Fouke, Hoi-Ying Holman, Jan Liphardt, Tijana Rajh, Alfred Spormann, 

and Sunney Xie.

Participants

Barcellos-Hoff, Mary Helen

New York University School of Medicine

Bard, Allen

The University of Texas at Austin

Batt, Carl

Cornell University

Bhat, Talapady

National Institute of Standards 

and Technology

Brandizzi, Federica

Michigan State University

Chen, Liaohai

Argonne National Laboratory

Cooks, Graham

Purdue University

Cramer, Stephen

University of California, Davis

Davison, Brian

Oak Ridge National Laboratory

Doktycz, Mitchel

Oak Ridge National Laboratory

Ellisman, Mark

University of California, San Diego

Fink, Wolfgang

California Institute of Technology

Fouke, Bruce

University of Illinois

Fredrickson, Jim

Pacific Northwest National Laboratory

Gilna, Paul

University of California, San Diego

Haswell, Elizabeth

Washington University, St. Louis

Hlatky, Lynn

Tufts University School of Medicine

Holman, Hoi-Ying

Lawrence Berkeley National Laboratory

Hugenholtz, Phillip

DOE Joint Genome Institute

Knight, Rob

University of Colorado, Boulder

Lane, Stephen

University of California, Davis

Liphardt, Jan

Lawrence Berkeley National Laboratory

Markley, John

University of Wisconsin-Madison

Miller, Lisa

Brookhaven National Laboratory

Murphy, Robert

Carnegie Mellon University

Murrell, Colin

University of Warwick

Doktycz, Mitchel**

Oak Ridge National Laboratory

Fredrickson, Jim

Chief Scientist, Genomic Science 

program (through Sept. 30, 2009)

Pacific Northwest National Laboratory

Hugenholtz, Phillip**

DOE Joint Genome Institute

Sobecky, Patricia†

University of Alabama

Sweedler, Jonathan†

University of Illinois

Wiley, H. Steven

Pacific Northwest National Laboratory

Myles, Dean

Oak Ridge National Laboratory

Patterson, George

National Institutes of Health

Pianetta, Piero

Stanford Linear Accelerator Center

Rajh, Tijana

Argonne National Laboratory

Sobecky, Patricia

University of Alabama

Spormann, Alfred

Stanford University

Sweedler, Jonathan

University of Illinois

Taylor, Kenneth

Florida State University

van der Lelie, Daniel

Brookhaven National Laboratory

Vo-Dinh, Tuan

Duke University

Wallenstein, Matthew

Colorado State University

Wiley, H. Steven

Pacific Northwest National Laboratory

Xie, Sunney

Harvard University

Appendix 2
50 New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

Anderson, Todd

U.S. Department of Energy

Cole, Dean

U.S. Department of Energy

Graber, Joseph

U.S. Department of Energy

Gregurick, Susan

U.S. Department of Energy

Hirsch, Roland

U.S. Department of Energy

Katz, Arthur

U.S. Department of Energy

Observers

Srivastava, Prem

U.S. Department of Energy

Stacey, Gary

(Biological and Environmental Research 

Advisory Committee)

University of Missouri

Stack, Robert

U.S. Department of Energy

Swain, Amy

National Center for Research Resources

Weatherwax, Sharlene

U.S. Department of Energy

Mansfield, Betty

Oak Ridge National Laboratory

Marron, Michael

National Center for Research Resources

McLean, Gail

U.S. Department of Energy

Metting, Noelle

U.S. Department of Energy

Palmisano, Anna

U.S. Department of Energy

Sayler, Gary

(Biological and Environmental Research 

Advisory Committee)

University of Tennessee
October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems 51

Appendix 3

American Academy of Microbiology. 2006. “Microbial 

Energy Conversion: A Report from the American Academy of Microbiology,” eds. Merry Buckley and Judy Wall 

(http://academy.asm.org/index.php?option=com_content

&task=view&id=51&Itemid=54).

Bahatyrova, S., et al. 2004. “The Native Architecture of a 

Photosynthetic Membrane,” Nature 430(7003), 1058–62.

Bayer, E. A., Y. Shoham, and R. Lamed. 2006. “CelluloseDecomposing Bacteria and Their Enzyme Systems.” In The 

Prokaryotes, 3rd ed., Vol. 2: Ecophysiology and Biochemistry, edited by M. Dworkin, S. Falkow, E. Rosenberg, 

K.-H. Schleifer, and E. Stackebrandt, 578–617. Springer, 

New York.

Behrens, S., et al. 2008. “Linking Microbial Phylogeny 

to Metabolic Activity at the Single-Cell Level by Using 

Enhanced Element Labeling-Catalyzed Reporter Deposition Fluorescence In Situ Hybridization (EL-FISH) and 

NanoSIMS,” Applied and Environmental Microbiology

74(10), 3143–50. doi:10.1128/AEM.00191-08.

Bergeron, A., et al. 2007. “Divide and Conquer: Enriching 

Environmental Sequencing Data,” PLoS ONE 2(9), e830. 

doi:10.1371/journal.pone.0000830.

Betzig, E., et al. 2006. “Imaging Intracellular Fluorescent 

Proteins at Nanometer Resolution,” Science 313, 1642–45.

Bisseling, T., J. L. Dangl, and P. Schulze-Lefert. 2009. “NextGeneration Communication,” Science 324(5928), 741. 

Special issue, Plant-Microbe Interactions.

Blakeslee, J. J., et al. 2007. “Interactions among PINFORMED and P-Glycoprotein Auxin Transporters in 

Arabidopsis,” The Plant Cell 19(1), 131–47.

Blow, N. 2009. “Systems Biology: Untangling the Protein 

Web,” Nature 460, 415–18. doi:10.1038/460415a.

Callister S. J., et al. 2008. “Comparative Bacterial Proteomics: Analytics of the Core Genome Concept,” PLoS ONE

3(2), e1542. 

Cell Centered Database, ccdb.ucsd.edu.

Chan, J. W., et al. 2007. “Monitoring Dynamic Protein 

Expression in Living E. coli Bacterial Cells by Laser Tweezers 

Raman Spectroscopy,” Cytometry 71A(7), 468–74.

Choi, P., et al. 2008. “A Stochastic Single-Molecule Event 

Triggers Phenotype Switching of a Bacterial Cell,” Science

322(5900), 442–46.

Dong, M., et al. 2008. “A ‘Tagless’ Strategy for Identification 

of Stable Protein Complexes Genome-Wide by Multidimensional Orthogonal Chromatographic Separation and 

iTRAQ Reagent Tracking,” Journal of Proteome Research

7(5), 1836–49.

Dumont, C. G., and J. C. Murrell. 2005. “Stable Isotope 

Probing—Linking Microbial Identity to Function,” Nature 

Reviews Microbiology 3, 499–504.

Edwards, J. S., R. U. Ibarra, and B. O. Palsson. 2001. “In 

silico Predictions of Escherichia coli Metabolic Capabilities 

Are Consistent with Experimental Data,” Nature Biotechnology 19(2), 125–30. doi:10.1038/84379.

Elf, J., G.-W. Li, and X. S. Xie. 2007. “Probing Transcription 

Factor Dynamics at the Single-Molecule Level in a Living 

Cell,” Science 316, 1191–94.

Elowitz, M. B., et al. 2002. “Stochastic Gene Expression in a 

Single Cell,” Science 297, 1183–86.

Faley, S., et al. 2008. “Microfluidic Platform for Real-Time 

Signaling Analysis of Multiple Single T Cells in Parallel,” 

Lab on a Chip 8, 1700–12.

Feng, X., et al. 2009. “Characterization of the Central 

Metabolic Pathways in Thermoanaerobacter sp. Strain X514 

via Isotopomer-Assisted Metabolite Analysis,” Applied and 

Environmental Microbiology 75(15), 5001–08.

Fink, W. 2008. “Stochastic Optimization Framework 

(SOF) for Computer-Optimized Design, Engineering, and 

Performance of Multi-Dimensional Systems and Processes,” 

Proceedings of the SPIE Conference on Space Exploration Technologies, 6960(69600N), doi:10.1117/12.784440.

Fong, S. S., and B. O. Palsson. 2004. “Metabolic GeneDeletion Strains of Escherichia coli Evolve to Computationally Predicted Growth Phenotypes,” Nature Genetics 36(10), 

1056–58. doi:10.1038/ng1432.

Fouke, B. W., et al. 2000. “Depositional Facies and 

Aqueous-Solid Geochemistry of Travertine-Depositing 

Hot Springs (Angel Terrace, Mammoth Hot Springs, Yellowstone National Park, U.S.A.),” Journal of Sedimentary 

Research 70(3), 565–85.

Fouke, B. W., et al. 2003. “Partitioning of Bacterial Communities Between Travertine Depositional Facies at Mammoth 

Hot Springs, Yellowstone National Park, U.S.A.,” Canadian

Journal of Earth Sciences 40(11), 1531–48.

Frias-Lopez, J., et al. 2008. “Microbial Community Gene 

Expression in Ocean Surface Waters,” Proceedings of the 

National Academy of Sciences of the United States of America

105(10), 3805–10. doi: 10.1073/pnas.0708897105.

Galperin, M. Y., and E. V. Koonin. 2004. “‘Conserved 

Hypothetical’ Proteins: Prioritization of Targets for Experimental Study,” Nucleic Acids Research 32(18), 5452–63.

Glory, E., and R. F. Murphy. 2007. “Automated Subcellular Location Determination and High-Throughput 

Microscopy,” Developmental Cell 12(1), 7–16. 

doi:10.1016/j.devcel.2006.12.007.

Greenfield, D., et al. 2009. “Self-Organization of the E. coli

Chemotaxis System Imaged with Super-Resolution Light 

Microscopy,” PLoS Biology 7(6), e1000137. doi: 10.1371/

journal.pbio.1000137.

Bibliography 
52 New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009

Gustafsson, M. G. L. 2005. “Nonlinear StructuredIllumination Microscopy: Wide-Field Fluorescence 

Imaging with Theoretically Unlimited Resolution,” Proceedings of the National Academy of Sciences of the United 

States of America 102(37), 13081–86.

Hell, S. 2003. “Toward Fluorescence Nanoscopy,” Nature 

Biotechnology 21(11), 1347–55.

Hess, S. T., T. P. Girirajan, and M. D. Mason. 2006. “UltraHigh Resolution Imaging by Fluorescence Photoactivation Localization Microscopy,” Biophysical Journal 91(11), 

4258–72.

Huang, B., et al. 2008. “Three-Dimensional Super-Resolution 

Imaging by Stochastic Optical Reconstruction Microscopy,” 

Science 319, 810–13.

Huang, W. E., et al. 2007. “Raman-FISH: Combining 

Stable-Isotope Raman Spectroscopy and Fluorescence In 

Situ Hybridization for the Single Cell Analysis of Identity 

and Function,” Environmental Microbiology 9, 1878–89.

Hugenholtz, P., and G. W. Tyson. 2008. “Microbiology: 

Metagenomics,” Nature 455, 481–83. doi:10.1038/455481a.

Hura, G. L., et al. 2009. “Robust, High-Throughput Solution Structural Analyses by Small Angle X-Ray Scattering 

(SAXS),” Nature Methods 6(8), 606–12.

Kandianis, M., et al. 2008. “Microbial Biomass: A Catalyst for CaCO3 Precipitation in Advection-Dominated 

Transport Regimes,” Geological Society of America Bulletin, 

120(3–4), 442–50.

Kirchhoff, H., U. Mukherjee, and H. J. Galla. 2002. 

“Molecular Architecture of the Thylakoid Membrane: Lipid 

Diffusion Space for Plastoquinone,” Biochemistry 41(15), 

4872–82.

Kunin, V., et al. 2008. “Millimeter-Scale Genetic Gradients 

and Community-Level Molecular Convergence in a Hypersaline Microbial Mat,” Molecular Systems Biology 4, 198. 

doi:10.1038/msb.2008.35.

Kruger, N. J., and R. G. Ratcliffe. 2009. “Insights into Plant 

Metabolic Networks from Steady-State Metabolic Flux 

Analysis,” Biochimie 91(6), 697–702.

Llaneras, F., and J. Picó. 2008. “Stoichiometric Modelling 

of Cell Metabolism,” Journal of Bioscience and Bioengineering

105(1), 1–11. doi:10.1263/jbb.105.1.

McMahon, K., H. García Martín, and P. Hugenholtz. 

2007. “Integrating Ecology into Biotechnology,” Current 

Opinion in Biotechnology 18(3), 287–92. doi:10.1016/

j.copbio.2007.04.007.

National Research Council, Committee on Review of the 

Department of Energy’s Genomics: GTL Program. 2006. 

Review of the Department of Energy’s Genomics:GTL Program, National Academies Press, Washington, D.C.

Nealson, K., and W. A. Ghiorse. 2001. Geobiology: Exploring 

the Interface Between the Biosphere and the Geosphere, American Academy of Microbiology, Washington, D.C.

Neufeld, J. D., M. Wagner, and J. C. Murrell. 2007. “Who 

Eats What, Where and When? Isotope-Labelling Experiments Are Coming of Age,” ISME Journal 1, 103–10.

Pelletier, D. A., et al. 2008. “A General System for Studying 

Protein-Protein Interactions in Gram-Negative Bacteria,” 

Journal of Proteome Research 7(8), 3319–28.

Peng, H. 2008. “Bioimage Informatics: A New Area of Engineering Biology,” Bioinformatics 24(17), 1827–36.

Pinchuk, G. E., D. A. Rodionov, C. Yang, et al. 2009. 

“Genomic Reconstruction of Shewanella oneidensis MR-1 

Metabolism Reveals a Previously Uncharacterized Machinery for Lactate Utilization,” Proceedings of the National 

Academy of Sciences of the United States of America 106(8), 

2874–79.

Price, N., et al. 2003. “Genome-Scale Microbial In Silico

Models: The Constraints-Based Approach,” Trends in Biotechnology 21(4), 162–69.

Pushkarev, D., N. F. Neff, and S. R. Quake. 2009. “SingleMolecule Sequencing of an Individual Human Genome,” 

Nature Biotechnology 27, 847–50. doi:10.1038/nbt.1561.

Ratcliffe, R. G., and Y. Shachar-Hill. 2006. “Measuring 

Multiple Fluxes Through Plant Metabolic Networks,” Plant 

Journal 45(4), 490–511.

Rozhkova, E. A., et al. 2009. “A High-Performance Nanobio 

Photocatalyst for Targeted Brain Cancer Therapy,” Nano 

Letters, Article ASAP. doi: 10.1021/nl901610f.

Rust, M. J., M. Bates, and X. Zhuang. 2006. “Sub-Diffraction–

Limit Imaging by Stochastic Optical Reconstruction Microscopy (STORM),” Nature Methods 3(10), 793–95.

Schilling, C. H., et al. 2002. “Genome-Scale Metabolic Model 

of Helicobacter pylori 26695,” Journal of Bacteriology 184(16), 

4582–93. doi:10.1128/JB.184.16.4582-4593.2002.

Sener, M. K., et al. 2007. “Atomic-Level Structural and 

Functional Model of a Bacterial Photosynthetic Membrane 

Vesicle,” Proceedings of the National Academy of Sciences of 

the United States of America 104(40), 15723–28.

Shaner, N. C., G. H. Patterson, and M. W. Davidson. 2007. 

“Advances in Fluorescent Protein Technology,” Journal of 

Cell Science 120(24), 4247–60.

Shi, Y., G. W. Tyson, and E. F. DeLong. 2009. “Metatranscriptomics Reveals Unique Microbial Small RNAs in the 

Ocean’s Water Column,” Nature 459, 266–69. doi:10.1038/

nature08055.

Shroff, H., et al. 2008. “Live-Cell Photoactivated Localization Microscopy of Nanoscale Adhesion Dynamics,” Nature 

Methods 5(5), 417–23.

Stolyar, S., et al. 2007. “Metabolic Modeling of a Mutualistic 

Microbial Community,” Molecular Systems Biology 3(92). 

doi:10.1038/msb4100131.

Tang, Y. J., et al. 2008. “Advances in Analysis of Microbial 

Metabolic Fluxes Via 13C Isotopic Labeling,” Mass Spectrometry Reviews 28(2), 362–75.
October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems 53

Tringe, S. G., and P. Hugenholtz. 2008. “A Renaissance for 

the Pioneering 16S rRNA Gene,” Current Opinion in Microbiology 11(5), 442–46.

U.S. DOE. 2006. Breaking the Barriers to Cellulosic Ethanol: A Joint Research Agenda. DOE/SC-0095 (http://

genomicscience.energy.gov/biofuels/b2bworkshop.shtm).

U.S. DOE. 2008. Carbon Cycling and Biosequestration: Integrating Biology and Climate Through Systems Science. DOE/

SC-108 (http://genomicscience.energy.gov/carboncycle/).

U.S. DOE. 2009. U.S. Department of Energy Office of Science 

Systems Biology Knowledgebase for a New Era in Biology: A 

Genomics:GTL Report from the May 2008 Workshop, DOE/

SC-113, U.S. Department of Energy Office of Science 

(http://genomicsgtl.energy.gov/compbio).

Vaziri, A., et al. 2008. “Multilayer Three-Dimensional Super 

Resolution Imaging of Thick Biological Samples,” Proceedings of the National Academy of Sciences of the United States of 

America 105(51), 20221–26.

VerBerkmoes, N. C., et al. 2009. “Systems Biology: 

Functional Analysis of Natural Microbial Consortia Using 

Community Proteomics,” Nature Reviews Microbiology 7, 

196–205. doi:10.1038/nrmicro2080.

Veysey, J., et al. 2008. “Reconstruction of Water Temperature, pH, and Flux of Ancient Hot Springs from Travertine 

Depositional Facies,” Journal of Sedimentary Research, 

78(2), 69–76.

Vinson, V., and G. Chin. 2007. “Lights, Camera, Action,” 

Science 316(5828), 1143–58. Special issue on single molecules. doi: 10.1126/science.316.5828.

Warnecke, F., et al. 2007. “Metagenomic and Functional 

Analysis of Hindgut Microbiota of a Wood-Feeding Higher 

Termite,” Nature 450, 560–65.

Woese, C. R. 2004. “A New Biology for a New Century,” 

Microbiology and Molecular Biology Reviews 68, 173–86.

Xie, X. S., et al. 2008. “Single-Molecule Approach to 

Molecular Biology in Living Bacterial Cells,” Annual Reviews 

of Biophysics 37, 417–44.

Yu, J., et al. 2006. “Probing Gene Expression in Live Cells 

One Protein Molecule at a Time,” Science 311, 1600–03.
54 New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009
October 2009 Department of Energy Office of Science New Frontiers in Characterizing Biological Systems 55

Appendix 4

Figure Credits 

Cover Images

Ecosystem image courtesy of U.S. Department of Agriculture Natural Resources Conservation Service. Microbial 

community image © Simko/Visuals Unlimited. Plant tissue image (switchgrass stem cross-section) courtesy of 

S. Ding, DOE BioEnergy Science Center. Cellular systems image of E. coli protein expression from Greenfield et 

al. 2009. Cellular systems image of switchgrass plant cell walls using atomic force microscope courtesy of S. Singh 

and B. Simmons, DOE Joint BioEnergy Institute. Molecular and cellular systems image showing protein complexes courtesy of M. Biggin, Lawrence Berkeley National Laboratory.

Figure 2.2

RuBisCO image from PDB ID: 9RUB, Lundqvist, T., and G. Schneider. 1991. “Crystal Structure of Activated 

Ribulose-1,5-bisphosphate Carboxylase Complexed with Its Substrate, Ribulose-1,5-bisphosphate,” Journal of 

Biological Chemistry 266(19), 12604–611. Bacterial ribosome image courtesy of H. Noller, Center for Molecular 

Biology of RNA, University of California, Santa Cruz. Bacterial cellulosome image from Y. Bomble, M. Crowley, and M. Himmel, DOE BioEnergy Science Center. Carboxysome image courtesy of D. Morris and G. Jensen 

and reprinted from Journal of Molecular Biology 372, Iancu, C., et al., “The Structure of Isolated Synechococcus

Strain WH8102 Carboxysomes as Revealed by Electron Cryotomography,” 764–73, © 2007, with permission 

from Elsevier. Synechocystis image from W. Vermaas, Arizona State University. Bacterial periplasm image and 

Shewanella bacteria image from A. Dohnalkova, Environmental Molecular Sciences Laboratory, DOE Pacific 

Northwest National Laboratory. Plant cell-wall thickness image courtesy of S. Jung and A. Ragauskas, Georgia 

Tech, DOE BioEnergy Science Center. Chloroplast image © G. Chapman/Visuals Unlimited. Mitochondrion 

image © H. Berg/Visuals Unlimited. Plant cell image © M. Powell/Visuals Unlimited.

Figure 3.2

Ecosystem image courtesy of U.S. Department of Agriculture Natural Resources Conservation Service. Microbial community image from micro*scope, http://microscope.mbl.edu. Plant cell image © M. Powell/Visuals 

Unlimited. Cyanobacterial cell image from W. Vermaas, Arizona State University. Carboxysome image courtesy 

of D. Morris and G. Jensen and reprinted from Journal of Molecular Biology 372, Iancu, C., et al., “The Structure 

of Isolated Synechococcus Strain WH8102 Carboxysomes as Revealed by Electron Cryotomography,” 764–73, 

© 2007, with permission from Elsevier. Protein complex image from PDB ID: 9RUB, Lundqvist, T., and 

G. Schneider. 1991. “Crystal Structure of Activated Ribulose-1,5-bisphosphate Carboxylase Complexed with 

Its Substrate, Ribulose-1,5-bisphosphate,” Journal of Biological Chemistry 266(19), 12604–611.
56 New Frontiers in Characterizing Biological Systems Department of Energy Office of Science October 2009
 Printed with soy ink on recycled paper

atomic force microscopy

DOE Office of Biological and Environmental Research

catalyzed reporter deposition–fluorescence in situ hybridization

deoxyribonucleic acid

Department of Energy

elemental labeling–fluorescence in situ hybridization

electron microscopy

flux balance analysis

fluorescence in situ hybridization

fluorescence resonance energy transfer

infrared radiation

microautoradiography–fluorescence in situ hybridization

metabolic flux analysis

nuclear magnetic resonance imaging

mass spectrometry

nano–secondary ion mass spectrometry

nuclear magnetic resonance

photoactivated localization microscopy

positron emission tomography

ribonucleic acid

ribulose-1,5-bisphosphate carboxylase oxygenase

stimulated emission depletion

stochastic optical reconstruction microscopy

ultraviolet

AFM 

BER 

CARD-FISH 

DNA 

DOE 

EL-FISH 

EM 

FBA 

FISH 

FRET 

IR 

MAR-FISH 

MFA 

MRI 

MS 

nanoSIMS 

NMR 

PALM 

PET 

RNA 

RuBisCO 

STED 

STORM 

UV 

Acronym List

Reference Table of 

Numeric Prexes

1024 yotta

1021 zetta

1018 exa

1015 peta

1012 tera

109 giga

106 mega

103 kilo

100

10−3 milli

10−6 micro

10−9 nano

10−12 pico

10−15 femto

10−18 atto

10−21 zepto

10−24 yocto