Roundtable
Empowering 21st Century Biology
GENE E. ROBINSON, JODY A. BANKS, DIANNA K. PADILLA, WARREN W. BURGGREN, C. SARAH COHEN,
CHARLES F. DELWICHE, VICKI FUNK, HOPI E. HOEKSTRA, ERICH D. JARVIS, LORETTA JOHNSON, MARK Q.
MARTINDALE, CARLOS MARTINEZ DEL RIO, MONICA MEDINA, DAVID E. SALT, SAURABH SINHA,
CHELSEA SPECHT, KEVIN STRANGE, JOAN E. STRASSMANN, BILLIE J. SWALLA, AND LARS TOMANEK
Several lists of grand challenges in biology have been published recently, highlighting the strong need to answer fundamental questions about how
life evolves and is governed, and how to apply this knowledge to solve the pressing problems of our times. To succeed in addressing the challenges
of 21st century biology, scientists need to generate, have access to, interpret, and archive more information than ever before. But for many
important questions in biology, progress is stymied by a lack of essential tools. Discovering and developing necessary tools requires new technologies, applications of existing technologies, software, model organisms, and social structures. Such new social structures will promote tool building,
tool sharing, research collaboration, and interdisciplinary training. Here we identify examples of the some of the most important needs for
addressing critical questions in biology and making important advances in the near future.
Keywords: ecology, genomics, bioinformatics, cell biology, biological infrastructure
B
iology is confronted with the need to answer fundamental
questions about how life and natural systems evolve, are
governed, and respond to changing environments. We need
to understand the basic biological processes that drive life
on this planet—those common to all organisms as well as
those that provide unique adaptation to different environments. We also urgently need to identify all the life forms
on this planet and understand their interrelationships and
geographic distributions.
Biology must also apply new and existing knowledge to
solve the pressing problems of our times, which include the
environmental crises of global climate change, ocean acidification, biodiversity loss and the introduction of nonnative
species, serious concerns for human health, emerging and
pandemic diseases, and critical needs for agricultural and
biofuel production. The urgency of these fundamental and
practical needs has prompted scientists to begin to identify
sets of “grand challenges” in biology (Denver et al. 2009,
NRC 2009, Satterlie et al. 2009, Schwenk et al. 2009).
To succeed in addressing the challenges of 21st century
biology, scientists must generate, have access to, interpret,
and archive more information than ever before. This effort
will involve analyses that span scales of time and space, from
decoding information from genomes to extracting information from the environment on how organisms survive
and reproduce (NRC 2009). Scientists need to learn how
complex biological systems work across levels of organization, from cells to ecosystems, and across time scales, from
the millisecond response of neural systems to the long-term
response of evolutionary change. We need to be able to
trace the effects of changes in DNA sequence or epigenetic
regulation on multiple organismal phenotypes, understand
how these changes affect ecological relationships, and have
sufficient examples of these to begin to articulate new
theories of organismal function and evolution. Addressing
the challenges of 21st century biology requires integrating
approaches and results across different subdisciplines of
biology, such as genetics, development, physiology, ecology,
and evolution, as well as technologies, information, and
approaches from other disciplines, such as engineering,
computer science, physics, chemistry, mathematics, and the
geological and atmospheric sciences (figure 1).
However, biologists do not have the tools required to achieve
this vision. For many important questions in biology, progress
is stymied by a lack of the essential instruments to make rapid
advances. In some cases, certain devices or technologies exist in
other fields but are currently unavailable to biologists. In other
cases, we need tools that scientists have not yet imagined. Developing those tools may require new technologies, applications
of existing technologies, software, model organisms, and social
structures to promote tool building, tool sharing, research collaboration, and interdisciplinary training. This article presents
examples of what we believe to be the most important needs for
tools to address critical questions in biology. We focus on the
tools and the social structures needed to enable such tools; for
an in-depth treatment of biology’s grand challenges, see Denver
and colleagues (2009), the National Research Council report A
New Biology for the 21st Century (2009), Satterlie and colleagues
(2009), and Schwenk and colleagues (2009).
Tools
Researchers need tools to enable high-throughput acquisition
and synthesis of information at all levels of the hierarchy of
biological organization, and across all biologically relevant
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Figure 1. Tools for 21st century biology. To solve grand
challenges, biology is becoming increasingly integrated across
levels of organization, over different spatial and temporal
scales, and it has become allied with other disciplines.
Twenty-first century biology requires new tools that involve
new technologies; new applications of existing technologies;
new adaptations of tools from established model organisms
to new models; new software; new model organisms; and
new social structures to promote tool building, tool sharing,
research collaboration, and interdisciplinary training.
spatial and temporal scales. These include technologies, software, and devices related to “omics”; informatics and systems
biology; sensors and imaging; and information archiving.
Omics, informatics, and systems biology. The ability to
sequence the genomes of microbes, plants, and animals has led to
remarkable advances in biology. But this “first genomic revolution” has been based on the genome sequences of only a relatively
small number of organisms: hundreds of microbes, and just a
few dozen plant and animal species (www.genomenewsnetwork.
org/). The relentless push to lower DNA sequencing costs for
biomedical purposes will continue, and will soon make it
possible to sequence the genomes of most species of interest for
any biological question. Lower sequencing costs will usher in a
“second genomic revolution,” having a transformative effect on
all areas of biology because genome sequence information can
be used to illuminate questions at all levels of biological organization; we present just a few examples here.
DNA-based tools can have profound interdisciplinary
impacts, beginning with faster and cheaper field identification of species and extending to assessments of genomewide
patterns of genetic variation in different environments to
determine what allows or limits the ability of individuals to
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adapt to changing environments. Planning for the sequencing of 10,000 different vertebrate species’ genomes has already
begun (Haussler et al. 2009), and similar plans will certainly
emerge for invertebrates and plants. In some cases, there also
will be genome sequences for thousands of individuals belonging to the same species (Kuehn 2008, Weigel and Mott 2009).
We envision many such projects for species that are important
models in the laboratory or field, play particularly important
ecological roles in different environments or that are situated
at critical branch points in phylogeny.
Insights into the mechanisms and evolution of organisms can be gained with “ancient DNA” from specimens of
archeological or historical importance (Millar et al. 2008),
and from specimens collected over centuries and held in
museums or natural science collections. Ancient DNA can
be used to study major evolutionary patterns and diversification, extinction, and temporal changes in genetic variation—studies that compare Neanderthal and Homo sapiens
genomes, for example (Green et al. 2006). Ancient DNA also
could be used to understand molecular responses to past
climate changes, and thus help predict potential responses in
the future, but better tools are needed to facilitate the study
of DNA. Improved DNA processing techniques that allow
sequenceable DNA to be obtained from historic samples
despite suboptimal preservation are needed to engender
ever-more creative uses of ancient DNA in the future.
Metagenomics is revolutionizing the study of microbial
ecology, from identifying new microbial species, strains, and
genes to describing microbial communities associated with
different parts of the human body. It is not far fetched to
imagine the ability to extend this approach to eukaryotes,
especially small ones. For example, strategically placed insect
traps that feed into an automated metagenomic sequencing
and informatics pipeline could be used to monitor outbreaks
of agricultural pests or vectors of human disease.
New laboratory and computational tools also are required
to leverage genome sequencing for a comprehensive omics
revolution. Researchers need improved technologies for
high-throughput interrogation of transcriptomes (including
all forms of RNA transcripts) and novel methods for highthroughput in situ hybridization to precisely map changes in
gene expression in order to illuminate our understanding of
key biological processes.
To understand the complexity of biochemical processes
in a living cell or organism, technologies to acquire comprehensive profiles of other molecules—such as metabolites
(metabolomics), proteins (proteomics), elements (ionomics),
and the stable isotopic composition of organisms—are just
as important as genomic tools. The information obtained
can be applied to a variety of problems, including the development of new drugs, improvements in human nutrition,
and understanding the impacts of climate change.
Biologists need new bioinformatics methods to determine
and compare genomes and functions across individuals and
taxa, and to find and synthesize meaningful patterns within
the floods of genomic data that will soon appear. New software
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should be easily accessible to biologists, and computer programs
should not require researchers to have extensive programming
skills to use large, integrated data sets of genomic, phenotypic,
phylogenetic, ecological, and environmental information for
in silico hypothesis testing and discovery. There is a need for
cyberinfrastructure composed of databases, communication
protocols, and computational services designed to help make
data and computational tools more usable for biologists
(Giardine 2005, Stein 2008). And underlying this new cyberinfrastructure must be ever-more powerful computers to provide accessible high-end computing.
The impending omics revolution will require that traditional comparative genomics methods, which have served
well in studies with few genes or genomes, give way to new
visualization and analytical approaches that simultaneously
can use thousands of genome sequences. The availability of
thousands of genome sequences should make it possible to
devise new algorithms to better detect orthologs in distantly
related species in order to study the evolution of complex
traits, such as flowering, development, and social behavior,
or to discover new mechanisms of biofuel production.
Contemporary software is needed to extract and synthesize genomic data so that DNA sequences become, in essence,
a new repository of biological information. Imagine an environmental biologist in need of a model species to study the
risks of a certain type of environmental toxicant posing the
following question: What species are particularly vulnerable
to this environmental toxicant? This question prompts the
software to perform an automatic knowledge-based search
to identify genes from the literature known to encode the
relevant detoxifying enzyme, using powerful text-mining
algorithms in development (Muller et al. 2004, Ling et al.
2007). If an answer were available, then the program would
conduct a Web search of all genome sequences to find and
report on those species that lack the corresponding gene or
genes, or those individuals within a species with particular
single-nucleotide polymorphisms. Not all the information
returned from such a search would be useful; for example,
there is a variety of technical reasons a gene may be missing
from an assembly of a particular genome. But the ability to
move seamlessly between genome content and higher-level
biological questions will provide biologists with a novel and
powerful means of extracting information from genomes.
Systems analyses at all levels of biological organization
promise to provide an innovative framework for understanding complex traits, including those at the molecular level.
This will help illuminate how variation in genotype is related
to variation in phenotype. Scientists will need new bioinformatic tools to integrate omics data into sophisticated models
of gene and protein regulatory networks. To study how
organisms adapt to environmental change, bioinformatics
will need to be able to integrate the information derived from
regulatory, signaling, and metabolic networks (Hyduke and
Palsson 2010) with other types of phenotypic and population
genetic data, ideally obtained across diverse environmental
conditions. Automated methods of information analysis
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are sorely needed, as are user-friendly software programs
that facilitate the integration of data from multiple levels of
biological organization across spatial and temporal scales,
and that lead to predictive models for specific conditions.
Biologists must be able to navigate by moving easily up and
down the biological hierarchy from micro- to macroscales.
Investigators also need user-friendly modeling tools with
algorithms that can infer causal relationships from large data
sets to help them develop hypotheses.
Scientific databases and literature must be more interactive
and dynamic. Improved forms of text mining, already in development, employ statistical literature analyses to help identify
new biomarkers for human disease (Shi et al. 2008) or explore
how genes influence social behavior (Ling et al. 2007).
Sensors. Progress in many areas of biology depends on learn-
ing how genotypes generate specific phenotypes, how these
relationships vary with environmental conditions, and how
these relationships have evolved (Houle 2009). This requires
new devices to measure organisms in their environment.
Researchers need devices to enable acquisition of phenotypic
and performance information that can be matched to genotypic
and environmental information at all levels of the hierarchy
of biological organization. Moreover, this information must
be obtained under the vast array of natural and biological
conditions in which organisms live and have evolved, and at
biologically relevant spatial and temporal scales.
On a microscale, biologists need devices to continuously
record the activity of cellular components as they interact
naturally in living cells; on a macroscale, they need devices to
continuously record the activity and performance of organisms and their component systems as they interact naturally in
their environment. This instrumentation must be cost effective,
miniaturized, and deployable in large numbers to continuously
collect and transmit data in diverse environments, on small and
large spatial scales. Automated image acquisition and shaperecognition software could permit the deployment of “smart”
sensors that obtain information from specific organisms, both
microscopic and macroscopic, and their environments, in real
time. Stable isotopes already are being used as “natural sensors”
because some enzymes (including rubisco and carbonic anhydrase) and biogeochemical processes (including temperature
and precipitation) affect the isotopic profiles of organisms
in characteristic ways. “Isoscapes,” made from isotope analysis coupled with geographic information systems (GIS), are
starting to reveal the relative importance of key physical and
biological processes at continental scales (Bowen 2009). But isotopic analysis of biological materials is slow and labor intensive,
and high-throughput methods are needed for this approach to
realize its full potential. Advanced radio-tracking technologies
will help with the study of dispersal and migration of small
animals, such as bats, insects, and songbirds. It is expected that
major innovations in radio-tracking technologies will make
possible important new insights into conservation biology,
climate change effects, and the spread of infectious disease
(Wikelski et al. 2007).
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Earth’s diverse environments also need to be more intensely
monitored if scientists are to appropriately contextualize new
knowledge about an organism’s physiological and behavioral
responses. Researchers require new technology to cheaply
monitor and measure many environmental parameters,
including pH, temperature, conductivity, wind force, water
flow rates and directions, dissolved oxygen, and mineral nutrient content, at biologically relevant scales, under controlled
conditions and in the field, in real time. This information
will help answer fundamental questions in biology that relate
to genotype-phenotype linkages; it also is critical to understanding and predicting anthropogenic effects on natural
resources and the impacts of climate change. It is hoped that
the National Ecological Observatory Network (NEON; www.
neoninc.org), funded by the US National Science Foundation
(NSF), will provide large-scale terrestrial environmental data
in the near future. Integrating NEON-type information with
the above-mentioned measuring devices can then occur,
enabling biologists to obtain information on individual
organisms and to further study the impacts of climate change
at all levels of biological organization.
Many forms of sophisticated sensory instrumentation and
technology already exist, but they are not useful for discovering
the linkages between genotypes, phenotypes, and the environment because of problems of scale. Many biological processes
and environments are much smaller than current technology
can measure. Miniaturization of instrumentation is critical for
our understanding of basic life processes. Microfluidic devices
are already starting to transform analyses of genomes, cells,
and tissues in the laboratory (Whitesides 2006). These devices
need to be adapted for wider use under an array of conditions,
including in natural environments. For example, “mini–mass
spectrometers” already exist that can be placed below sea level
to study the adaptation of diverse forms of life to extreme environmental conditions (Bell et al. 2007). Further development
of these technologies for a wide range of field-based applications could revolutionize real-time monitoring of organisms,
populations, communities, and ecosystems.
In microimaging, further development could make it possible to continuously monitor the 3-D structure of a developing organism, or to record the precise location and structure
of every organism in an environment visible within the field
of view. Raman spectroscopy is being used in the laboratory
to provide detailed chemical analysis of specimens of ancient
bone, shell, and teeth (Freudiger et al. 2008, Grant 2009).
These and other imaging technologies need to be miniaturized, easy to use, portable, and cost effective. Such improvements would allow scientists to examine organisms in nature
in detail, which is essential for making breakthroughs in fields
such as sustainable agriculture, forestry, and conservation.
It also would be possible to selectively sample organisms of
interest and perform real-time, nondisruptive population
monitoring, such as measuring the spread of invasive and pest
species, including those that carry human disease. Discoveries could be made of the mechanisms that drive system and
population resilience in the face of natural and anthropogenic
disturbance or climate change.
In macroimaging, more user-friendly remote sensing and
GIS would allow biologists to gain access to georeferenced
data of all types. One widely used mapping tool is Google
Maps, which has demonstrated the possibilities of using
remote sensing and GIS technology to scientists for a variety
of research purposes. Remote sensing and GIS technology can
be improved in spatial resolution and image quality, and by
the capability to integrate different types of data sets, including biological, geological, and topographical information
(Makris et al. 2009). These innovations would increase the
effectiveness of spatial modeling and habitat prediction algorithms to more accurately predict the spread of invasive or
pathogenic species and the consequences of land-use change,
for example, on local or global ecosystem scales. Improved
GIS modeling and mapping could also help scientists predict
when and where a new disease might emerge by revealing
places where human hosts and certain animal vector species
are in close proximity and at high densities. Better remote
sensing and GIS predictive tools are especially needed to study
the more remote regions of the world.
Imaging. Biologists need handheld personal imaging systems
that can be used or deployed in the field. Such imaging
technologies would allow scientists to examine organisms in
nature in detail, which is essential for making breakthroughs
in fields such as sustainable agriculture, forestry, and conservation. These devices are now feasible, thanks to advances in
real-time imaging. New methods, such as multilens cameras
that allow post hoc adjustment of focus and depth of field and
three-dimensional (3-D) image reconstruction (Bimber 2006),
permit imaging in ways that are qualitatively different from
what can be done with commonly used instruments. Deconvolution imaging makes it possible to overcome the limits of
numeric aperture to produce images with resolution or depth
of field that exceeds what is possible with a single image (Angel
and Fugate 2000). Because these methods permit capture and
integration of multiple traditional images into a single construct, they blur the line between image and computer model.
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Information archiving. Even now, our ability to acquire biologi-
cal data far outstrips our ability to store it in reliable and easily
retrievable formats. This is true for all types of data, from
genome sequence information to archived museum specimens,
to the wealth of environmental data being collected. Biologists
need modern methods of archiving, sharing, and accessing
data. These needs will become even more acute with biology
poised to acquire unprecedented amount of data, including
reference data and reference specimens. In addition, researchers
need easy access to the information, online or in person.
There is a need for improved software tools for deployment
in online databases, seed banks, stock centers, museums, and
other repositories of biological information. To be most useful,
these repositories need to be curated, and must be replete with
and searchable by different types of information (e.g., for organism specimens, DNA, species, time, and place of collection).
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Such an update will require formalized ontologies for analytical data at all levels of biological organization (Ashburner et al.
2000), and formalized methods of recording metadata that
describe the analytical data. Integrating and maintaining older
legacy data poses other sets of challenges in the digital era.
New information technology is required to facilitate database creation. This software includes programs to facilitate
uploading newly acquired data to centralized storage locations and programs that automate the process of creating
and maintaining community-specific databases, such FlyBase
(www.flybase.org) or WormBase (www.wormbase.org), which
historically have required extensive, and increasingly prohibitively expensive, manual curation.
To effectively use different types of data to address a common problem, researchers need new tools for data integration across databases with different formats or that reside
in different locations. For example, with the profusion of
genome sequences expected to come in the near future, it
is likely that an increasing number of sequences will reside
only on the computer servers of individual laboratories
rather than in centralized repositories like GenBank (www.
ncbi.nlm.nih.gov/Genbank/), so it is imperative to develop
software that can locate all the genome sequences in order to
extract meaning from them. All of these needs again underscore the need for accessible high-end computing.
Model organisms
Over the past several decades, research efforts on plants and
animals have increasingly focused on only a handful of model
genetic organisms, especially thale cress (Arabidopsis thaliana),
fruit fly (Drosophila melanogaster), worm (Caenorhabditis
elegans), and mouse (Mus musculus). These species are especially useful for laboratory studies because they are relatively
small bodied, have short generation times, can be maintained
in the laboratory, and are comparatively easy to breed. An
extensive array of genetic tools has been developed for these
species, including finished genome sequences and advanced
mutant and transgenic technology, such as transformation systems with control over spatial and temporal patterns of gene
inactivation. However, these few species are not representative
of the vast diversity of life. Twenty-first century biology would
greatly benefit from a broader array of model organisms,
including species not yet widely studied, to address a full range
of important biological questions, especially those related to
evolution and adaptation (Abzhanov et al. 2008, Brown et al.
2008, Behringer et al. 2009). But many organisms used to study
evolution and adaptation lack the genetic and genomic tools
necessary for the most rapid progress in scientific research.
It is now necessary to expand the number of model species so that a broader collection of questions can be studied
effectively and efficiently. To accomplish this goal, scientists
must use new model species to their fullest potential, using
methods that work for a variety of species.
New ways are needed to facilitate the development of forward and reverse genetic techniques for the analysis of gene
function. Viral vectors have been used to overexpress genes in
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a few animal species (Donaldson et al. 2008); innovations in
viral modification could extend this technique more broadly.
Gene knockdowns mediated by RNA interference (RNAi) are
a powerful reverse genetics tool to analyze gene function, but
this works better in some species and tissues than in others.
The RNAi method also is limited by problems of delivery,
knockdown efficiency, and artifacts resulting from off-target
effects, and, in animals, innate immune responses. Enhancing
the efficacy of RNAi across tissues and species, with methods
that transfer easily across species, is an important goal. One
critical component of empowering novel model organisms is
the development of methods for genetic transformation that
transfer easily from species to species. In animals, transposable elements such as PiggyBac show promise as vectors for
transformation that can work on a broad variety of species, but
additional research is required to build transformation systems
that work efficiently for many species (Wu et al. 2007).
Improved proteomic and metabolomic technologies make
it possible to use species as models for environmental-change
research even without fully sequenced and annotated genomes
(Epperson et al. 2004, Tomanek 2010). These tools would
empower new model organisms for physiological, developmental, behavioral, ecological, and evolutionary research.
A key issue is whether new tools should be developed on a
species-specific basis, or whether they can be applied broadly
across taxa. Species-specific tools will always require significant
investment, and involve careful justification of which species to
choose (Mandoli and Olmstead 2000, Jenner and Wills 2007).
We suggest that emphasis be placed on species that can be used
to understand key evolutionary patterns and important biological phenomena that present the most immediately pressing
need. Additionally, because we anticipate that some of the most
significant advances in biological research in this century will
involve integration across levels of biological organization and
across scales of time and space, we also suggest a special focus to
empower new model organisms that allow for such integration.
It is hoped that as new technologies are developed, costs will
lower, facilitating further development of an ever-increasing
number of model organisms. If these goals are achieved, the
designation of “model” organism will become less relevant over
time, as more and more species will be accessible to investigation at multiple levels of biological organization.
People power
To make the most progress in 21st century biology, a scientific research culture that nurtures creativity, encourages and
promotes tool building and sharing, and rewards scientists
accordingly is essential. In addition, as biology becomes more
and more interdisciplinary and information based, new training
paradigms must prepare the next generation of biologists and
tool builders. With the inter- and transdisciplinary development
and use of tools comes the challenge of cross-disciplinary communication. This challenge is particularly vivid, for example,
when computer scientists and biologists come together to
develop and use bioinformatic tools. Bioinformatics must
facilitate networking and the formation of virtual communities,
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resources for those who can “translate” across disciplines, and
institutional mechanisms that recognize and reward the work
and time needed to bridge communication across disciplines
and transfer knowledge and technology.
Tool building, tool sharing, and collaboration. Tools with trans-
formational potential have been, and will continue to be,
developed by inspired and highly motivated individual scientists and engineers. We advocate the creation of more collaborative mechanisms to enhance and facilitate the process
of tool building, and offer a few suggestions here.
Many of us lament the limited opportunities for interactions on our own campuses between biologists and those in
other fields, such as engineering. Engineers are frequently
unaware of biologists’ needs, and biologists do not always
know what technologies engineers have already produced
or invented. Similarly, engineers are not always familiar with
the solutions that biological systems provide for a variety of
engineering problems. Workshops that bring together engineers and biologists serve as a catalyst for innovative tool
development and could help remedy this issue.
National or regional facilities could serve as tool-development
incubators or tool-dissemination sources. For example, a center
that brings together engineers and biologists could play a crucial
role in the design, fabrication, testing, and use of microfluidic
devices for both the laboratory and the field. Another center,
involving a different mix of researchers, including perhaps
geneticists, developmental biologists, and physiologists, could
be formed to develop universal techniques of transgenesis. In
some cases, innovation could arise from breaking down communication barriers between fields so that problems can be
clearly seen from different disciplinary perspectives.
Interdisciplinary centers might be in physical locations, connected with universities or independent research institutes that
serve as focal points to bring people together for periods of
time. Examples of highly successful centers include the NSFsponsored National Center for Ecological Analysis and Synthesis and the National Evolutionary Synthesis Center (NESCent)
(Carpenter 2009). Field research stations and laboratories can
be particularly effective venues for extended and informal
cross-disciplinary interaction (Carpenter 2009).
Virtual interdisciplinary centers with geographically
dispersed partnerships will help make transformations in
biology. Virtual centers could be particularly useful for
the development of some of the new bioinformatic tools
outlined above. Funding mechanisms that encourage the
development of virtual communities to address particular
biological problems have seen strong success; for example, the National Institutes of Health–funded Glue Grants
(www.nigms.nih.gov/Initiatives/Collaborative/GlueGrants/),
NSF-funded Research Coordination Networks (www.nsf.gov/
funding/pgm_summ.jsp?pims_id=11691), and the NSF iPlant
initiative (www.iplantcollaborative.org/). iPlant is specifically
designed to address the development of cyberinfrastructure
to facilitate solutions to grand challenges in the plant sciences.
The nanoHUB (http://nanohub.org/) is a virtual center that
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distributes newly developed computational tools through
easily navigated interfaces to users at all levels of computer
sophistication. It is easy to imagine additional virtual centers
developing around new model organisms, technologies, or
ways of integrating across levels of biological organization.
One highly successful social structure in science is the
research community. Unlike hard disciplinary boundaries,
research communities are self-assembling and dynamic,
and often cross delineations of study. Clearly, a sense of
community and wanting to belong is not just a human
characteristic but also a desirable motivating force in science. Biological communities are often structured around
organisms (e.g., model organisms), systems (e.g., plant
communities), disciplines (e.g., comparative physiologists,
biomechanics, and functional morphologists), or fields of
interest (evolutionary development). Perhaps tool development can be facilitated by creating new research communities. Will important new advances be made if research communities are organized around tools or major problems,
rather than the organisms or systems they study? This is an
experiment worth trying.
New forms of networking in science, fueled by innovations in communication technology that operate on increasingly short time scales, can also contribute to changes in
social structure to facilitate 21st century biology. Scientists
use a growing number of networking tools for research and
public outreach, including Google, as well as Drupal.org,
Epernicus, Facebook, LinkedIn, MyExperiment.com,
SciVee.tv, Skype, Twitter, and YouTube. These socialnetworking tools are showing promise in facilitating just
the kinds of social processes that 21st biology requires. They
provide friendly and informal environments that can aid the
sharing of both tools and data. For example, Epernicus is a
social-networking Web site and professional networking platform resource built by scientists to help scientists find the right
people with the right expertise at the right time. Networking
tools should also prove useful in developing new research communities and supporting new training opportunities.
New institutional incentives are also needed to enable
building and sharing tools for biology. Although some toolbuilders have been amply rewarded for their efforts, including
with Nobel Prizes, the dominant motif in scientific research is
discovery. The collective development of a new tool may not
result in a publication in a high-impact journal, but a finding
made with it might. Institutions need new ways of evaluating
and recognizing collaborative efforts. Academic collaboration
has long been valued in the abstract—most scientists recognize that the outcome of a successful collaboration is usually
more than just the sum of individual parts, but traditional
metrics of recognition favor individual achievement. Funding agencies and academic institutions have taken steps to
incentivize scientists to form productive collaborative teams
of researchers by establishing specific grants mechanisms
for collaboration and creating interdisciplinary institutes,
respectively. We expect these important paradigm-changing
efforts to intensify in the future.
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Training. Training paradigms are essential for preparing young
biologists for the more extensive research collaboration that is
needed for interdisciplinary work, and for providing them with
the quantitative skills and broad perspectives necessary for success. Tomorrow’s biologists must have training across genetics,
development, biochemistry, physiology, ecology, and evolution, as well as experience working across different disciplines.
More important, they also must have conceptual and quantitative training in mathematics and computer science to integrate
these domains of knowledge using new computational tools.
Wake (2008) emphasized the importance of training students
early in their careers to not only think independently but also
to work effectively in team-based scientific research.
Innovative training programs couple training with research
in a team-based, problem-driven format. Undergraduatefocused universities provide opportunities for students to
design and implement experiments and then analyze and
present their results while receiving guidance and support
from professors as well as peers. Innovative undergraduate
training programs exist (Pevzner and Shamir 2009), and
some undergraduates already are annotating genomes and
using mass spectrometers for environmental metabolomic
projects, for example. Investing more in research-intensive
training programs at a variety of undergraduate-focused
universities will increase the size of a diverse and highly
motivated graduate student pipeline.
Conclusions
This article identifies some tools that are critically needed
for biology to answer fundamental questions about how life
evolves and is governed, as well as tools to apply this knowledge to solve the pressing problems of our times. We have
tried to highlight possibilities for tools that integrate and
affect disciplines and those that allow scientists to work across
levels of biological organization—these will likely have the
strongest influence on 21st century biology. We have outlined
steps necessary to create the culture and social and educational structures that will facilitate and nurture tool development and toolmakers now and in the future. Scientists require
more than new technologies, devices, and software; they also
need to create and support a culture of science and education
that stimulates and nurtures creativity, supports potential
toolmakers, and trains the next generation of engineers. Many
tools not yet imagined might make possible the next revolutionary biological discoveries; they might enable scientists
to study remote areas of the world or reach and integrate
underserved and underrepresented groups in science, thus
encouraging progress toward common societal values for human health and the natural environment.
Acknowledgments
We thank the National Science Foundation for supporting
the workshop that led to this article, Letitia Cundiff for
assistance in arranging the workshop and preparing the
manuscript, and two anonymous reviewers for suggestions
that improved the manuscript.
www.biosciencemag.org
References cited
Abzhanov A, Extavour CG, Groover A, Hodges SA, Hoekstra HE, Kramer
EM, Monteiro A. 2008. Are we there yet? Tracking the development of
new model systems. Trends in Genetics 24: 353–360.
Angel R, Fugate B. 2000. Astronomy-adaptive optics. Science 288: 455–456.
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H. 2000. Gene ontology:
Tool for the unification of biology. Nature Genetics 25: 25–29.
Bell RJ, Short RT, van Amerom FHW, Byrne RH. 2007. Calibration of an in
situ membrane inlet mass spectrometer for measurements of dissolved
gases and volatile organics in seawater. Environmental Science and
Technology 41: 8123–8128.
Behringer R, Johnson AD, Krumlauf RE, eds. 2009. Emerging Model Organisms: A Laboratory Manual. Cold Spring Harbor Laboratory Press.
Bimber O. 2006. Computational photography: The next big step. Computer
39: 28–29.
Bowen GJ, et al. 2009. Isoscapes to address large-scale Earth science challenges.
Eos, Transactions of the American Geophysical Union 90: 109–110.
Brown FD, Prendergast A, Swalla BJ. 2008. Man is but a worm: Chordate
origins. Genesis 46: 605–613.
Carpenter SR, et al. 2009. Accelerate synthesis in ecology and environmental
sciences. BioScience 59: 699–701.
Denver RJ, Hopkins PM, McCormick SD, Propper CR, Riddiford L, Sower
SA, Wingfield JC. 2009. Comparative endocrinology in the 21st century.
Integrative and Comparative Biology 49: 339–348.
Donaldson ZR, Young LJ. 2008. Oxytocin, vasopressin, and the neurogenetics
of sociality. Science 322: 900–904.
Epperson LE, Dahl TA, Martin SL. 2004. Quantitative analysis of liver
protein expression during hibernation in the golden-mantled ground
squirrel. Molecular and Cellular Proteomics 3: 920–933.
Freudiger CW, Min W, Saar BG, Lu S, Holtom GR, He C, Tsai JC, Kang JX,
Xie XS. 2008. Label-free biomedical imaging with high sensitivity by
stimulated Raman scattering microscopy. Science 322: 1857–1861.
Giardine B, et al. 2005. Galaxy: A platform for interactive large-scale
genome analysis. Genome Research 15: 1451–1455.
Grant B. 2009. Sample, don’t trample. The Scientist 23: 20–22.
Green RE, et al. 2006. Analysis of one million base pairs of Neanderthal
DNA. Nature 444: 330–336.
Haussler D, et al. 2009. Genome 10K: A proposal to obtain whole-genome
sequence for 10,000 vertebrate species. Journal of Heredity 100: 659–674.
Houle D. 2009. Numbering the hairs on our heads: The shared challenge
and promise of phenomics. Proceedings of the National Academy of
Sciences 107 (suppl. 1): 1793–1799.
Hyduke DR, Palsson BØ. 2010. Towards genome-scale signalling-network
reconstructions. Nature Reviews Genetics 11: 297–307.
Jenner RA, Wills MA. 2007. The choice of model organisms in evo-devo.
Nature Reviews Genetics 8: 311–319.
Kuehn BM. 2008. 1000 genomes project promises closer look at variation in
human genome. Journal of the American Medical Association 300: 2715.
Ling X, Jiang J, He X, Mei Q, Zhai CX, Schatz B. 2007. Generating gene
summaries from biomedical literature: A study of semi-structured summarization. Information Processing and Management 43: 1777–1791.
Makris NC, Ratilal P, Jagannathan S, Gong Z, Andrews M, Bertsatos I, Olav
RG, Nero RW, Jeck JM. 2009. Critical population density triggers rapid
formation of vast oceanic fish shoals. Science 323: 1734–1737.
Mandoli DF, Olmstead R. 2000. The importance of emerging model systems
in plant biology. Journal of Plant Growth Regulation 19: 249–252.
Millar CD, Huynen L, Subramanian S, Mohandesan E, Lambert DM. 2008.
New developments in ancient genomics. Trends in Ecology and Evolution 23: 386–393.
Muller HM, Kenny EE, Sternberg PW. 2004. Textpresso: An ontology-based
information retrieval and extraction system for biological literature.
PLoS Biology 2: e309.
[NRC] National Research Council. 2009. A New Biology for the 21st Century: Ensuring the United States Leads the Coming Biology Revolution.
National Academies Press.
Pevzner P, Shamir R. 2009. Computing has changed biology: Biology education must catch up. Science 325: 541–542.
December 2010 / Vol. 60 No. 11•BioScience929
Roundtable
Satterlie RA, Pearse JS, Sebens KP. 2009. The black box, the creature from
the black lagoon, August Krogh, and the dominant animal. Integrative
and Comparative Biology 49: 89–92.
Schwenk K, Padilla DK, Bakken GS, Full RJ. 2009. Grand challenges in
organismal biology. Integrative and Comparative Biology 49: 7–14.
Shi L, Perkins RG, Fanh H, Tong W. 2008. Reproducible and reliable
microarray results through quality control: Good laboratory proficiency
and appropriate data analysis practices are essential. Current Opinions
Biotechnology 19: 10–18.
Stein LD. 2008. Wiki features and commenting: Towards a cyberinfrastructure for the biological sciences: Progress, visions and challenges. Nature
Reviews Genetics 9: 678–688.
Tomanek L. 2010. Environmental proteomics: Changes in the proteome
of marine organisms in response to environmental stress, pollutants,
infection, symbiosis and development. Annual Review of Marine
Science. (29 September 2010; www.annualreviews.org/doi/pdf/10.1146/
annurev-marine-120709-142729)
Wake MH. 2008. Integrative biology: Science for the 21st century. BioScience
58: 349–353.
Weigel D, Mott R. 2009. The 1001 genomes project for Arabidopsis thaliana.
Genome Biology 10: 107–111.
Whitesides GM. 2006. The origins and the future of microfluidics. Nature
442: 368–373.
Wikelski M, Kays RW, Kasdin J, Thorup K, Smith JA, Cochran WW,
Swenson GW Jr. 2007. Going wild: What a global small-animal tracking
system could do for experimental biologists. Journal of Experimental
Biology 210: 181–186.
Wu S, Ying G, Qiang W, Capecchi MR. 2007. Toward simpler and faster
genome-wide mutagenesis in mice. Nature Genetics 39: 922–930.
930BioScience•December 2010 / Vol. 60 No. 11
Gene E. Robinson (generobi@illinois.edu) is with the Department of Entomology,
Neuroscience Program, and Institute for Genomic Biology, and Saurabh Sinha is
with the Department of Computer Science, at the University of Illinois at UrbanaChampaign, in Urbana. Jody A. Banks (banksj@purdue.edu) is with the Department of Biology, and David E. Salt is with the Department of Horticulture and
Landscape Architecture, at Purdue University, in West Lafayette, Indiana. Dianna
K. Padilla (dianna.padilla@sunysb.edu) is with the Department of Ecology and
Evolution, at Stony Brook University, in Stony Brook, New York. Warren W. Burggren is with Department of Biological Sciences at the University of North Texas, in
Denton. C. Sarah Cohen is with the Tiburon Center and Department of Biology,
at San Francisco State University, in Tiburon, California. Charles F. Delwiche is
with the Department of Cell Biology and Molecular Genetics, at the University of
Maryland, in College Park. Vicki Funk is with the Department of Botany, at the
Smithsonian Institution in Washington, DC. Hopi E. Hoekstra is with the Department of Organismic and Evolutionary Biology, Museum of Comparative Zoology,
at Harvard University, in Cambridge, Massachusetts. Erich D. Jarvis is with the
Department of Neurobiology at Duke University, in Durham, North Carolina.
Loretta Johnson is with the Division of Biology at Kansas State University, in
Manhattan, Kansas. Mark Q. Martindale is with the Department of Zoology at
the University of Hawaii, in Honolulu. Carlos Martinez del Rio is with the Department of Zoology and Physiology at the University of Wyoming, in Laramie. Monica
Medina is with the School of Natural Sciences at the University of California,
Merced. Chelsea Specht is with the Department of Plant and Microbial Biology at
the University of California, Berkeley. Kevin Strange is with the Department of Anesthesiology at Vanderbilt University, in Nashville, Tennessee. Joan E. Strassmann
is with the Department of Ecology and Evolutionary Biology at Rice University, in
Houston, Texas. Billie J. Swalla is with the Department of Biology at the University
of Washington, in Seattle. Lars Tomanek is with the Department of Biological Sciences at California Polytechnic State University, in San Luis Obispo.
www.biosciencemag.org
C. Sarah Cohen