A New Biology for the 21st Century (2009)

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3 Why Now? The moment is ripe to invest in the development of the new biology because the life sciences are in the midst of a historical period analogous to the early 20th century in the physical sciences. The discovery of the electron in 1897 marked the beginning of a major turning point in the history of sci- ence. Over the next few decades, physics, chemistry, and astronomy were all transformed. Physicists uncovered the fundamental constituents of matter and energy and discovered that these constituents interact in unanticipated ways. Chemists related the structure and properties of substances to the interactions of electrons surrounding atomic nuclei. Astronomers related the light received from stars and the sun to the chemical properties of the atoms generating that light. In this way, new connections within the physical sciences became appar- ent and drove further advances. These theoretical advances also led to practical applications that trans- formed society. Having a “parts list” of the physical world enabled scientists and engineers to develop technologies that would not have been possible without this understanding. These technologies led in turn to the electronic industry, the computer industry, and the information technology industry, which together have created a world that could scarcely have been imagined a century ago. Before the transition associated with the discovery of the electron, scientists gathered increasing amounts of data, but those data could not be put to full use because of the lack of a conceptual framework. After that discovery, previously gathered data took on new usefulness, entirely new areas of inquiry emerged, and discovery and application accelerated rapidly. Such discoveries are critical junctures that send science and society in new directions. These moments of rapid acceleration of scientific progress have different origins. Some occur due to technological advances, such as the invention of the telescope or microscope. Some occur due to conceptual advances, such as the 39 

40 A NEW BIOLOGY FOR THE 21ST CENTURY description of evolution by natural selection or the development of ­relativity theory. In some cases there is a principal driver; in others, multiple factors combine to accelerate progress. New discoveries and new technologies do not guarantee that discovery will accelerate. The world must be ready for change, and the tools and resources must be available to capitalize on new capabilities or knowledge. This committee believes that the life sciences currently stand at such a point of inflection. Drawing ever nearer is the possibility of understanding how all of the parts of living systems operate together in biological organisms and ecosystems. This understanding could have a profound influence on the future of the human species. It could help produce enough food for a growing population, prevent and cure chronic and acute diseases, meet future needs for energy, and manage the preservation of Earth’s biological heritage for future generations. The approach of this moment of opportunity in the life sciences has become increasingly evident over the last decade, and in many ways, the New Biology has already begun to emerge. It has become common to hear statements that the 21st century will be the century of biology. What are the factors that have brought biology to this point? And what current ideas, tools and approaches represent the emergence of new capabilities? The Fundamental Unity of Biology Has Never Been Clearer or More Applicable The great potential of the life sciences to contribute simultaneously to so many areas of societal need rests on the fact that biology, like physics and chem- istry, relies on a small number of core organizational principles. The ­reality of these core commonalities, conserved throughout evolution––that DNA is the chemical of inheritance, that the cell is the smallest independent unit of life, that cells can be organized into complex, multicellular organisms, that all organisms function within interdependent communities and that photo-systems capture the solar radiation to provide energy for all of life processes––means that any knowledge gained about one genome, cell, organism, community, or ecosystem is useful in understanding many others. Because living systems are so com- plex, much biological experimentation has had to focus on individual or small numbers of components within a single organizational level. The reductionist approach has helped reveal many of the basic molecular, cellular, physiological, and ecological processes that govern life. This work needs to continue in the future. Many aspects of biological func- tion remain unknown on all levels. But biologists are now gaining the capability to go beyond the interactions of components within a single level of biological organization and the study of one or a few components at a time. As microbi- ologist and evolutionist Carl Woese said over 30 years ago, “Our task now is to resynthesize biology; put the organism back into its environment; connect it 

WHY NOW? 41 again to its evolutionary past. . . . The time has come for biology to enter the nonlinear world (Woese & Fox, 1977). The practical ability to achieve Woese’s vision is now beginning to emerge. Biologists are increasingly able to integrate information across many organisms, from multiple levels of organization (such as cells, organisms, and populations) and about entire systems (such as all the genes in a genome or all the cells in a body) to gain a new integrated under- standing that incorporates more and more of the complexity that characterizes biological systems (Box 3.1). As the biological sciences advanced during the 20th century, separate fields emerged to tackle the complex subsystems that together make up living sys- tems. Genetics, cell biology, ecology, microbiology, biochemistry, and ­molecular biology each took on various aspects of the challenge. The sheer volume of knowledge generated in each of these subdisciplines made it increasingly dif- ficult for researchers who studied organisms to keep up with the progress being made by researchers studying cells, and those studying molecules rarely interacted with those studying ecosystems. Scientists in each of these specialties BOX 3.1 The Levels of Complexity of the Biosphere Box 3.1 The Levels of Complexity of the Biosphere Biosphere - The world we live in Ecosystem - The set of communities of all domains of life that interacting with one another and the abiotic environment to form a unit (e.g., freshwater ecosystems, taigas) Community - Interacting populations of organisms (e.g., coral reefs, montane forest) Population - All individuals of a species or phylotype within a community (e.g., trees of a given species within a single forest, the fishes of a given species in a single coral reef) Organism - An single individual (e.g., a lizard, a tree, a bacterium) Organ system - A specialized functional system of an organism (e.g., digestive, nervous) Organ - A set of tissues that function as a unit (e.g., heart, brain, kidney) Tissue - A set of interacting cells (e.g., epithelia, muscle) Cell - The functional unit of all living organisms (e.g., red blood cell, neuron, bacterium) Organelle - A specialized subunit within cell (e.g., mitochondria, chloroplast) Molecule - biochemical constituents of cells (e.g., a protein, a nucleic acid) SOURCE: Committee on a New Biology for the 21st Century. Figure 3 

42 A NEW BIOLOGY FOR THE 21ST CENTURY attended separate meetings, published in different journals, and generally had little communication. Although it was understood on a conceptual level that the organizational levels are tightly interlocked, most researchers focused on a single system in great detail. Recently, though, the connections among the fields of the life sciences have become easier to study. For example, tools and concepts that arose within individual subdisciplines within the life sciences are now applied through- out biology. Thus, biochemistry and molecular biology are now techniques that are applied nearly universally across the life sciences. Genomic data and techniques have widespread applications in biology and reveal the connec- tions among fields. In particular, genomic comparisons reveal the common descent of organisms and enable researchers to make comparisons of different types of organisms (Box 3.2), while also highlighting the differences that have arisen in separate evolutionary lineages. These cross-species investigations have started to blur the boundaries between such fields as microbiology, botany, and z ­ oology. Discovering and understanding the features shared by all living organ- isms, and the differences that make each system or organism unique, has never been easier or more productive. Despite the development of common tools, questions, and methodologies, scientists within subdisciplines that were historically separate still do not have the optimal level of interaction. This is particularly true when the goal is to develop new science linking multiple levels of organization in biological sys- tems. To accelerate progress in the life sciences, researchers from different sub- disciplines need to interact and collaborate to a greater extent. Presenting these communities with a common problem to solve will provide an opportunity for them to bring their different skills and perspectives to bear and accelerate the development of conceptual and technological approaches to understanding the connections between the different levels of biological organization (Box 3.3). New Players are Entering the Field, Bringing New Skills and Ideas Just as integration is becoming more important within the life sciences, immense value is emerging from the integration of the life sciences with other disciplines. For example, the recent and continuing revolution in genomics has come from an integration of techniques and concepts from engineering, robotics, computer science, mathematics, statistics, chemistry, and many other fields. The precipitous decline in the cost of genome sequencing would not have been possible without a combination of engineering of equipment, robotics for automation, and chemistry and biochemistry to make the sequencing accurate. Similarly, expertise from fields as diverse as evolutionary biology, computer science, mathematics, and statistics was necessary to analyze raw genomic data and to extend the use of these data to other fields. 

WHY NOW? 43 BOX 3.2 Common Descent and the Integration of the Life Sciences Though every species on the planet is unique in some ways, all species are linked to each other by common descent: that is, any two species evolved from a common ancestor that lived at some point in the past. As a result, all species share some bio- logical properties due to the inheritance of features present in their common ancestor. Work in one species can be of direct relevance to the understanding of other species because processes may be identical or highly similar between the two due to their shared descent. This is part of the reason for the importance of “model organisms.” Studies in mammalian model systems such as the mouse have led to major insights into human biology. Examples include studies of cholesterol metabolism (which led to the development of the statins, a class of drugs that have dramatically reduced atherosclerosis and cardiovascular disease; beta adrenergic receptors, which led to the development of beta blockers for the treatment of hypertension and heart disease; and tumor necrosis factor, which led to the development of therapeutic antibodies that provide relief to people with rheumatoid arthritis. Frequently, discoveries in one organism have implications even for very distantly related organisms. The degree of relatedness of two organisms, which is determined in large measure by the amount of time that has elapsed since their common ancestor, indicates how much of their biology they share. Consequently, the older a biological process, the more likely that it will be shared by a great number of organisms. Thus, an important mechanism for regulating protein levels in cells, called “small interfering RNA” or siRNA, was initially discovered in plants, but was then found to be at work in human cells and shows great promise as a new approach for drug development (Carthew & Sontheimer, 2009). Even more distantly related organisms can share common genes and pathways. Studies of mutational processes in the bacterium Escherichia coli and the yeast Saccharomyces cerevisiae helped identify the genes that are defective in hereditary nonpolyposis colon cancer (HNPCC) in humans (Fishel & Kolodner, 1995). Because no two species are exactly the same, work in model organisms does not always translate perfectly into other species, even if the two species are quite closely related. For example, the most promising AIDS vaccine candidate failed in human clinical trials (Buchbinder et al., 2008) despite showing promise in experiments with monkeys (Shiver et al., 2002). Animal models of neurodegenerative disease, such as Alzheimer’s or Parkinson’s, and of psychiatric diseases, such as schizophrenia or depression, do not fully reproduce the clinical signs seen in humans with these disorders. Moreover, drugs shown to have efficacy in such animal models have failed in human clinical trials. Better understanding of which characteristics are shared and which are not is a major outstanding challenge in biology, which, when met, will greatly improve our ability to predict how results in one organism will apply to another. 

44 A NEW BIOLOGY FOR THE 21ST CENTURY BOX 3.3 Eco-Evo-Devo: Integration across Subdisciplines The field of evolutionary-developmental biology (known as evo-devo) has emerged in the last 15 years. It offers a powerful example of the potential for integra- tion of biological theory and practice across the hierarchy of life, from molecules to ecosystems. Studies in evo-devo have demonstrated that different animal body plans can result from the alternative expression patterns of a “toolbox” of conserved genes (such as the homeobox genes) and gene networks. The research frontiers of this field lie in the continued development of computational and mathematical tools for the study of the links between development and evolution, and determination of the environmental cues that underlie developmental processes over evolutionary time and within the life of a given individual. As an indication of the kind of mathematical and computational tools needed, analyses of gene sequence data to derive the phylogeny of the animal kingdom required the full-time use of 120 processors over several months (Hejnol et al., in press). Widespread application of such approaches is prohibited by technical con- straints, demonstrating the need for significantly more efficient means of computa- tional analysis for such large datasets. The field of evo-devo is now poised for integration across the entire hierarchy of molecular through organismal biology. For example, at the broadest levels, biolo- gists have long known that the environmental parameters, such as temperature and length of daylight, can profoundly influence developmental processes. However, until recently, developmental biologists have focused almost entirely on an understanding of the gene-to-organism aspects of development—that is, they have studied how the cells and tissues of an organism progress from fertilization through death. Much must still be done to understand these processes, but at the same time they can now be placed within a context of the “ecology of development,” or eco-devo. Developmental processes are often the “canaries” of environmental perturbation. For example, the biogeographic distributions of many marine animals are determined by the temperature sensitivity of their larval stages. The distribution of large numbers of marine taxa is likely to be dramatically altered by either changes in ocean tempera- tures or in ocean circulation patterns. The ripple effects of such changes would affect many aspects of human interface with the oceans, including fisheries. Predicting and minimizing the impact of environmental changes on development, species ranges, and ecosystem services will require collaboration among developmental biologists, ecologists, computational scientists, oceanographers, climate scientists, and others to integrate their knowledge of different parts of the system. The need to analyze these data has driven a rapid expansion in the appli- cation of mathematics to biology. In particular, two aspects of computation have been critical. First, algorithms and computational power for analysis of large data sets help make sense of the massive amounts of data produced by genomic studies. Second, the placement of data in accessible digital databases has greatly improved the ability to share information and build on prior work. 

WHY NOW? 45 Such advances in computation have been critical in many areas of biology, such as ecosystem studies, conservation biology, evolutionary biology, and epidemiol- ogy. Each of these fields needs to handle large complex data sets, digitize and share information, and test complex models and theories. To carry out this work, biology has taken advantage of developments from other fields such as physics, astronomy, and earth sciences, which have been for many years h ­ andling and analyzing massive data sets. Biological data sets are especially challenging because they must cover so many diverse organisms and measure many different characteristics that are constantly changing. Connecting those data sets is extremely difficult but absolutely essential. Success will demand close cooperation between the biologists and other scientists who study the sys- tem, computer scientists and mathematicians who develop new ways to analyze the data, and engineers who bring expertise in modeling. The issue of predictability is one major reason why even the present level of integration of life sciences with engineering is already productive. Engineer- ing offers a way of thinking that can contribute substantively to unraveling the inherent complexity of biological science. The essence of engineering is predictive design. Engineers seek to create systems that can operate reliably within some circumscribed conditions. Moreover, engineering design is almost always undertaken in the face of incomplete information. Even with technolo- gies based on the physical and chemical sciences, there remain many poorly characterized parameters. These limitations also apply to the biological systems, even under the best of circumstances, so bringing an engineering mindset to bear on biological questions is already beginning to add a new layer of value to basic biological research. Already, large numbers of physicists are being drawn into the biological sciences and teams that include engineers, earth scientists, biologists, computa- tional scientists, and others are beginning to conceive and approach biological research in new ways (Boxes 3.4 and 3.5). What needs to occur next is for the boundaries between disciplines to be broken down even further, much as the boundaries within biology are being broken down. Making the New Biology a reality will require not only the best in technology and science, but also a uniquely interdisciplinary approach. Efforts to date must be seen as a “first pass” rather than as a complete integration across multiple fields. The eventual goal is for all scientists and engineers who study biological systems, whether their in-depth training is in physics, mathematics, or chemical engineering, to see themselves also as New Biologists, together contributing to the emergence of the New Biology. A Strong Foundation Has Already Been Built Over the past 40 years, large investments have produced remarkable dis- coveries in the biological sciences. These discoveries have in a large part come 

46 A NEW BIOLOGY FOR THE 21ST CENTURY BOX 3.4 Brain-Machine Interfaces Brain-machine interfaces are systems that allow people or animals to control an external device through their brain activity. In 2003, scientists demonstrated that monkeys with electrode implants in their brains, connected to a robotic arm, could manipulate a robotic arm using only their thoughts. In 2008, scientists demonstrated for the first time that brain signals from a monkey could make a robot walk on a tread- mill. Scientists hope that such technology will be a great benefit for people who are paralyzed or no longer have control of their physical movements. Such technology and experiments will also lead to an increased understanding of how the brain works. Brain-machine interfaces are an example of the convergence of different areas of science and technology, and the importance of encouraging the emergence of the New Biology as an integrated science. In the case of the brain-machine interface that permitted monkey thoughts to make a robot walk, the electrodes were placed in the part of the brain that earlier neurobiological studies had shown contained neurons that fired when primates walk. Detailed video images of leg movements were then combined with measurements of simultaneous brain cell activity, and then analyzed using sophisticated computational methods. A robot, previously designed to closely mimic human locomotion, was then programmed to respond to the brain signals in the monkey (Blakeslee, 2008). SOURCE: Sanchez et al., 2009. Figure 4 R01620 biitmapped fixed image 

WHY NOW? 47 BOX 3.5 Nanotechnology—The Artificial Retina The intertwined nature of the physical and life sciences is exemplified in the prog- ress that has been made with the artificial retina, a device that resulted from a multi- laboratory initiative supported by the Department of Energy. The device has already shown promise in patients with macular degeneration, a major cause of blindness in the elderly. The nerves that are responsible for visual perception are at the surface of the retina, such that these nerves are accessible to electrodes. Microchips composed of ordered arrays of microscopic solar cells, capable of converting light into electrical pulses, have been implanted into the eyes of animals and patients. With a 60-electrode array, patients who had been blind are able to recognize objects and read a large print newspaper. (http://www.artificialretina.energy.gov/) SOURCE: U.S. Department of Energy, Artificial Retina Project. Figure 5 R01620 biitmapped fixed image 

48 A NEW BIOLOGY FOR THE 21ST CENTURY from a reductionist focus on the basic molecular components of cells, which has uncovered many of the molecular and cellular processes that govern life. Work focusing on other levels of organization––organisms, communities, and ecosystems––also has produced profound new insights. Reductionism, which dissects and analyzes individual components of ­living systems to infer mechanisms and to account for the behavior of the whole, has been remarkably successful. And nothing in this report should be inter- preted to suggest that support for what is often called “small science” should d ­ iminish—indeed it must grow––as the traditional approach to life sciences research will continue to be a major source of discovery and innovation. It has already revolutionized concepts of molecular interactions and cellular func- tioning, unraveled many of the processes that allow the development of a multi-cellular organism from a fertilized egg, and identified many of the factors that contribute to ecosystem stability. The effort to construct the “parts list” for living systems has been a tremendously exciting intellectual adventure in its own right and has had revolutionary outcomes, such as the biotechnology revolution in medicine and agriculture. Continuing support for peer-reviewed, investigator-initiated research across the broad spectrum of biological sciences is critically important. The New Biology cannot replace––indeed, will not flourish without––those efforts. Construction of an interstate highway does not mean that local road maintenance can cease; the two systems depend on and benefit from each other. Just so, continued support is needed for the science that lays the groundwork for synthesis. Past Investments Are Paying Big Dividends The release in 2000 of the draft sequence of the human genome was the product of a decade-long program that involved billions of dollars in invest- ment. Funding for this project came from multiple U.S. government agencies (especially the National Institutes of Health and the Department of Energy), as well as from international governmental and private sources. In addition, the project was spurred on by competition and collaboration with the private sector, and by the development of new technologies. The sequencing of the human genome was a goal akin to that of sending humans to the moon, in that the science and technology needed to achieve the mission did not exist when the goal was announced. But new technologies and concepts were developed that have now become routine components of all genome sequencing projects. The magnitude of the challenge spurred cre- ative engagement leading to transformative advances. Many of the advances in sequencing technology were incremental, but there were some game-changing developments, like the demonstration that random shotgun sequencing could be applied successfully to a large complex genome. That kind of transforma- tive event cannot be predicted, but setting an important goal and providing 

WHY NOW? 49 resources to reach it makes it more likely that creative minds will turn to devel- oping revolutionary new approaches in addition to incremental progress. Random shotgun sequencing, in which a computer detects overlaps of raw sequencing “reads” to construct a complete genome, was not considered useful for human genome sequencing due to the size and complexity of the human genome. However, with the development of new sequencing and computational methods (developed by engineers and computational scientists who turned their efforts to solving biological problems), shotgun genome sequencing became the standard method for genome sequencing and has led to an exponential increase in the number of complete or nearly complete genomes available. This in turn has led to the development of next-generation sequencing technolo- gies that produce massive amounts of sequence data that can only be analyzed computationally. With the rapid development of sequencing and analysis capabilities, DNA sequencing has become a routine tool in unanticipated areas. A good example is metagenomics, which involves the random sequencing of DNA isolated from environmental samples (such as from soil or water). Metagenomics provides insight into what has previously been a mostly hidden world of microbial diver- sity, which itself is important because microbes have a fundamental impact on the biogeochemical cycles of the planet and on the health of all its inhabitants (Box 3.6). Another example is population genomics, where researchers are generating multiple complete genomes of different individuals within a species. These data, in turn, serve as valuable input for multiple areas of biology, includ- ing genetics, plant and animal breeding, and disease studies. Other biological areas being transformed by genome sequencing include ecology, agriculture, bioenergy research, forensics, and biodefense. None of this would have been possible without the tools and resources developed as part of efforts geared primarily toward sequencing the human genome. The past 15 years have seen the development of tools and technologies that have extended research capabilities well beyond genome sequencing. These tools include methods to characterize the presence and quantities of many other biological molecules, including transcribed RNA, proteins, metabolites, molecules secreted by cells, DNA methylation patterns, and so on. The com- prehensive sets of data generated about these biological molecules––commonly referred to as “omes” (transcriptomes, proteomes, metabolomes, etc.)—are as yet more difficult and expensive to generate and less standardized than genomes. Advances in these technologies will be critical to rapid advances in the life sciences. Being able to collect and analyze these comprehensive data sets allows researchers to relate and integrate the components of biological systems, a pursuit known as systems biology. They also allow researchers to investigate organisms other than the model systems that have been studied in the past. With relatively little effort and cost, researchers can derive information on an 

50 A NEW BIOLOGY FOR THE 21ST CENTURY BOX 3.6 Microbial Genomics Microbiology, through microbial genomics, is experiencing a renaissance enabled by technological advances over the past several years that have allowed researchers to explore the diversity and metabolic capabilities of a microbial world thousands of times more diverse than before appreciated. This newfound potential is allowing us to understand the critical position that microbes have in the biological world. ­Harnessing the molecular biology and biochemistry of microbes, either in pure culture under laboratory conditions or in naturally occurring complex communities, promises to contribute significantly to addressing all four challenges presented in this report (Maloy & Schaechter, 2006; Woese & Goldenfeld, 2009). Microbial communities support the growth of plants, affect human health, are critical components of all ecosystems, and can be engineered to produce fuels. Until the advent of low cost, high throughout sequencing, most of the microbial world was essentially invisible. By necessity, microbiologists focused on the study of individual microbial species grown in pure laboratory culture. Increasingly, it is clear that pure culture does not reflect how microbes live outside of the laboratory and that the microbial world is more diverse, more important, and far more interdependent than had previously been imagined. Interdependence––whereby complex communities of microbes work together to carry out such functions as digesting food, breaking down waste and capturing solar or geothermal energy––is the rule, and the many microbes that can only grow in community were never isolated by classical culturing methods. There is now a tremendous opportunity, and imperative, to develop ­methods to effi- ciently characterize these communities. For any given circumstance (e.g., the body of an organism, the soil supporting a specific crop, or the water sustaining temper- ate fisheries), we must be able to determine the composition of such communities, how they function under conditions that promote the health of the system, and the effects of imbalances in these communities when they are perturbed. The patterns that emerge from these studies can be used to develop predictive models, so that we might recognize problems early and intervene before the situation is irreversible. Inte- grating microbiology into healthcare, agriculture, energy production, and eco­system management will be critical to the future of all of these areas. 

WHY NOW? 51 Confocal micrograph depicting the colonization of host animal tissues (blue) by two different types of bacteria (red and green). The bacteria are colonizing extra­cellularly along the apical surfaces of the host-animal epithelia, in a similar manner to the way that bacteria colonize the mammalian intestine. Unlike the mammalian intestine, Figure 6 which harbors a consortium of hundreds of bacterial types, the animal whose tissue R01620 is depicted here, the Hawaiian squid Euprymna scolopes, only harbors one species biitmapped fixed image of bacteria, Vibrio fischeri. The organ is co-colonized by two strains of V. fischeri, a wild-type strain (red) and a mutant strain (green). SOURCE: Image courtesy of Dr. Joshua V. Troll, University of Wisconsin, Madison. 

52 A NEW BIOLOGY FOR THE 21ST CENTURY organism’s genome, gene expression patterns, and population variation. One result has been the development of fields such as “polar genomics,” “agricul- tural genomics,” and “ecological genomics.” The explosion of unanticipated benefits of the Human Genome Project demonstrates how biology can benefit from large-scale interdisciplinary efforts. Another lesson from the Human Genome Project is that even scientific efforts that appear incremental can spawn transformative advances. Similar efforts to allow systematic characterizations at other levels of biological complexity, like the cell, organism, and community, could have similarly dramatic downstream payoffs. New Tools And Emerging New Sciences Are Expanding What Is Possible Recent technological advances in a number of fields outside biology make possible unprecedented quantitative analyses of biological systems. These fields are diverse, including physics, electronics, chemistry, nanotechnology, computer science, and information technology. In most instances, tools and methods developed for specific applications in their respective fields have been adapted for use in probing biological systems. But in many cases the complexity of biological systems presents new challenges that call for creative solutions and additional innovation. The descriptions that follow are not meant to be exhaus- tive or prescriptive. They are examples of the kinds of technologies and sciences that would have major impacts in many areas of biological inquiry. Foundational Technologies Information Technologies Advances in information technology (IT), particularly during the last two decades, have dramatically affected our private lives and all aspects of society. The steady increase in computer power, accompanied by a sharp decrease in cost, has been particularly remarkable. Calculations that would take weeks only 10 years ago and required access to large mainframe computers can now be executed in minutes on a laptop. The advent of optical fibers with bandwidth of up to 40 Gb/sec (100 Gb/sec predicted for 2010) has enabled increasingly large volumes of data to be seam- lessly transferred over the network. The interactive and dynamic manipulation and visualization of complex images has become commonplace, with many ubiquitous applications, most notably in computer games. It must be realized that implementation of these advances required not only the availability of specialized hardware but very importantly, the devel- opment of sophisticated software (e.g. the operating systems and their user 

WHY NOW? 53 friendly interfaces, the algorithms capable of carrying out the computations efficiently, the software protocol for ensuring error-free data transmission, and the standards for data exchange and communication between computers and other devices). The impact of these developments has been particularly far-reaching for the life sciences, because it has come at the very moment in time when the life sciences are undergoing a historical transition, from a low-throughput descriptive experimental discipline to a high-throughput increasingly quantita- tive science. More than ever before, the life sciences are about collecting, archiving, and analyzing information on living organisms and their myriad components, and this effort is distributed across the globe. Worldwide genome sequencing efforts, including the recent efforts to sequence the genome of 1000 indi- viduals, are generating terabytes of sequence data (1TB [terabyte] = 1 trillion bytes) that need to be processed, stored, and analyzed. While information on genome sequences is relatively straightforward to represent because of its one- dimensional nature, it is much more difficult to represent information on the biological function of genes and proteins and their organization into dynamic cellular processes. Nevertheless, much of the output of life sciences researchers is now cap- tured in electronic form, and databases now include far more than just DNA sequence data. Biological imaging and scanning are producing vast amounts of data about biomolecules, cells, organs, organisms, and environments that is difficult to index and interpret because of its three-dimensional pictorial or even four-dimensional dynamic nature. It is often necessary to preserve such data in its raw form because of uncertainty about how it will eventually be sum- marized and codified for downstream analyses by diverse users. The sheer size of some of these files suggests that some decisions will be required about what must be saved or made easily accessible. Applications in commerce, Internet search, and data acquisition in the sciences have spurred advances in database systems to handle large data volumes and provide versatile tools for facilitating user interaction, data management, and visualization. Nonetheless, the volume, complexity and diversity of biological data, and the lack of proper conceptual frameworks for representing and analyzing it, will continue to push the limits of data modeling methods and database technology. In vivo and Real Time Imaging of Cells, Organisms, and Ecosystems The technologies of in vivo and real-time imaging include a related set of such methods as fluorescence, total internal reflection fluorescence, near-field and confocal microscopy, and functional magnetic resonance imaging, and their related technologies, such as the manipulation of fluorescent proteins, fluores- cent dyes, and MRI contrast reagents at the cellular and organismal level. 

54 A NEW BIOLOGY FOR THE 21ST CENTURY Cells are densely packed with thousands of interacting components that must be produced, transported, assembled into complexes, and recycled all at the appropriate time and place. Whereas proteomics techniques such as those discussed below aim to provide large-scale systematic characterization of the components of cells or other biological samples, current technology does not allow observation of the spatial and temporal organization of these entities while they are at work in cells. The main limitations to reaching a systems level understanding of living cells is the lack of experimental tools that can analyze the cell’s complicated internal complexes as they are forming, working, and disassembling. Several of the experimental tools described below are starting to fill this void; significant progress in this area would be valuable across the life sciences. Recent advances in imaging techniques, such as cryogenic electron tomog- raphy (Cryo-ET), offer the capability of charting cellular landscapes at previ- ously unattainable resolutions of less than 10Å, with predictions to attain near atomic resolution in specific cases (Leis et al., 2009). To date Cryo-ET analyses have been mostly restricted to isolated macromolecular assemblies, small bacte- rial cells, or thin regions of more complex cells, due to the limited penetration depth of electrons. However, recently developed cryo-sectioning techniques make it possible to transcend these limitations and acquire detailed views of many kinds of cells and tissues. The interpretation of these views relies on help from various techniques for labeling protein constituents (immunolabelling, or use of fluorescent tags), and is a fast evolving area. These techniques can be complemented by information from the rapidly expanding repertoire of known 3D structures of individual proteins, as atomic models of these proteins can be used to expand the lower resolution images obtained by the Cryo-ET technique. This combination of techniques provides unprecedented insight into the molecular organization of cellular landscapes. Similarly, the technology to characterize the location and activity of indi- vidual cells within a living organism is also improving. Substantial progress has been made over the last two decades in extending the application of fluorescent semiconductor nanocrystals (also known as quantum dots or qdots) from elec- tronic materials science to biological systems (Gao et al., 2004). Examples of their recent use in the analysis of biological systems include monitoring the dif- fusion of individual receptor proteins (e.g., glycine receptors) in living neurons and the identification of lymph nodes in live animals by near-infrared emission during surgery. Multifunctional nanoparticle probes based on semiconductor quantum dots (qdots) have recently been developed for cancer targeting and imaging in living animals. The applications include in vivo targeting studies of human prostate cancer growing in mice and sensitive and multicolor fluores- cence imaging of cancer cells under in vivo conditions (Box 3.7). In addition, microfluidic and microfabrication approaches are generating the ability to monitor cells and their components at unprecedented levels of resolution. 

WHY NOW? 55 BOX 3.7 Quantum Dots for Biological Investigation A C i ii B Normalized uorescence InAs InP CdSe iii 60Å 46 36 28Å 46Å 40 35 30 46 36 31 24 21Å 1780 1030 730 560 460 Wavelength (nm) Quantum dots (QDs) provide a powerful tool for biological investigations. A) Two di erent sizes of silica-coated CdSe/CdS core/shell QDs were used to label mouse 3T3 broblasts in the rst demonstration of QD-biological labeling (Bruchez Jr. et al., Science 1998; related work by Chan and Nie, Science 1998); here, red QDs label F-actin laments while green QDs label the cell nucleus. Image width 84 µm. B) The emission wavelength of QDs can be spectrally tuned by varying nanocrystal size and composition. The narrow emission and photostability of QDs as compared with traditional dye molecules enable a multiplexable, long-lasting uorescence imaging tool. Image courtesy of M. Bawendi; spectra from Bruchez Jr. et al., Science 1998. C) Dynamics of single QD-labeled glycine receptors are observed over time. (i) QD-labeled glycine receptors (red) are detected over the somatodendritic compartment identi ed by Alexa 488-labeled microtubule-associated protein-2 (green). (ii) The electron density of QDs allows them to also be additionally visualized by electron microscopy, which is not feasible for traditional uorophore markers. QDs within the synaptic cleft are identi ed at both dendrites (d) and synaptic boutons (b). Scale bar 500 nm. (iii) The locations of 17 individual QDs (green) are tracked at 5 min intervals over 40 min (trace at right) and observed as synaptic (s), perisynaptic (p), or extrasynaptic (e) relative to AM4-64-labeled boutons (red). SOURCE: Dahan et al., 2003. Also, whole-organism imaging and remote sensing, including satellite remote sensing and multispectralFigure 7 at the ecosystem level, are avail- imaging R01620 able in real time. The problems associated with the full development of these methods include image processing and analysis, to enable bitmapped visual- vector editable, although components are features to be ized and automatically recognized. This area is of importance to many of the central problem areas that have been identified, including crop productivity (in analysis of plant cells and growth of tissues) and sustainable crop production, ecological monitoring by ecosystem visualization, and better understanding of human health through advances in medical imaging. To the degree that plant growth is central to biofuel production, these technology platforms are also of importance in this area. Satellite remote sensing of the earth’s surface, beginning in the early 1970s, has dramatically influenced understanding of the distribution of life processes on the planet, as well as pointing critical attention to the rapid human-induced changes at global scales. Broad-spectral reflectance optical sensors, such as the 

56 A NEW BIOLOGY FOR THE 21ST CENTURY series of Landsat satellites, have been used to measure rates of deforestation, and these and similar sensors are now used routinely to measure these rates over tropical South America. NASA has developed global data sets for the 1980s, 1990s, 2000, and 2005 that cover essentially the entire terrestrial surface in six visible and near-infrared spectral bands at approximately 30 m spatial resolution. This is a powerful time series of the actual change in land-cover and vegetation for the earth’s surface, and has already proven useful not only for understanding amounts and rates of deforestation and habitat change, but also for assessing agricultural extent and productivity. These medium resolution data have been substantially augmented by higher temporal resolution sampling of a broader range of spectral reflectances from sensors such as MODIS on NASA’s TERRA and AQUA platforms. MODIS provides twice daily sampling of a wider range of more precise spectral bands, and enables the analysis of long (now nearly a decade) time series of net pri- mary productivity, vegetation distribution, seasonality, surface temperatures, and along with other optical sensors, ocean biological productivity through the measurement of ocean color (i.e., observations of the chlorophyll concentra- tions of the surface ocean). Through experimental missions, aircraft missions, and some space obser- vations, remote measurements can increasingly be used to derive specific pro- cess-based information, or to retrieve critical parameters directly. Examples include the use of hyperspectral information to detect canopy nitrogen and lignin content, and therefore estimate photosynthetic potential and distinguish individual species distributions, the use of synthetic aperture radar to estimate the distribution of above-ground biomass, and the use of lidars to measure the height distribution of vegetation canopies and thus estimate the vertical distri- bution of woody biomass, in addition to its total mass. Earth Science and Applications from Space (National Research Council, 2007b) has identified 17 missions, many of which have specific biological goals related to understanding the interaction of ecosystems, the physical climate system, and human disturbances. These represent the scientific community’s best summary to date of the fundamental advances that are believed possible, and that would transform understanding of how ecosystems function now, and how they are expected to function in the future. High-Throughput Technologies Recent advances in DNA sequencing technologies have been tremendous. Using current next-generation technology, the Joint Genome Institute, headed by the Lawrence Berkeley National Laboratory and Lawrence Livermore National Laboratory, sequenced over 20 billion nucleotides in the month of October 2008 (DOE Joint Genome Institute, 2009). The ability to sequence individual genomes, or relevant portions of genomes, will have a major impact 

WHY NOW? 57 on the ability to develop and deliver personalized medicines, to speed plant breeding, and to monitor environmental conditions. Already, sequencing costs are so low that they do not represent a barrier to experiments that would have been unthinkable even five years ago. Box 3.8 describes one new sequencing approach made possible by advances in nanotechnology. Proteins play key roles in virtually all cellular processes. Measuring their expression levels and the chemical modifications that they undergo as a result of changing cellular environments and developmental and disease states has become one of the major goals of present-day biology and medicine. Also, proteins rarely act alone. They interact with one another, often forming large edifices that act as complex molecular machines. The systematic characteriza- tion of these interactions is required in order to elucidate the functional inter- dependencies among proteins. Technological advances over the past 10 years have made it possible to carry out these various analyses on very large scales, giving rise to the field of proteomics, or the study of all of the proteins in a particular biological sample (for example, a single cell or a drop of saliva). Progress in molecular biology techniques and purification methods, coupled with mass spectrometry (MS) techniques, have played a major role in these advances, with MS increasingly becoming the method of choice for the analysis of complex protein samples. BOX 3.8 Nanotechnology and Sequencing There are many competing technologies being developed for DNA sequencing (Shendure & Ji, 2008). One of them provides an illustrative example of the interface between biology and nanotechnology, referred to as single-molecule, real-time DNA sequencing. This method utilizes DNA polymerase, an enzyme that synthesizes DNA, and fluorescent nucleotides (different labels for each of the four nucleotides). Because DNA polymerase incorporates complementary nucleotides, monitoring the fluorescent signal of the added nucleotide during synthesis allows one to determine the DNA sequence of the original DNA. A critical component of the method is the use of a zero- mode waveguide (ZMW), a nanofabricated hole that only allows light to penetrate a tiny distance, so that the fluorescence of a single molecule can be detected despite the presence of high concentrations of fluorescent molecules in the remainder of the sample. A single molecule of DNA polymerase is immobilized at the bottom of a ZMW, which is illuminated from below with a laser light. As each incoming fluorescently labeled nucleotide binds to the DNA polymerase, the signal is detected using single- molecule spectroscopy. The faster, cheaper sequencing that may result from this approach (again, only one of many being pursued) emphasizes the potential impact of collaborations that cross traditional disciplines (here, molecular biology, chemistry, applied physics, and nanoengineering) in the life sciences (Eid et al., 2009). ������������������� 

58 A NEW BIOLOGY FOR THE 21ST CENTURY Of the different MS-based techniques for protein profiling, the most sensi- tive ones are currently able to detect protein expressed at levels of only a few hundred copies per cell. MS-based methods for detecting protein interaction partners and protein complexes have been successful in identifying thousands of protein interactions and hundreds of multi-protein complexes in simple organisms such as yeast and bacteria, and are now being extended to higher organisms such as the mouse and human. Silicon microelectronics has made computation ever faster, cheaper, more accessible, and more powerful. Microfluidic chips, feats of minuscule plumbing where more than a hundred cell cultures or other experiments can reside in a rubbery silicone integrated circuit the size of a quarter, could bring a similar revolution of automation to biological and medical research. Using techniques drawn from engineering, chemistry, and physics, highly miniaturized sensors and analysis devices can be generated that measure real-time parameters at the level of individual cells or even subcellular compartments, allowing the study and manipulation of processes at relevant functional levels. The expense, inefficiency, and high maintenance and space requirements of robotic automation systems present barriers to performing experiments. By contrast, microfluidic chips are inexpensive and require little maintenance or space. They also need very small amounts of samples and chemical inputs to make experiments work, making them more efficient and potentially cheaper to use. These chips are made using optical lithography to etch the circuit pat- tern into silicon. The etched silicon acts as a mold. Silicone is poured into the mold and then removed. By stacking several layers of molded silicone and then encasing them in glass, researchers can create an integrated circuit of channels, valves and chambers for chemicals and cells—like a rubbery labyrinth. Cell culture chips with up to 100 chambers have been designed to hold individual cells and all the microscopic plumbing necessary to add any com- bination of different chemical inputs to those chambers. Such chips can be used to test how different inputs might cause stem cells to transform into more specific cells needed for particular treatments. They could also be used to test how different combinations of antibiotics affect a particular bacterium. Other chips can be designed for the preparation of valuable and expensive purified proteins for structural analysis by X-ray diffraction. The tedious trial-and-error process of preparing crystals of macromolecules may be greatly accelerated using microfluidic chips. A recent report describes a new type of microfluidic chip enabling the detailed analysis of up to a dozen different protein indica- tors of diseases from a single drop of blood in less than 10 minutes (Chen et al., 2008). Such chips would significantly lower the cost of clinical lab tests that measure the presence and relative abundance of specific proteins, thereby enabling early detection of diseases such as cancer. Microfluidic-based “ ­ sippers” that allow monitoring of cell contents in living systems, devices that allow sequencing of the genome from a single cell, and multiplexed systems 

WHY NOW? 59 that monitor ­parameters in high throughput are all pushing the boundaries of our understanding of the dynamics and complexity of living systems (Box 3.9). These monitoring approaches are also beginning to impact ecological sciences, with real-time 24/7 monitoring of habitat function within reach. In addition, the ability to monitor health parameters from a wristwatch, eyeglass, or even contact lenses is under development for real-time health monitoring and report- ing that can occur anywhere. BOX 3.9 The Chemistrode The chemistrode is a new microfluidic device created by Rustem Ismagilov and colleagues at the University of Chicago that “sips” from a living cell in a way analogous to that in which a microelectrode measures electrical signals. Using a V-shaped tube with an opening at the point of the V, small amounts of cell contents are delivered to aqueous droplets separated by a hydrophobic carrier, which are then passed through a splitter to create replicate arrays of the contents for downstream analysis. This system has the potential to stimulate, record, and analyze molecular signals in cells (Chen et al., 2008). SOURCE: Chen et al., 2008. Copyright 2009 National Academy of Sciences, U.S.A. Figure 8 R01620 biitmapped fixed image 

60 A NEW BIOLOGY FOR THE 21ST CENTURY Major areas that still require development are nanoscale electrochemical sensors to enable multiplexing with optical sensors, extension of existing sensor technology to broad ranges of analytes, creation of novel platforms for facile deployment, and an increase in the reliability and reproducibility to allow a range of biologically meaningful measurements. The coupling of microfluidics with microfabricated parts will also broaden applicability, and the addition of remote transmission of data will extend the use of these devices outside of the laboratory. In the future, imbedded and largely invisible systems for mea- surement, analysis, and reporting will become commonplace and will change our lives tomorrow, in much the same way that miniaturized communications technology has changed our world today. Engineered Biological Systems Understanding and manipulation of biological systems depends crucially on being able to grow them reproducibly in the laboratory and, for some appli- cations, to scale that ability to commercial production scales. ­ Technological breakthroughs in materials and devices are making it easier to maintain biologi- cal entities in an environment that maximizes their production of a particular product or allows their experimental observation and manipulation. Generally these systems are designed to grow cells of a particular type, microbial, plant, or animal, but systems to maintain communities of microbes or support the growth of tissues and organs are the next wave of engineered biological systems. This capability was developed first for microbial cells in the 1970s and next for animal cells in the 1980s, via in vitro cell culture bioreactors aimed predominantly toward production of therapeutic proteins and non-medical biomolecules such as polymers and specialty chemicals. These reactors included process control systems that could not only track environmental conditions, but also modulate the bioreactors to keep conditions in a desired state. For the most part, however, these bioreactors were limited to cell types that would be productive when growing in fluid suspensions, attached to particle surfaces, or immobilized within membranes. If tissues and organs from multi-cellular organisms like animals and plants are to be studied in culture, the conditions under which they normally grow must be reproduced––tissues have internal and external surfaces, they often respond to signals from an extracellular matrix, and depend on the continuous delivery of nutrients and removal of waste. Efforts to create effective environments along these lines have accelerated since the early 1990s, in a field generally called ­“tissue engineering.” Tissue engineers develop materials, scaffolds, or devices that pro- vide biochemical and biophysical cues to facilitate cell survival, proliferation, dif- ferentiation, and organization into functional three-dimensional tissues. The field of tissue engineering promises to provide more effective experimental systems for studying complex human tissue physiology and pathophysiology in vitro. This 

WHY NOW? 61 capability is highly desirable because animal models fail to capture many crucial facets of human physiology, notably in the areas of tissue-specific transcrip- tional regulation, drug-induced liver toxicity, pathogenic infection, host immune responses, and cancer. Engineered tissues built with human cells are thus being developed for a range of application areas, including hepatic drug metabolism and toxicity, mammary gland morphogenesis and oncogenesis, lymphoid tissue neogenesis, and stem cell differentiation, and offer promise for scaling to the data collection demands of high-throughput screening and systems biology. Foundational Sciences Systems Biology A crucial requisite for basic science understanding and technology design is a capability for predicting how the entity under consideration will behave under conditions not yet examined. In the New Biology, pursuing and apply- ing this capability to the greatest extent feasible must be a high priority in order to accomplish the kinds of objectives laid out elsewhere in this report. In particular, maximal impact of the molecular and genomic biology revolutions in late-20th century life science will arise from endeavoring to build predictive models of physiological behaviors, in terms of underlying molecular compo- nent properties. The lesson from physical and chemical sciences over the past century is that a combination of quantitative multivariate measurement with computational analysis is typically essential for predictive models, and the challenge for life science is that for the foreseeable future it will still have an incomplete knowledge of all of the components and interactions that make up biological systems. Improved measurement technologies and mathematical and computational tools have led to the emergence of a new approach to biological questions, termed “systems biology,” which strives to achieve predictive modeling. Systems biology seeks a deep quantitative understanding of complex biological pro- cesses through dynamic interaction of components that may include multiple molecular, cellular, organismal, population, community, and ecosystem func- tions. It builds on foundational large-scale cataloguing efforts (e.g., ­genomics, proteomics, metabolomics, etc.) that specify the “parts list” needed for con- structing models. The models relate the properties of parts to the dynamic operation of the systems they participate in. The systems approach was applied early on to ecosystem processes (Hagen, 1992), a legacy that has resulted in the development of complex simulation models capable of evaluating interactions among plant communities, ecosystem processes, and atmospheric dynamics. More recently, systems biology has expanded to molecular components involved in intrinsic cellular processes including gene expression, metabolism, structure and force generation, and regulatory signal transduction. These new 

62 A NEW BIOLOGY FOR THE 21ST CENTURY advances in systems biology at the cellular level now make it feasible to analyze large data sets of molecular level data that then may be related to phenotypic functions at cellular and higher levels via appropriate kinds of computational methods. A broad range of computational modeling approaches for studying cell signaling and its physiological consequences is needed in the arsenal of systems biology. Fortunately, a wide spectrum of algorithmic methods relevant to systems biology modeling is available from mathematical and computational science, as well as the physical sciences. These tools include Bayesian networks, Boolean and fuzzy logic, inverse modeling, and data assimilation, among others. It clearly can be anticipated that development and application of novel math- ematical and computational approaches will be motivated by the difficult prob- lems continuing to arise in systems biology due to issues such as incomplete information concerning system components and properties, heterogeneity and stochasticity, convolution of biochemical and biophysical processes, and the multiple length- and time-scales inherent in attempting to establish predictive models at all levels of biological organization, from the molecular, through the organism, population, ecosystem, and finally, the global scales. Computational Biology Biology and mathematics have long been intertwined. The dynamic inter- play of hosts and parasites, molecular forces in proteins, biological pattern formation, and signal transmission along axons has been studied using tools of mathematical analysis such as non-linear dynamics and partial differential equations. Fluid dynamics and differential geometry have been applied to heart physiology, group theory to x-ray crystallography, and topological knot theory to the coiling of DNA. From its very origin it was recognized that the study of genetic processes requires probability and statistics. In all these instances the data requirements of the mathematical models were relatively modest, and the daily work of most experimental biologists was relatively unaffected by the results of these studies. The picture changed completely with the advent of genome sequencing, functional genomics, and systems biology. Biology became an information- based field in which large shared databases are an indispensable tool. The mathematical underpinnings of the field expanded to embrace probabilistic and combinatorial methods. Combinatorial algorithms are essential for solving the puzzles of genome assembly, sequence alignment, and phylogeny construc- tion based on molecular data. Probabilistic models such as Hidden Markov models and Bayesian networks are now applied to gene finding and compara- tive genomics. Algorithms from statistics and machine learning are applied to genome-wide association studies and to problems of classification, clustering and feature selection arising in the analysis of large-scale gene expression data. The rate of innovation in these statistical disciplines is rapid as new problems 

WHY NOW? 63 of increasing complexity arise in the analysis of models based on heterogeneous data sources. Close collaboration between biologists and mathematicians is increasingly fruitful for both fields by providing new approaches to biological questions and also driving innovation in mathematics. Synthetic Biology Another foundational science that reflects the growing role of engineering in biology is synthetic biology. The ability not only to understand, but also to modify and construct biological systems will be essential if we are to apply the power of biology to diverse environmental, energy, and health problems. Syn- thetic biology aims to use biological modules as the components with which to engineer new biological systems. By standardizing biological parts and the way in which classes of parts can be functionally linked together, this field aims to make large-scale genetic engineering easier and more predictable, potentially leading to cells, organisms, or biologically inspired systems with highly opti- mized industrial or therapeutic applications. Synthetic biology is also proving to be an effective teacher as a way to learn more about the fundamental logic of biological systems. Traditionally, natural biological systems have been studied by observation and by dissection (reverse engineering). These approaches alone, however, are often insufficient to uncover the core design principles of a system It can be difficult to identify which components and parameters are most important, especially when dealing with natural systems that have arisen through idiosyncratic evolutionary paths. The ability to build and modify a biological system provides tools to directly probe and interrogate the system. One can modify individual parameters in a controlled and combinatorial fashion to understand which ones are function- ally most important and under what circumstances. One can identify minimal or alternative systems that can achieve a particular function, thereby more clearly outlining core design principles. Success in forward engineering is the ultimate test of predictable understanding; failure can be our most constructive teacher. These approaches are already bearing fruit and may ultimately gener- ate the next great conceptual advance: a general understanding of how nature constructs robust and precise systems from noisy and imperfect parts (as well as why these systems fail under certain circumstances). Conclusion All of these factors—increasing integration within the life sciences and between the life sciences and other disciplines, a deep pool of detailed knowl- edge of biological components and processes, previous investment in the gen- eration of shared data resources, stunning technological innovations, and cross- cutting sciences that are foundational across many applications—have put the 

64 A NEW BIOLOGY FOR THE 21ST CENTURY life sciences unmistakably on course to a major acceleration of discovery and innovation. It is a matter of great and justified excitement that a sharp upturn in the curve of conceptual progress is coming into view. But realizing this potential will require a crucial transition within the life sciences. It will require significant investment and will no doubt cause some disruption of engrained educational, institutional, and even intellectual habits. The question must be asked whether the life sciences are ready to capitalize on this potential. Perhaps it would be preferable to continue to focus on cur- rent approaches until further progress makes success more likely. What is the urgency, or the claimed opportunity, to move forward now? One response appeals to America’s competitive spirit. The United States was a leader in the development of the life sciences throughout the 20th century and would benefit greatly by remaining in that position in the 21st century. Especially in economically challenging times, the drive to stay at the forefront of critical areas of research can motivate needed investments and changes. The time to move forward is now.