Catalyzing Inquiry at the Interface of Computing and Biology (2005) / Chapter Skim
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10 Culture and Research Infrastructure
10 Culture and Research Infrastructure
Pages 331-382
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From page 331... ...
and for biologists to learn how to use the new tools of information technology. Because the BioComp interface encompasses a variety of intellectual paradigms and disparate institutions, Section 10.2 describes the organizational and institutional infrastructure supporting work at this interface, illustrating a variety of programs and training approaches.
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From page 332... ...
10.2 ORGANIZATIONS AND INSTITUTIONS Efforts to pursue research at the BioComp interface, as well as the parallel goal of attracting and training a sufficient workforce, are supported by a number of institutions and organizations in the public and private sectors. A prime mover is the U.S.
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From page 333... ...
Those differences in origin result in varying emphases on what constitutes core subject matter, whether interdisciplinary work is encouraged and how it is handled, and how research is supported and evaluated. What is clear is that this is an active area of development and investment, and many major colleges and universities have a formal educational program of some sort at the BioComp interface (generally in bioinformatics or computational biology)
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From page 334... ...
Thus, the goal of undergraduate curricula at the BioComp interface is to expose students to a wide range of biological knowledge and issues and to the intellectual tools and constructs of computing such as programming, statistics, algorithm design, and databases. Today, most such programs focus on bioinformatics or computational biology, and in the most typical cases, the integration of biology and computing occurs later rather than earlier in these programs (e.g., as senior-year capstone courses)
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From page 335... ...
· Summer programs for undergraduates offer undergraduates an opportunity to get involved in actual research projects while being exposed to workshops and tutorials in a range of issues at the BioComp interface. Many such programs are funded by a National Science Foundation or National Institutes of Health program.7 When none of these options are available, a student can still create a program informally (either on his or her initiative or with the advice and support of a sympathetic faculty member)
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From page 336... ...
, the report noted the importance to biology students of understanding concepts such as rate of change, modeling, equilibrium, and stability, structure of a system, and interactions among components, and argued that every student should acquire the ability to analyze issues arising in these contexts in some depth, using analytical methods (including paper-and-pencil techniques) and appropriate computational tools.
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Other biological phenomena that can be analyzed using an engineering approach and that are suitable for inclusion in a biology curriculum include the following: · The blood circulatory system and its control; fluid dynamics; pressure and force balance; · Swimming, flying, walking, dynamical description, energy requirements, actuators, control; material prop erties of biological systems and how their structure relates to their function (e.g., wood, hair, cell membrane cartilage) ; · Shapes of cells: force balance, hydrostatic pressure, elasticity of membrane and effects of the spatial depen dence of elasticity; effects of cytoskeletal force on shape; and · Chemical networks for cell signaling; these involve the concepts of negative feedback, gain, signal-to noise, bandwidth, and cross-talk.
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338 CATALYZING INQUIRY Box 10.2 Essential Concepts of Mathematics and Computer Science for Life Scientists Calculus · Complex numbers · Functions · Limits · Continuity · The integral · The derivative and linearization · Elementary functions · Fourier series · Multidimensional calculus: linear approximations, integration over multiple variables Linear Algebra · Scalars, vectors, matrices · Linear transformations · Eigenvalues and eigenvectors · Invariant subspaces Dynamical Systems · Continuous time dynamics -- equations of motion and their trajectories · Test points, limit cycles, and stability around them · Phase plane analysis · Cooperativity, positive feedback, and negative feedback · Multistability · Discrete time dynamics -- mappings, stable points, and stable cycles · Sensitivity to initial conditions and chaos Probability and Statistics · Probability distributions · Random numbers and stochastic processes · Covariation, correlation, and independence · Error likelihood Information and Computation · Algorithms (with examples) · Computability · Optimization in mathematics and computation · "Bits": information and mutual information Data Structures · Metrics: generalized "distance" and sequence comparisons · Clustering · Tree relationships · Graphics: visualizing and displaying data and models for conceptual understanding SOURCE: Reprinted from National Research Council, BIO2010: Transforming Undergraduate Education for Future Research Biologists, The National Academies Press, Washington, DC, 2003.
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From page 339... ...
Recognizing that students might require competence at multiple levels depending on their needs, the BIO2010 report identified three levels of competence as described in Box 10.4. Box 10.3 Essential Concepts of Computer Science for the Biologist Key for the computer scientist is the notion of a field that focuses on information, on understanding of computing activities through mathematical and engineering models and based on theory and abstraction, on the ways of representing and processing information, and on the application of scientific principles and methodologies to the development and maintenance of computer systems -- whether they are composed of hardware, software, or both.
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Algorithmic thinking, information representation, and computer programs are themes central to all subfields of computer science and engineering research. They also provide material for intellectual study in and of themselves, often with important practical results.
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Kasif, eds., Elsevier Science Ltd., New York, 1998. 10.2.2.4 Graduate Programs Graduate programs at the BioComp interface are often intended to provide B.S.
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In addition, all students take an introductory course in computational science. Dissertation research is supervised by a committee with faculty members as required by the student's home department, but with representation from the computational biology faculty at other Keck Center institutions as well.
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in computer science, biology, or math as a prerequisite; to produce a well-rounded computational biologist will require very different training programs. The University of Colorado's certificate program in computational biology requires incoming students to take preparatory classes in "Biology for Computer Scientists," "Computer Science for Bioscientists," or "Mathematics for Bioscientists," depending on what the student missed earlier in his or her education.
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From page 344... ...
and Canadian academic institutions in developing interdisciplinary graduate and postdoctoral training programs for individuals with backgrounds in the physical, computational, or mathematical sciences to pursue biological questions. For example, pre- and postdoctoral fellows at the La Jolla Consortium and the University of Chicago's Institute for Biophysical Dynamics had to propose research projects that required the participation of two mentors-one from the quantitative sciences and one from the biological sciences -- before being awarded financial support.
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The committee was unable to find programs specifically oriented toward retraining computer scientists to do biological research. However, the National Science Foundation (NSF)
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In these instances, absent a center, it is difficult to unify and coordinate research and educational activities or to convey to the outside world what the university is doing in the area. Centers are intended to be focal points for research at the BioComp interface (most often with a bioinformatics or computational biology flavor)
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is a campus-wide education and research program that links biologists, computer scientists, and engineers in a multidisciplinary approach to the systematic analysis of complex biological phenomena.18 CSBi places equal emphasis on computational and experimental methods and on molecular and systems views of biological function. CSBi includes about 80 faculty members from more than 10 academic units in science, engineering, and management.
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CSHL was established in 1889 with missions in biological research and education. In 1993, it began the annual Cold Spring Harbor Symposium on Quantitative Biology.
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:330-335, 2001. 27For example, the Tufts Center for the Study of Drug Development estimates the cost of a new prescription drug at $897 million, a figure that includes expenses of project failures (e.g., as those drugs tested that fail to prove successful in clinical trials)
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Blue Gene is designed in part to be able to simulate the molecular forces that occur during protein folding, in order to better understand how a large protein shape emerges from a peptide sequence.28 Blue Gene is only one project, albeit the best known, of IBM Research's Computational Biology Center. This is a group of approximately 35 researchers who are investigating computational techniques in molecular dynamics, pattern discovery, genome annotation, heterogeneous database techniques, and so forth.
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From page 351... ...
For example, in December of 2001, Amgen announced that it was buying the bioinformatics-rich biotech company Immunex Corp for $16 billion.31 Genentech highlights its own bioinformatics capabilities as a key part of the research portfolio.32 However, while these firms and the pharmaceutical giants are clearly great consumers of bioinformatics software and human resources, it is less clear to what extent they are performing original computational biology research. A second wave of companies was founded in the 1990s, in the era of the Human Genome Project and the increase in availability of information technology.
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While many IT vendors are developing and pushing their grid platform, Stanford has been running Folding@Home, a screen-saver that anyone can download and run on a home computer, which calculates a tiny piece of the protein folding problem.36 10.2.5 Funding and Support Both the federal government and private foundations support research at the BioComp interface. (The latter can be regarded as an offshoot of the historically extensive foundation support for biology research.)
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10.2.5.2 Federal Support A variety of federal agencies support work at the BioComp interface, and this support has grown over time. 10.2.5.2.1 The National Institutes of Health For computational biology (i.e., the computing-to-biology side of the BioComp interface)
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In addition to these institutional entities, NIH has created a set of programmatic initiatives to promote quantitative, interdisciplinary approaches to biomedical problems that involve the complex, interactive behavior of many components.38 One initiative consists of a variety of programs to develop human capital, including those for predoctoral training for life scientists in bioinformatics and computational biology,39 support for short courses on mathematical and statistical tools for the study of complex phenotypes and complex systems,40 postdoctoral fellowships in quantitative biology,41and support for a period of supervised study and research for professionals with quantitative scientific and engineering backgrounds outside of biology or medicine who have the potential to integrate their expertise with biomedicine and develop into productive investigators.42 The National Library of Medicine supported awards for predoctoral and postdoctoral training programs in informatics research oriented toward the life sciences (originally medical informatics but moving toward biomedical informatics in its later years) .43 A second group of programs is targeted toward specific problems involving complex biomedical systems.
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The most significant BioComp initiative within the Roadmap, however, is the Bioinformatics and Computational Biology program. This program seeks to create and support a National Program of Excellence in Biomedical Computing (NPEBC)
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The BIO directorate ended this program in 1999,50 not because the research no longer deserved funding, but because computational biology had "mainstreamed" to become an important part of many other biological research activities, particularly environmental biology, integrative biology, and molecular and cellular biosciences. NSF does, in its Biological Infrastructure Division, maintain a biological databases and informatics program that funds direct research into the creation of tools and datasets.
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NSF is supporting this research through its Assembling the Tree of Life program, funded at $29 million; databases will contain molecular, morphological, and physiological evidence for placing taxa in relationship to other taxa. Current algorithms and data structures do not scale well at the number of taxa and data points necessary, so both computational and biological research is necessary to achieve this grand challenge.
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for a total of $103 million over the period from 2002 to 2007. In the project descriptions of the winners, four included "computational models" as part of their charge.59 A second DOE effort is the Microbial Genome program, which spun off from the Human Genome Project in 1994.
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This research area, largely supported under DARPA's biocomputation program,61 was described in Section 8.4. Managed out of DARPA's Information Processing Technology Office (IPTO)
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This program is using two specific challenge problems to motivate research into technologies for designing novel proteins for specific biological purposes. Such design will require advances in computational models, as well as knowledge of molecular biology.
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In many cases, these programs target private industry as well as the more engineering-oriented academic institutions. 10.3 BARRIERS Because work at the BioComp interface draws on different disciplines, there are barriers to effective cooperation between practitioners from each field.
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In contrast to the theoretical computer scientist's idea of formal proof, biologists and other life scientists rely on empirical work to test hypotheses. Because accommodating a large number of independent variables in an experiment is expensive, a common experimental approach (e.g., in medicine and pharmaceuticals)
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Thus, successful researchers and practitioners at the BioComp interface must be willing to approach problems with a wide array of methodologies and problem-solving techniques. Computer scientists often may be specialists in some specific methodology, but biological research often requires the coordination of multiple approaches.
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However, later work has shown that the biological solution to the pattern formation problem is inelegant and "kludgy", with many "redundant" or "inefficient" parts.1 · A senior computer scientist faced the issue of how one might infer the structure of a genetic regulatory network from data on the presence or absence of transcription factors. In a cell, a set of genes interact to produce a protein -- and the transcription factors (themselves proteins)
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Biological researchers are beginning to see the potential explanatory value of computational and mathematical approaches -- a potential that is less apparent than might be expected because of the very success of an empirical approach to biology that has been grounded in experiment and observation for many decades. 10.3.1.4 Data and Experimentation As mentioned above, computer scientists and biologists also view data quite differently.
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The central role that experimental data plays in biology is responsible for the fact that, to date, computer scientists have been able to make their most important contributions in areas in which the details of some biological phenomena can be neglected to some important extent. Thus, the abstraction of DNA as merely a string of characters derived from a four-letter alphabet is a very powerful notion, and considerable headway in genomics can be made knowing little else.
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· Computer scientists are trained to take categorical statements literally, whereas biologists use them informally. 10.3.2 Differences in Culture Another barrier at the BioComp interface is cultural.
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But because most of the intellectual credit inheres in the prototype (e.g., papers for publication and promotions) , research computer scientists have little motivation to move from the prototype system, which can generally be used only by those familiar with the quirks of its operation, to a more robust system that can be used by the broader community at large.
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One disadvantage is that by engaging individuals at the beginning of their careers, the biologist is deprived of the intellectual maturity and insight that generally accompanies more seasoned computer scientists -- and such maturity and insight may be most necessary for making headway on complex problems. The integration of computational expertise into a biological research enterprise can be undertaken in different ways.
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This example suggests a view of mathematics and computer science that is ancillary and peripheral to the "real" substance of biology. The fact that computing and mathematics have developed powerful tools for the analysis of biological data makes it easy for biologists to see the computer scientist as the data equivalent of a lab technician.
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Still a third point is the recognition that while primary data generation and experiment remain important to the life sciences, analytical work on existing data can be every bit as valuable -- bioinformatics is not simply "taking someone else's data." This last point suggests a more subtle risk in partnerships -- that a person with specialized skills may be regarded as a technician or a stand-alone consultant rather than as a true collaborator. 10.3.3 Barriers in Academia One important venue for research at the BioComp interface is academia.
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If these faculty are relatively uninformed or disconnected from ongoing research at the BioComp interface, the needs and intellectual perspectives of interface researchers will not be fully taken into account. 10.3.3.2 Structure of Educational Programs Stovepiping is also reflected in the structure of educational programs.
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10.3.3.3 Coordination Costs In general, the cost of coordinating research and training increases with interdisciplinary work. When computer scientists collaborate with biologists, they also are likely to belong to different departments or universities.
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Furthermore, they increase the difficulty of developing mutual regard and common ground, and they lead to more misunderstandings.78 Coordination costs can be addressed in part through changes in technology, management, funding, and physical resources. But they can never be reduced to zero, and learning to live with greater overhead in conducting interdisciplinary work is a sine qua non for participants.
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10.3.3.7 Local Cyberinfrastructure Section 7.1 addressed the importance of cyberinfrastructure to the biological research enterprise taken as a whole. But individual research laboratories need to be able to count on the local counterpart of community-wide cyberinfrastructure.
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10.3.4.2 Reduced Workforces The cultural differences between life scientists and computer scientists described in Section 10.3.2 have ramifications in industry as well. For example, a sense that bioinformatics is in essence technical work or programming in a biological environment leads easily to the conclusion that the use of formally trained computer scientists is just an expensive way of gaining a year or two on the bioinformatics learning curve.
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But the nature and scale of this support vary by agency, in terms of the procedures for making decisions about what proposals are worthy of support. 10.3.5.1 Scope of Supported Work For example, although the NIH does support a nontrivial amount of work at the BioComp interface, its approach to most of its research portfolio, across all of its institutes and centers, focuses on hypothesis-testing research -- research that investigates well-isolated biological phenomena that can be controlled or manipulated and hypotheses that can be tested in straightforward ways with existing methods.
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In particular, a plausible and well-supported computational hypothesis may be as important as a biological one and may be instrumental in advancing biological science. Today, a biological research proposal with excellent computational hypotheses may still be rejected because reviewers fail to see a clearly articulated biological hypothesis.
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No better example can be found than the reactions in many parts of the life sciences research community to the Human Genome Project when it was first proposed -- with a projected price tag in the billions of dollars, the fear was palpable that the project would drain away a significant fraction of the resources available for biological research.79 Work at the BioComp interface, especially in the direction of integrating state-of-the-art computing and information technology into biological research, may well call for support at levels above those required for more traditional biology research. For example, a research project with senior expertise in both biology and computing may well call for support for co-principal investigators.
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These program managers would be characterized primarily by an outstanding ability to develop or recognize unusual concepts and approaches to scientific problems. Review panels constituted outside the standard peer review mechanisms and specifically charged with the selection of high-risk, high-payoff projects would provide advice and input to program managers, but decisions would remain with the program managers.
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. In principle, this is not particularly different at the BioComp interface than in any other research area of commercial value.
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