About the Keck Computational Biology Training Program (KCB)
The W. M. Keck Computational Biology Training Program supports pre- and postdoctoral fellows training at the interfaces in biological and biomedical sciences, mathematics, physics, chemistry, computational sciences, and engineering. The training program integrates students in computational and mathematical research with those in biomedical and biological research in particular areas of emphasis. These areas include:
- Structural Biology
Using such techniques as X-ray crystallography, electron cryomicroscopy, NMR spectroscopy, and magnetic resonance imaging, structural biology provides detailed information at multiple levels ranging from biological molecules to animals. This new understanding, which continues to astound researchers in its richness, is key to effective drug design, and to international efforts in the completion of structural genomics.
- Molecular Biophysics
Molecular biophysics connects the microscopic atomic properties of atoms within biomolecules with experimentally observable functional and structural properties. The ability to construct simulations aimed at uncovering the relationship between design and function helps identify the ways in which the functional properties can be altered selectively.
- Bioinformatics and Computational Genomics
Mathematical, statistical, and computer methods are integrated to analyze biological, biophysical, and biomedical data. An important focus for the future is distilling massive data—such as that provided by genome projects—into key principles. Computational approaches to diagnosis and treatment, as well as the management of patients’ data, are other important applications.
- Array Technologies/Proteomics
The ability to detect and analyze expression of proteins in different tissues in a spatial- and temporal-specific manner is essential to our understanding of genetic information and how it is utilized. Microarray technology is an integral aspect of assessing protein/mRNA expression, and development of automated systems for the generation and analysis of arrays is an essential component of modern biomedicine.
- Bioengineering
The combination of engineering goals—to develop a product or process—with biology is an essential component of biomedicine in the future. This area encompasses the design and production of recombinant proteins as pharmaceutical and industrial agents, tissue engineering to provide organs for “replacement” parts, micro- and nano-robotic systems for early diagnosis and treatment of disease, detection systems for pathogens, and noninvasive methods to monitor biological processes.
- Computational Methodologies and Algorithms
These enabling technologies form the basis of the new tools to be developed. The development of computational methods, and a systems engineering approach to model complex biomolecular networks and systems, is a crucial step in finding the approaches that are effective in dealing with the informational challenges that are increasingly a part of modern biomedicine.
These areas together encompass the intellectual activities needed to ask incisive biological questions, develop appropriate algorithms based on the related biological data, and then to execute the biologically derived set of rules using the latest computational methods. Trainees in the program work alongside established researchers in a number of areas including:
- Development of new techniques for structural determination of biological complexes, including novel uses of three-dimensional electron cryomicroscopy, X-ray crystallography, and magnetic resonance methods.
- Development of advanced simulations for interpretation of structural data, including molecular dynamics, Brownian motion, signal transduction networks, cell modeling, genetic information propagation, and other biological phenomena.
- Development of appropriate numerical and computational techniques to support biological and computational applications.
- Development of DNA and protein sequence analysis, including alignment of sequences for genome construction, identification of coding regions, homology searching, and prediction of structure and function of proteins and protein complexes.
Pre-doctoral Training
The curriculum for the pre-doctoral students is carefully crafted for each student based on his or her transcript, prior field(s) of study, research interests, and long-term career goals. Each student’s personalized course plan must be approved collectively by the Training Directors and must include three courses outside the home discipline. Each student has an individual faculty mentor and is also overseen by a faculty committee comprising individuals from both the home department and from the larger consortium.
Students are also required to attend weekly seminars, workshops, annual conferences, and to present their work at least once a year in student seminars, programs, or research conferences. This degree of attention to each student’s training takes considerable time to administer and implement, and is well outside the norm for training programs in this area. However, this method has been demonstrated to give each student depth in the home discipline, without sacrificing breadth in the complementary area.
Postdoctoral Training
The post-doctoral training program provides effective polish for maturing junior scientists. To be admitted to the program, each applicant must pass a rigorous set of interviews with the Training Directors from each institution (with final decisions made by the Executive Committee), as well as give an open seminar on previous and proposed work at the boundary between biology and the computational sciences. During the fellowship, each postdoctoral fellow is required to have two mentors, one in a biological science and one in a computational, chemical, or physical science.
Taken together, the pre- and postdoctoral training components under the Keck Center for Computational Biology form the vibrant core of the Gulf Coast Consortia. The quality of the trainees helps to attract new faculty; further, the trainees themselves help to form important living ties among the participating institutions. By being encouraged to form interesting hypotheses, the trainees often chart new research directions, leading to new combinations of faculty teams.
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