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Computational Biology Program |
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Graduate Student Coordinator: Melanie Puhar
Computational Biology Faculty Director: David Sept
Computational Biology Program Guidelines
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 The last twenty years have seen major technological changes in biological research. Raw information in the form of DNA and protein sequences is available in enormous quantities compared to only a few years ago, and is continuing to increase exponentially. Other types of information have also been made much easier to obtain through additional technological advances, and all of biology is undergoing a revolution in the scale of problems that are being addressed. One of the ramifications of this change in scale is the need for computational approaches to extract the most important biological information from the enormous amounts of raw data. At the same time there have been equally impressive increases in the computational power available to the typical scientist. The goal of the Computational Biology Program is to train a new generation of scientists who can bridge the gap between the biological data and the computational methods needed to maximize the utility of that data.
Program students come from a variety of undergraduate disciplines including biology, biochemistry, computer science, mathematics, statistics, physics and others. They receive broad training in all of the disciplines merged together in computational biology, but without the time required to meet all of the traditional requirements of each discipline separately. This requires a new curriculum that is specific to the new field of Computational Biology (or Bioinformatics). Thesis research can be performed with many accomplished faculty within the Program, from a variety of disciplines, as well as with other faculty in the Division.
For information regarding career path and complete program guidelines, click here.
Program of Study
The curriculum is designed to meets the needs of students with a wide variety of backgrounds. The specific courses taken by any student will be determined by that student's needs and interests, in consultation with an advisory committee. All students in the program will take two required courses:
Computational Molecular Biology (Bio 5495/BME 537)
Modeling Biomolecular Systems I (Bio 5476)
Students who enter the program lacking adequate training in computer science will take Fundamentals of Computer Science (CS514), a graduate level course designed by the Computer Science Department for students without an undergraduate computer science degree whose graduate work will involve significant computational activities. Some students will be required to take additional, preparatory computer science classes. All students must take at least one graduate course in computer science.
In consultation with their advisors, students choose a minimum of three advanced electives or special topic. The interdisciplinary nature of the program allows considerable flexibility in choosing these courses, and sometimes more than two courses may be recommended, depending upon the student's needs. Common choices for these advanced electives include the following:
Population Genetics (Bio 4181)
Molecular Evolution (Bio 4183)
Macromolecular Interactions (Bio 5312)
Mathematical Methods for Biophysics and Biochemistry (Bio 5329)
Algorithms for Computational Biology (Bio 5474)
Nucleic Acids and Protein Biosynthesis (Bio 548)
Genomics (Bio 5488)
Advanced Genetics (Bio 5491)
Statistical Thermodynamics (Chem 562)
Statistical Computation (Math 475)
Probability (Math 493)
Mathematical Statistics (Math 494)
Stochastic Processes (Math 495)
Statistical Mechanics (Phys 529)
Intro to Formal Languages and Automata Theory (CS 507)
Information Systems and Database Design (CS 530)
Numerical Methods (SSM 465)
A variety of Division special topics courses are available for students, but courses especially suited for students in the Computational Biology Program are:
Special Topics in Computational Biology (Bio 5497)
Genome Analysis/Functional Genomics (Bio 5498)
All graduate students are encouraged to begin attending relevant journal clubs in their first year of study and to continue participating on a regular and active basis throughout their graduate careers. New journal clubs will no doubt emerge on specialized topics within Computational Biology; one already exists and is well attended by students within the Division as well as students from Computer Science and Biomedical Engineering (see Bio5496/CS6805 Computational Biology Journal Club.) All students are required to take an Ethics course (Spring of 2nd year - Bio 5011) and Teaching assistantship (Fall or Spring of 2nd year - Bio 5915).
Computational Biology Program Faculty |
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| Nathan A. Baker, Ph.D. - The use of theoretical and computational methods to study the physical phenomena underlying the behavior of biological systems. |
| Michael R. Brent, Ph.D. - Systems biology, kinetics of regulatory networks, network inference, adaptive value of regulatory networks, yeast |
| Jeremy D. Buhler, Ph.D. - Developing algorithms for large-scale biosequence comparison, genome annotation, and proteomics. |
| Bruce A. Carlson, Ph.D. - Temporal coding in sensory systems |
| Anders E. Carlsson, Ph.D. - Simulation and theory of actin polymerization processes. |
| Barak A. Cohen, Ph.D. - Genomic and bioinformatic analysis of genetic regulatory networks. |
| Enrico Di Cera, M.D. - Structure and function of proteases; protein engineering. |
| Issam M. El Naqa, Ph.D. - Computational biology and radiation oncology informatics for understanding patients response to cancer treatments. |
| Justin C. Fay, Ph.D. - Population and evolutionary genetics, computational and experimental genomics. |
| Kathleen B. Hall, Ph.D. - We study the interactions between RNA and its binding proteins. |
| James J. Havranek, Ph.D. - |
| Timothy E. Holy, Ph.D. - Neural mechanisms of pheromone detection, recognition, and olfactory learning. |
| Shin-ichiro Imai, M.D., Ph.D. - Understanding the molecular mechanism of aging and longevity in mammals. |
| Mark Johnston, Ph.D. - Signal transduction mechanisms regulating gene expression; comparative genomics. |
| Allan Larson, Ph.D. - Molecular population genetics and phylogenetics of amphibians and reptiles. |
| Elaine R. Mardis, Ph.D. - Robotics and automation for DNA sequencing, genotyping, CNV. |
| Garland R. Marshall, Ph.D. - Molecular recognition is the key to drug design and peptide conformation. |
| Robi D. Mitra, Ph.D. - Technology development for functional genomics and systems biology. |
| Rakesh Nagarajan, M.D., Ph.D. - Bioinformatics analysis of functional genomic, gene annontation, and clinicopathology datasets. |
| Himadri B. Pakrasi, Ph.D. - Systems Biology of photosynthetic organisms. |
| Rohit V. Pappu, Ph.D. - Biophysical studies of protein denatured states, intrinsically disordered proteins, amyloid formation, RNA-protein interactions, and nanoscale self-assembly of charged peptides. |
| Jay W. Ponder, Ph.D. - Computational chemistry, protein engineering, theoretical protein structure and folding. |
| Nancy L. Saccone, Ph.D. - Statistical genetics, complex human diseases, linkage analysis, association studies, analysis methods. |
| David Sept, Ph.D. - Protein-protein and protein-drug interactions. |
| Gary D. Stormo, Ph.D. - Computational biology, bioinformatics, protein-DNA interactions, RNA structure prediction, gene regulation. |
| Alan R. Templeton, Ph.D. - Application of molecular genetic techniques and statistical evolutionary genetics to the study of genotype/phenotype associations, the evolution of the human genome, and the conservation of endangered species. |
| Kurt A. Thoroughman, Ph.D. - Psychophysical and computational investigation of human motor control and learning. |
| David Wang, Ph.D. - Functional genomic approaches to new pathogen discovery. |
| Samuel A. Wickline, M.D. - Molecular imaging and targeted therapeutics with nanobiotechnology. |
| Weixiong Zhang, Ph.D. - Computational approaches to elucidating transcriptional and post-transcriptional gene regulation underlying complex human diseases and plant stress tolerance. |
The following faculty members participate in the Computational Biology Program, but are not affiliated with DBBS:
Charles H. Anderson, Ph.D. - Theoretical models and large-scale computer simulations of sensory, motor and neocortical neural systems.
C. Charles Gu, Ph.D. - Develop and implement statistical algorithms/procedures for genetic/epidemiological study of complex human diseases.
Stan Sawyer, Ph.D. - Statistical inference from DNA sequences; for example, estimation of gene conversion or Darwinian selection.
William Shannon, Ph.D. - The use and development of classification and clustering tools to analyze large biomedical databases.
Subash Suri, Ph.D. - Use of geometric insights and techniques to develop modeling algorithms.
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