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Computational and Systems Biology Program |
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Graduate Student Coordinator: Melanie Puhar
Computational Biology Faculty Director: Barak Cohen
Computational and Systems Biology Program Guidelines
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The goal of the Computational and Systems Biology Program is to train the next generation of scientists in technology intensive, quantitative, systems level approaches to molecular biology. We aim to graduate students who are as comfortable operating the latest high end instrumentation as they are manipulating the mathematical formalisms that are required to make sense of their data. It is our hope that the students who join the Computational and Systems Biology Program will apply these approaches to unraveling the complex genetic circuits that control the cell.
Technological advances are having a major impact on molecular biology. Advances in experimental techniques mean that large amounts of sequence, expression, and localization data are now routinely gathered by individual investigators. In addition terabytes of these kinds of data are stored in various public and private databases. Concurrently, access to large scale computing resources has become more and more common in molecular biology laboratories. Students in the Computational and Systems Biology Program will learn to leverage these advances in both experimental and computational resources.
Faculty in the Computational and Systems Biology Program work on a variety of different biological problems, but in most cases students will find a tight coupling between computational and experimental approaches. Some of the general areas in which faculty work include
Large-scale genetic network analysis and reconstruction.
Technology development for high-throughput collection of genetic and biochemical data
Molecular modeling of genetic regulatory circuits
Real time, single cell analyses of genetic regulatory circuits
Specificity and evolution of DNA-protein interactions
Algorithm development for comparison of DNA, RNA, and protein sequences
Population genetic analysis of genetic variation
Functional genomic approaches to disease gene identification
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. Program students come from undergraduate disciplines including biology, genetics, biochemistry, computer science, mathematics, statistics, physics and others. 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)
Genomics (Bio 5488)
Students who enter the program needing additional training in computer science 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.
In consultation with their advisors, students choose a minimum of three advanced electives or special topics. 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)
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 one especially suited for students in the Program is:
Special Topics in Computational Biology (Bio 5497)
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 and Systems 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 also required to take an Ethics course (Spring of 2nd year - Bio 5011) and Teaching assistantship (Fall or Spring of 2nd year - Bio 5915).
Computational and Systems 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 metagenomics |
| 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 anlyses of regulatory networks, models of complex traits and genetic variation |
| Joseph C. Corbo, M.D., Ph.D. - Transcriptional regulatory networks in photoreceptor development, evolution, and disease. |
| Gautam Dantas, Ph.D. - Engineering microbial biofuel catalysts and characterizing microbial reservoirs of antibiotic resistance |
| Enrico Di Cera, M.D. - Structure and function of proteases; protein engineering; allosteric enzymes |
| Issam M. El Naqa, Ph.D. - Computational and system 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. |
| Jeffrey I. Gordon, M.D. - Genomic and metabolic foundations of symbiotic host-microbial relationships in the mammalian gut |
| James J. Havranek, Ph.D. - Structural modeling, experimental characterization, and engineering of protein-DNA interactions |
| Timothy E. Holy, Ph.D. - Neural mechanisms of pheromone detection, recognition, and olfactory learning; novel optical methods for recording neuronal activity |
| Shin-ichiro Imai, M.D., Ph.D. - Understanding the molecular mechanism of aging and longevity in mammals. |
| Allan Larson, Ph.D. - Molecular population genetics and phylogenetics of amphibians and reptiles. |
| Jr-Shin Li, Ph.D. - Control and systems theory with applications to spectroscopy, imaging, and computation |
| 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 annotation, 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. |
| Sheila A. Stewart, Ph.D. - Telomere biology in human cancer and aging. |
| Gary D. Stormo, Ph.D. - Computational biology, bioinformatics, protein-DNA interactions, RNA structure prediction, gene regulation. |
| Yinjie Tang, Ph.D. - Bioremediation of toxic compounds and metabolic engineering of environmental microorganisms for biofuel production |
| 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. |
| George M. Weinstock, Ph.D. - Genomic and computational approaches to human and microbial biology |
| 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:
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.
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