Graduate School of Arts and Sciences Bulletin of Yale University
 
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Computational Biology and Bioinformatics

300 George Street, Suite 501, 737.6029
http://cbb.yale.edu/
M.S., Ph.D.

Directors of Graduate Studies
Mark Gerstein (Bass 432A, 432.6105, mark.gerstein@yale.edu)
Perry Miller (300 George St., Suite 501, 737.2903, perry.miller@yale.edu)

Professors
James Aspnes (Computer Science), Joseph Chang (Statistics), Ronald Coifman (Mathematics; Computer Science), Lynn Cooley (Genetics; Cell Biology; Molecular, Cellular & Developmental Biology), Donald Engelman (Molecular Biophysics & Biochemistry), Mark Gerstein (Biomedical Informatics; Molecular Biophysics & Biochemistry; Computer Science), William Jorgensen (Chemistry), Douglas Kankel (Molecular, Cellular & Developmental Biology), Kenneth Kidd (Genetics; Ecology & Evolutionary Biology), Paul Lizardi (Pathology), Perry Miller (Anesthesiology; Medical Informatics; Molecular, Cellular & Developmental Biology), Willard Miranker (Computer Science), Anna Pyle (Molecular Biophysics & Biochemistry), Martin Schultz (Computer Science), Gordon Shepherd (Neuroscience), Abraham Silberschatz (Computer Science), Michael Snyder (Molecular, Cellular & Developmental Biology; Molecular Biophysics & Biochemistry), Dieter Söll (Molecular Biophysics & Biochemistry; Chemistry), Günter Wagner (Ecology & Evolutionary Biology), Heping Zhang (Epidemiology & Public Health; Statistics), Steven Zucker (Computer Science; Electrical Engineering; Biomedical Engineering)

Associate Professors
Kei-Hoi Cheung (Anesthesiology; Computer Science; Genetics), Elias Lolis (Pharmacology), Andrew Miranker (Molecular Biophysics & Biochemistry), Michael Stern (Genetics), Hongyu Zhao (Epidemiology & Public Health; Genetics)

Assistant Professors
Thierry Emonet (Molecular, Cellular & Developmental Biology), Steven Kleinstein (Pathology), Michael Krauthammer (Pathology), Steven Ma (Epidemiology & Public Health), Annette Molinaro (Epidemiology & Public Health), Valerie Reinke (Genetics), David Tuck (Pathology)

Fields of Study

Computational biology and bioinformatics (CB&B) is a rapidly developing multidisciplinary field. The systematic acquisition of data made possible by genomics and proteomics technologies has created a tremendous gap between available data and their biological interpretation. Given the rate of data generation, it is well recognized that this gap will not be closed with direct individual experimentation. Computational and theoretical approaches to understanding biological systems provide an essential vehicle to help close this gap. These activities include computational modeling of biological processes, computational management of large-scale projects, database development and data mining, algorithm development and high-performance computing, as well as statistical and mathematical analyses.

To enter the PH.D. program, students apply to an interest-based track within the interdepartmental program in the Biological and Biomedical Sciences.

Special Admissions Requirements

Applicants are expected (1) to have a strong foundation in the basic sciences, such as biology, chemistry, and mathematics, and (2) to have training in computing/informatics, including significant computer programming experience. The Graduate Record Examination (GRE) General Test is required, and the GRE Subject Test in cell and molecular biology, biology, biochemistry, chemistry, computer science, or other relevant discipline is recommended. Applicants for whom English is not their native language are required to submit results from the Test of English as a Foreign Language (TOEFL).

Special Requirements for the Ph.D. Degree

With the help of a faculty advisory committee, each student plans a program that includes courses, seminars, laboratory rotations, and independent reading. Students are expected to gain competence in three core areas: (1) computational biology and bioinformatics, (2) biological sciences, and (3) informatics (including computer science, statistics, and applied mathematics). The courses taken to satisfy the core areas of competency may vary considerably. A typical program will include nine courses. Completion of the core curriculum will typically take three to four terms, depending in part on the prior training of the student. Students will typically take two to three courses each term and three research rotations during the first year. After the first year, students will start working in the laboratory of their Ph.D. thesis supervisor. Students must pass a qualifying examination normally given at the end of the second year or the beginning of the third year. There is no language requirement. Students will serve as teaching assistants in two term courses.

Master’s Degree

M.S. (en route to the PH.D.). To qualify for the awarding of the M.S. degree a student must (1) complete two years (four terms) of study in the Ph.D. program, with nine required courses taken at Yale, (2) complete the required course work for the Ph.D. program with an average grade of High Pass, (3) successfully complete three research rotations, and (4) meet the Graduate School’s Honors requirement.

Courses

CB&B 645b, Statistical Methods in Genetics and Bioinformatics.  Joseph Chang.
TTh 10.30–11.45
Stochastic modeling and statistical methods applied to problems such as mapping quantitative trait loci, analyzing gene expression data, sequence alignment, and reconstructing evolutionary trees. Statistical methods include maximum likelihood, Bayesian inference, Monte Carlo Markov chains, and some methods of classification and clustering. Models introduced include variance components, hidden Markov models, Bayesian networks, and coalescent. Recommended background: STAT 541a, STAT 542b. Prior knowledge of biology is not required. Times to be arranged at organizational meeting. Also BIS 692b, STAT 645b.

CB&B 740a, Clinical and Translational Informatics.  Richard Shiffman, Michael Krauthammer.
HTBA
The course provides an introduction to clinical and translational informatics. Topics include (1) overview of biomedical informatics, (2) design, function, and evaluation of clinical information systems, (3) clinical decision making and practice guidelines, (4) clinical decision support systems, (5) informatics support of clinical research, (6) privacy and confidentiality of clinical data, (7) standards, (8) issues in defining the clinical phenotype, and (9) topics in translational bioinformatics. Permission of the instructor required.

CB&B 750a, Core Topics in Biomedical Informatics.  Perry Miller and faculty.
HTBA
Introduction to common unifying themes that serve as the foundation for different areas of biomedical informatics, including clinical, neuro-, and genome informatics. The course is designed for students with significant computer experience and course work who plan to build computational tools for use in bioscience research. Emphasis is on understanding basic principles underlying informatics approaches to biomedical data modeling, interoperation among biomedical databases and software tools, standardized biomedical vocabularies and ontologies, modeling of biological systems, and other topics of interest. The course involves lectures, class discussions, student presentations, and computer programming assignments. Permission of the instructor required. Also MCDB 750a.

CB&B 752b, Genomics and Bioinformatics.  Dieter Söll, Mark Gerstein, Michael Snyder.
MW 1–2.15
Genomics describes the determination of the nucleotide sequence and many further analyses to discover functional and structural information on all the genes of an organism. Topics include the methods and results of functional and structural gene analysis on a genome-wide scale as well as a discussion of the implications of this research. Bioinformatics describes the computational analysis of genomes and macromolecular structures on a large scale. Topics include sequence alignment, biological database design, comparative genomics, geometric analysis of protein structure, and macromolecular simulation. Prerequisite: EEB 122 and MATH 115, or permission of the instructor. Also CPSC 752bu, MB&B 752bu, MCDB 752bu.

CHEM 526au, Computational Chemistry and Biochemistry.  William Jorgensen.
TTh 9–10.15.
An introduction to modern computational methods employed for the study of chemistry and biochemistry, including molecular mechanics, quantum mechanics, statistical mechanics, and molecular dynamics. Special emphasis is placed on the hands-on use of computational packages for current applications ranging from organic reactions to protein-ligand binding and dynamics.

Additional courses focused on the biological sciences and on areas of informatics are selected by the student in consultation with CB&B faculty.

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