Computational Biology
COMPUTATIONAL BIOLOGY Biology as a science is in the midst of a major transition, as modern experimental methods are generating data at an unprecedented rate. The availability of this data is leading to the development of quantitatively detailed models of complex biological systems and associated computational approaches to the study of biology.
The Stony Brook Department of Applied Mathematics and Statistics houses a strong research program in Computational Biology. Our faculty's interests span the full range of biological problems: genomic analysis and data-mining, computational structural biology, structure-based drug design, signaling and gene-regulatory networks, and cell and tissue models. For more information on computational biology research projects, see Computational Biology projects. Further, Applied Math faculty and graduate students collaborate with faculty and graduate students in biomedical science departments to offer a rich multi-disciplinary training in computational biology. For example, lab rotations for Applied Math computational biology students involve faculty labs across the University and at nearby Cold Spring Harbor Laboratory.
A number of Stony Brook interdisciplinary biological initiatives foster close interactions between computational and experimental scientists. Among these are the Institute of Chemical Biology and Drug Discovery, the Centers for Molecular Medicine and programs at nearly Brookhaven National Laboratory (which is managed by Stony Brook).
Masters and Doctoral Program Requirements in the Computational Biology Track The standard professional degree in computational biology and biomedical sciences is a Ph.D. The computational biology track also offers a M.S. degree, consisting of 30 credits of graduate coursework with no thesis. The core courses required for the M.S. degree in computational biology are also taken by all PhD students in this track in preparation for the PhD qualifying examinations. For more details about requirements for the Ph.D., please see Ph.D. Requirements.
Course Requirements
The required courses provide computational biology students with the fundamentals of both biology and applied mathematics, as well as in specific methods and applications in computational biology. It is expected that the student will choose a set of electives to provide in depth specialization. Students with less formal training in either math or biology may wish to audit an undergraduate course concurrently with, or prior to, taking the graduate level courses.
Core Courses
Fundamentals of Applied Math
AMS 507 Introduction to Probability
AMS 510 Analytical Methods for Applied Mathematics and Statistics
AMS 530 Principles in Parallel Computing
Fundamentals of Biology
CHE 541/MCB 520 Graduate Biochemistry (an alternate graduate level cell or developmental biology course may be substituted with permission)
Methods of Computational Biology
AMS 532 Journal Club/Lab Rotations in Computational Biology (2 semesters)
AMS 533 Numerical Methods and Algorithms in Computational Biology
AMS 535 Introduction to Computational Structural Biology and Drug Design
CSE 549 Computational Biology
Electives
AMS 536 Molecular Modeling of Biological Molecules
AMS 537 Dynamical Models of Gene Regulation and Biological Pattern Formation
AMS 538 Methods in Neuronal Modeling
Electives may also be chosen from any area relevant to computational biology, based on the specific interests of the student. The student is encouraged to consult with a faculty adviser in advance of choosing electives. Likely areas of specialization may include:
Computational applied math.
Optimization and simulation of complex systems.
Structural biology/biochemistry.
Developmental/cell biology.
Biostatistics.