15-871(A): COMPUTATIONAL METHODS FOR BIOLOGICAL MODELING AND SIMULATION
INSTRUCTOR: Russell Schwartz
UNITS: 12
SPRING 2007

DESCRIPTION:

This course is designed to teach computational aspects of using modeling and simulation methods to understand biological systems, with an emphasis on practical application. The course will be divided into three general topics: models for optimization problems, simulation and sampling, and model parameter tuning. Specific model types to be covered will include graph models in evolution, string models for biological sequence data, Markov chain Monte Carlo models, hidden Markov models, and discrete-event models, as well as examples of special-purpose models important to specific sub-disciplines of biology. Algorithmic techniques to be studied will include common algorithms for graph optimization problems, mathematical programming methods, event queue data structures, key machine learning methods, commonly used heuristic methods, and methods for generating random numbers and accurately sampling from probability distributions required by or implicit in mathematical models. All of the above will be illustrated with examples from molecular, cellular, or evolutionary biology.

The overall goal of the course is to familiarize students with the computational tools available to them for designing and analyzing models of biological systems. By the end of the course, students should be familiar with a broad range of computational techniques useful in biological modeling and simulation, know how to develop models that take advantage of the available methods, and be able to analyze the expected performance of the models they use. While this course will cover too many topics to teach the theory behind each in depth, students will be sufficiently familiar with the methods covered to be able to apply them and to know where to look for further information.