Optimization Models in Computational Biology

This course will introduce a series of optimization models that find applications to various problems in bioinformatics and computational biology. The basics of the following topics will be covered: dynamic programming; graph algorithms (paths and flows); clustering and trees; linear, non-linear, and integer programming (including convex polytopes); certain probabilistic models; and very limited algebraic statistics. The applications of these optimization models to bioinformatics and computational biology will be illustrated by studying problems such as sequence motif search, DNA sequence alignment (including parametric sequence alignment), recombinations and other related phylogenetic problems, protein sequencing, and protein structure prediction (including side-chain positioning, scoring functions for threading, molecular dynamics etc.).
Course material created by Professor Bala Krishnamoorthy.

Target audience: Graduate

Institution: Washington State University

Materials available: Problem sets or projects, Course outline or syllabus, Textbook recommendations

Products: MATLAB