BICH/GENE 419 Computational Techniques for Evolutionary Analysis
Fall 23

Goal

As the increased availability of genome-scale data allows comparisons across a large number of genomes, it becomes possible to study evolutionary mechanisms in detail, which include conservation, duplication and specialization of genes and proteins. The study of evolution becomes a sub-area within computational biology, which involves the application of computational techniques to solve problems in biology.

In the past, computational biology was considered to be a sub-field within theoretical computer science, since many problems employ techniques in algorithm analysis. More recently, multi-disciplinary collaborations become the norm, which include participations of mathematicians, statisticians, biologists and biochemists.

The course will investigate the types of questions that can be addressed through the application of computational techniques, and algorithms for construction and analysis of evolutionary relationships. No programming experience is needed. The course will focus on understanding the main ideas gehind these approaches.

Topics

• Sequence alignment.
• Motif finding.
• Phylogenetic footprinting.
• Phylogenetic tree construction.
• Clustering and protein classification.
• Gene clustering within genomes.
• Genome rearrangement.
• Biological network analysis.
• High-throughput sequencing.

Grading

• Homework assignments (60%): short written assignments handed out every one or two weeks.
• Two midterms (20% each).

Prerequisites

• Junior or senior classification or approval of instructor.

References

• Jones N.C. and Pevzner P.A. (2004) An Introduction to Bioinformatics Algorithms (Computational Molecular Biology). The MIT Press.
• Mount D.W. (2004) Bioinformatics: Sequence and Genome Analysis, Second Edition. Cold Spring Harbor Laboratory Press.
• Cormen T.H., Leiserson C.E., Rivest R.L. and Stein C. (2009) Introduction to Algorithms, Third Edition. The MIT Press.