Algorithms for Computational Biology (CSE 587A)
Computational Molecular Biology (Bio 5495)

Fall, 2017

Subjects covered This course is a survey of algorithms and mathematical methods in biological sequence analysis (with a strong emphasis on probabilistic methods) and systems biology. Sequence analysis topics include introduction to probability, probabilistic inference in missing data problems, hidden Markov models (HMMs), profile HMMs, sequence alignment, and identification of transcription-factor binding sites. Systems biology topics include discovery of gene regulatory networks, quantitative modeling of gene regulatory networks, synthetic biology, and (in some years) quantitative modeling of metabolism.


Students who have not had a programming course do not do well. If this course is required for your graduate program but you have not had a programming course you should take CSE501N first. Programming assignments use Mathematica, aka the Wolfram languge, but you do not need any prior experience with Mathematica. I explain why we use Mathematica here.

Teaching approach Our teaching philosophy emphasizes active learning over lectures. This means you will be expected to learn the basic material outside of class, through reading, working problems, and programming. In class we will emphasize discussion and working on problems and projects. Passive observers never learn much, but in this environment it will be more obvious. If you are registered for the course, active participation in class is an absolute requirement.