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
transcriptionfactor 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.
Prerequisites

A formal, collegelevel course in computer programming

A course covering basic molecular biology equivalent to a highschool
Advanced Placement course or an introductory college course

A course covering basic differential and integral calculus (high
school AP or college level)
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.