Algorithms-for-Computational-Biology

#######

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.

Projects

• siteEMAssignment.m : Implemented MEME algorithm for finding multiple motifs in biopolymers.
• hmmDecodeAssignment.m : Implemented the Viterbi algorithm for HMM decoding.
• hmmPosteriorDecodeAssignment.m : Implemented Baum-Welch algorithm for unsupervised parameter estimation.
• alignment-Dist-1.1.m : Implemented the Smith-Waterman algorithm for local alignment of two DNA sequences.
• repressionSimulation-Dist-1.0.m : Simulated a system of any number of interacting repressors.
• repressionSystem-Dev-1.2.m : Modeled a system of repressors at steady state