CSE 527 Computational Biology

Syllabus (tentative)

• Introduction (1 class)

  • Short intro to molecular biology

  • Overview of the topics covered: genetics, systems biology, predictive medicine

• Introduction to probabilistic models for computational biology (2 lectures)

  • Bayesian network representation

  • Maximum likelihood estimation, Expectation maximization algorithm

• Genetics (5 lectures)

  • Genetics basics, QTL mapping

  • Human genetics basics, genome-wide association studies (GWAS)

  • Haplotype reconstruction

  • Imputation, tagging SNP selection

  • Other issues in GWAS, two-point linkage analysis

• Systems biology (7 lectures)

  • Gene regulation basics, microarray data anslysis

  • Learning transcriptional regulatory networks

  • Systems genetics, network medicine

  • Finding regulatory motifs

  • Inferring signaling networks

• Sequence analysis (5 lectures)

  • Sequencing

  • DNA, RNA sequence analysis

Dates for Assignments

• Problem set 1: out 10/12. due 10/26.

• Problem set 2: out 10/26. due 11/9.

• Problem set 3: out 11/9. due 11/23.

• Problem set 4: out 11/23. due 12/7.

Dates for Project Assignments

• Project proposal: due 10/21.

• Midterm report: due 11/16.

• Final report: due 12/14.

Late Day Policy

All assignments are due 11:59pm on the assigned due date. We recognize that students may encounter unexpected circumstances and so require more flexibility in the course schedule. Therefore, each student will be granted a total of 3 free late (calendar) days that can be applied to every assignment, but not the final report submission.