Bio E 241

• BioE241
  • Class info
    • Syllabus
      • Paper review sessions
      • Programming exercises
    • Lecture notes
    • Other materials
    • Recommended textbooks

Class info

• Title: Probabilistic Modeling, Genomics and Jurassic Park
• Instructor: IanHolmes
• Class: BioE 241
• When: Spring semester, 2009
  • Lectures: Tue/Thurs 11-12:30, 7 Evans Hall
• Instructor blog -- please do post comments/questions.

In this class you will use statistics, DNA sequencing and/or paleontology to reconstruct the genomes of organisms that were ancestral to multiple present-day species. Aspects of reconstruction that will be considered include inference algorithms; confidence estimates; detail at multiple levels (DNA sequence, genes, pathways, systems); theory and in silico experiments to investigate the accuracy of sequence reconstruction algorithms; and experimental approaches to synthesis and empirical investigation of reconstructed sequence.

This page is currently under review. Here is some older archived material: Bio E 241 Fall 07.

Syllabus

The syllabus for this class is loosely based on the syllabus for my Compbio Models Class whose theme is statistical inference in molecular evolution, but some additional material pertaining to reconstruction theory & experiments will also be covered.

Paper review sessions

See the BioE241Presentations page.

Programming exercises

See the BioE241Projects page.

Lecture notes

• Main lecture notes. ~90 pages (PDF)

• Introductory lectures on molecular biology & databases, taken from BioE131 (Powerpoint)

• Notable HMMs in bioinformatics (Powerpoint)
• MCMC sequence alignment (Powerpoint)

• RNA grammars (PDF)
• Statistical alignment of RNA (PDF)

Other materials

• Basic Matrix Theorems by Richard Quandt
• MacKay's ITILA book

• That Stanford ribo-happening video

Recommended textbooks

Primary texts (probabilistic modeling and bioinformatics)

• The Durbin Book. Durbin, Eddy, Krogh and Mitchison. Biological Sequence Analysis. ISBN:0521629713

• The MacKay Book. MacKay. Information Theory, Inference and Learning Algorithms. ISBN:0521642981
  • Can be downloaded from here (copyright grants permission to view but not print)

Background on molecular evolution & algorithms for doing it:

• Felsenstein. Inferring Phylogenies. ISBN:0878931775
• Graur and Li. Fundamentals of Molecular Evolution. ISBN:0878932666

For continuous-valued Markov processes, Gaussian & otherwise:

• Van Kampen. Stochastic Processes in Physics and Chemistry. ISBN:0444893490
• Gardiner. Handbook of Stochastic Methods. ISBN:3540208828

• Rasmussen and Williams. Gaussian Processes for Machine Learning. ISBN:026218253X

Computational neuroscience:

• The Spikes Book. Rieke, Warland, de Ruyter van Steveninck and Bialek. Spikes: Exploring the Neural Code. ISBN:0262681080
  • An excellent introduction to information theory & Bayesian analysis in computational neuroscience.

• Eliasmith and Anderson. Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems. ISBN:0262050714
  • Also has links on the Spikes book page