Bio E 241

• BioE241
  • Class info
    • Announcements
  • Teaching materials
    • Homework assignments
      • Paper review sessions
      • Programming exercises
    • Lecture notes
    • Other materials
    • Recommended textbooks
  • Brief description of syllabus
    • Genome evolution and ecology
    • Grammars for biological sequences
    • Probabilistic modeling
  • Extensions

Class info

• Title: Probabilistic modeling in computational biology
• Instructor: IanHolmes
• Class: BioE 241
• When: Fall semester
  • Lectures: MF 11-12:30, 621 Stanley Hall
    • Note room change (previously 425 Hearst Mining Building)
• Mailing list: bioe241-fall07 lists berkeley edu
  • Either email Ian Holmes, or subscribe yourself at http://lists.berkeley.edu/
• Grading is via 6 exercises. Suggested breakdown:
  • 4 class presentations (list)
  • 2 programming exercises (list)
• Instructor blog -- please do post comments/questions.

Announcements

• Final class presentation details: GraduateFinal
• The first three exercises are now posted on the BioE241Projects page. -- IanHolmes - 15 Sep 2007
• On 1 October, the class will temporarily relocate to 321 Stanley. -- IanHolmes - 14 Sep 2007
• I have now clarified the presentation guidelines on the BioE241Presentations page. -- IanHolmes - 08 Sep 2007
• There is now a class blog summarizing the content of each lecture. Feel free to leave comments. -- IanHolmes - 07 Sep 2007

Teaching materials

Homework assignments

Assessment is via presentations and coding exercises.

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

Brief description of syllabus

Genome evolution and ecology

What are the mathematical dynamics of genome evolution? How does random mutation of strings generate the most effective nanotechnology known? Can we build models of communities of genomes? Map the full spectrum of evolutionary timescales? Predict the course of evolution, or direct it? Reconstruct the past?

How have others responded to these questions?

Grammars for biological sequences

The metaphor of DNA sequence as "the language of life" is so over-used as to have become trite. Yet, remarkably, there are deep mathematical parallels between the information structure of DNA and that of natural language. How do these similarities arise? How can they be measured and put to use, particularly in high-throughput mode?

This class examines these questions from the point of view of someone interested in developing probabilistic modeling algorithms from principles of evolution and biophysics.

We will also examine several other kinds of probabilistic model, useful both in bioinformatics and in other areas of applied machine learning.

Probabilistic modeling

Probabilistic methods - including graphical models, HMMs, Gaussian processes, stochastic grammars, Markov random fields, Dirichlet processes, etc., along with associated algorithms such as Markov Chain Monte Carlo, Expectation Maximization, variational Bayes, etc. - are a mainstay of modern computer science applications. They are natural successors to earlier classes of approach such as expert systems, artificial intelligence and neural networks.

One area in which probabilistic methods have made a particularly strong impact is computational biology. Studying probabilistic algorithms in the context of molecular biology offers a uniquely interesting background to these methods. Not only is the probabilistic analysis directly transferable to other applications in CS and scientific informatics, but the the application to post-genomic biology provides an entry point into such breaking areas as synthetic biology, human genome evolution, molecular ecology or gene circuit analysis.

This class will develop probabilistic modeling techniques, particularly time-evolving random processes and stochastic grammars. A strong emphasis on underlying theoretical techniques will be complemented by reference to working code that can be applied to real problems in phylogenetics and genomic analysis.

A central theme of the course is the increasingly popular use of evolutionary grammars as a foundation for genomics algorithms (see PhylogeneticAlignmentReader). We will also cover other aspects of Stochastic Biology.

Extensions

Each year some new material is added to the class. Here are a few possibilities for this year:

• Transposon-related algorithms. See TransposonAnnotation for a preliminary list
• Reversible jump MCMC (e.g. Huelsenbeck et al on substitution model comparisons)
• Dirichlet processes (e.g. Jordan et al; MLpedia; Duncan Brown and Kimmen Sjolander's alignment models)
• Population genetics, the coalescent & recombination rate (e.g. Li and Stephens, Falush et al; see lit.review in intro to FearnheadDonnelly)
• The junction tree algorithm for graphical models (Jordan; Paskin)
• Variational methods for graphical models (e.g. Jordan et al; Siepel, Haussler et al)
• Other compbio applications of graphical models (c.f. Michael Jordan's CS281A)
• Langevin equations; simulation; Ito vs Stratanovich (Birger; Evans; see also Samoilov et al and other refs on stochastic biology page)

Email IanHolmes if you have preferences among these, and/or wish to lead discussion of paper(s) on this topic for class credit.