|
|
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
- 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
Other materials
- That Stanford ribo-happening video
Recommended textbooks
Primary texts (probabilistic modeling and bioinformatics)
- 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:
For continuous-valued Markov processes, Gaussian & otherwise:
- 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
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:
Email IanHolmes if you have preferences among these, and/or wish to lead discussion of paper(s) on this topic for class credit. |