CS791L Biological-Computation and Bio-Informatics

Friday 1:30-3:30 pm
CS building Room 142

Hava Siegelmann

email: hava@cs.umass.edu
Office: Computer Science 344
Telephone: 7-4282
Office Hours: Mon 10:30-12:00

Course Summary

This class is a joint effort between the BIGIALS group and the Computer Science department. All classes are arranged to reach the heterogeneous crowd interested in physical/biological sciences and informatics. The seminar is research oriented and will prepare the participating students and faculty attending it for collaborative research in this interdisciplinary area.

Bio-informatics includes the use of computational and mathematical techniques (e.g., algorithms, machine learning, AI, complexity theory, dynamical systems) to decipher complicated information in biological structures, gene sequences, and cellular signals. It also includes the construction of new technologies that have been overlooked before and whose roots are in nature, as is the aim of the IBM Blue gene project. This interdisciplinary field is of critical importance for understanding information from humongous biological sources, as well as for the design and construction of new computational methods, software, and hardware. Main topics include adaptive computational approaches and AI algorithms for structural and functional biology (e.g., protein structure prediction, prediction of proteins function and interactions prediction, fold prediction) , neural computation, "dynamical diseases" (e.g., finding structure in cardiac rhythms), building evolutionary robots, DNA and quantum computers, and more.

The course will survey leading trends in bio-computation. Some topics will be introduced by invited lecturers who are active and well known in the field.

Assignments and Grades

Grades will be based an in-class, oral presentation, attendance, participation in class discussion, and completing a term project. The presentation will involve reading background material about the work of one of the visiting scholars or on parts of the book Bio-informatics: the machine learning approach' and presenting it in class during the week preceding the guest lecture. A written summary of the presentation will be distributed by the student at the day of the talk. In addition, each student will propose a project related to their presentation, and complete a term paper by May 6, 2002.

Students are advised to read the relevant papers of outside speakers 1 week prior to the presentations, and be ready for a discussion on it. This is the best way of learning from their great experience.

We will also read some chapters from Bioinformatics - The Machine Learning Approach, by Pierre Baldi and Soren Brunak.

Tentative Schedule

2/1 Introduction to Biological computation via Neural Networks Prof. Hava Siegelmann
Computer Science, UMass
2/8 1) Introduction to BioInformatics

2) Combinatorial analyses of genetics regulatory networks
Prof. Ron Adkins
Biology UMass

Dr. Tzachi Pilpel
Harvard University
1) Genetic Algorithms

2) Evolutionary robotics and Computational Design
Prof. Hava Siegelmann
Computer Science, UMass
Prof. Hod Lipson
Mech. E. & Computing & Info. Sci,
Cornell University
2/22 An Introduction to Quantum Computation Prof. Mary Beth Ruskai
3/1 Blue Gene project - IBM Dr. Robert Germain - IBM
3/8 Systems Biology: Active Learning of Cellular Architectures Prof. Simon Kasif
Biomedical engineering-
BioInformatics, BU
3/15 Inferring regulatory networks from multiple constraints Prof. Tommi Jaakkola
Computer Science AI lab, MIT
Quantum Computers II Prof. Seth Lloyd
Mechanical Engineering, MIT
Solvation Effects on Protein folding, binding and design:
exploring the electrostatic balance
Prof. Bruce Tidor
Chemistry, MIT
4/5 "Dynamical" vs. "genetic" disease -
What do complex rhythms reveal about pathophysiology?
Prof. Leon Glass
Department of Physiology
McGill University
4/12 1) Protein Structure Prediction
2) The Scope of Structural Genomic
Prof. John Moult
Biotechnology Institute
University of Maryland
4/19 Context and Chemistry in Olfactory computation Prof. Leslie Kay
Psychology department
University of Chicago
4/26 The cocktail party problem: Animal models of computation
in the auditory cortex.
Prof. Tony Zador
Cold Spring Harbor Lab
Project presentations

Possible additional lecturers:

Talk Abstracts