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ECS 289K COMPUTATIONAL METHODS IN SYSTEMS AND SYNTHETIC BIOLOGY (4) II

Lecture: 3 hours

Discussion: 1 hour

Prerequisite: Course ECS 124 or instructor’s approval

Grading: Letter; project (50%), presentation (30%), class participation (20%)

Catalog Description:
Computational methods related to systems and synthetic biology. An overview of machine learning techniques related to the analysis of biological data (clustering, classification, HMM), biological networks (reconstruction, analysis, motif finding), biological modeling and simulation (Monte Carlo, numerical integration, discrete event simulation), topics on the engineering of biological systems.

Expanded Course Description:

The course will focus on the methods currently used in computational biology regarding the analysis of biological networks, the modeling and simulation of complex biological systems. Students will be introduced to cutting-edge research and will learn about the various computational and experimental challenges in the respective fields. Emphasis will be given to biological networks and the role of evolution in their organization. Students will have to read and present technical papers as well as complete a project. Guest speakers from Life Sciences will deliver some of the lectures.

  1. Introduction to Systems and Synthetic biology:
    1. Systems Biology: A network perspective
    2. Synthetic Biology: How (not) to engineer biological systems
  2. Methods in computational biology:
    1. Dynamic programming
    2. Clustering (K-means, Hierarchical, Bi-clustering, EM)
    3. Classification (Naïve Bayes, Neural Nets, SVM)
    4. Markov processes
    5. Hidden Markov Models
  3. Biological networks:
    1. Probabilistic reconstruction
    2. Conditional independence
    3. Network analysis and statistics
    4. Motif finding
    5. Graphical Models
  4. Biological modeling and simulation:
    1. Fundamental regulatory models
    2. Monte Carlo Method
    3. Markov Chain Monte Carlo (MCMC) sampling
    4. Numerical integration: ODE, SDE
    5. Discrete event simulation
  5. Engineering biological systems:
    1. Part standardization and characterization
    2. CAD Design and Implementation
    3. Testing and verification
    4. Ethics

Textbook:
None

References:
Technical papers and class notes will be used. For additional reading:
- C. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2007
- R. Schwartz, “Biological Modeling and Simulation”, MIT Press, 2008

Project:
Students will have to complete a computational/review project in coordination with the instructor. Teams will try to be balanced with students from various disciplines

Presentations:
Students will present technical papers in coordination with the instructor.

Computer Usage:
Computational projects will need the use of computers and knowledge of basic programming.

Instructor: I. Tagkopoulos

Prepared by: I. Tagkopoulos (November 2009)

Overlap Statement:
There is no significant overlap with other courses.

11/09

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