ECS 221: Computational Methods in Systems & Synthetic Biology

ECS 221
Computational Methods in Systems & Synthetic Biology
Effective Term
2016 Spring Quarter
Learning Activities
Lecture - 3.0 hours
Discussion - 1.0 hours
Computational methods related to systems and synthetic biology. An overview of machine learning techniques related to the analysis of biological data, biological networks. Predictive modeling and simulation of biological systems. Topics on biological circuit construction.
Enrollment Restrictions
Pass 1 and Pass 2 open to Graduate Students in Computer Science only.

Summary of Course Content
The course aims to introduce graduate students to various computational and experimental challenges in systems and synthetic biology. It will (a) help biologists to perform data analysis and create customized programs, and (b) help computer scientists identify important biological challenges to which computational thinking can be applied. The course will focus on state-of-the-art computational methods for the analysis and construction of biological networks, the simulation of biological systems, and the automated design of biological constructs. Students will have to read and present technical papers, and complete two sets of homework and a final project. Guest speakers from Life Sciences will deliver some of the lectures. 1. Introduction to Systems and Synthetic biology (week 1): a. Systems Biology: A network perspective b. Synthetic Biology: How (not) to engineer biological systems 2. Methods in computational biology (week 2 – 4): a. Dynamic programming b. Clustering (K-means, Hierarchical, Bi-clustering, Expectation Maximization) c. Classification (Naïve Bayes, Neural Nets, Support Vector Machines) d. Markov processes e. Hidden Markov Models 3. Biological networks (week 5 – 6): a. Probabilistic reconstruction b. Network analysis and statistics c. Motif finding d. Graphical Models 4. Biological modeling and simulation (week 7 – 8): a. Fundamental regulatory models b. Monte Carlo Method c. Markov Chain Monte Carlo sampling d. Numerical integration: Ordinary and Stochastic Differential Equations 5. Engineering biological systems (week 9 – 10): a. Part standardization and characterization b. Flux Balance Analysis c. Computer Aided Design and Implementation d. Testing and verification e. Ethics

Illustrative Reading
Textbook: None Technical papers and class notes will be used. Optional additional reading from: - C. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2007 - Edda Klipp, Wolfram Liebermeister, Christoph Wierling, Axel Kowald, Hans Lehrach, Ralf Herwig, “Systems Biology: A Textbook”, Wiley, 2009 - Uri Alon, “An introduction to Systems Biology”, Chapman & Hall/CRC, 2007

Potential Course Overlap

Course Category