Course Summary

Analysis of biological networks requires a combination of tools from all branches of applied mathematics and engineering. The course will introduce computational techniques for the analysis of structure and function of genetic, biochemical, and cellular networks. The topics covered include dynamics of intracellular networks, network identification from gene expression data, deterministic and probabilistic tools for networks inference and simulation.

This course is lecture-based and is appropriate for graduate and upper-level undergraduate students with some computational background.  Introduction to biology will be provided. 

Administrative Information

Lectures: Wed 1:00-3:20pm, Room: CS 401

Professor: Stanislav Shvartsman (stas@) & Olga Troyanskaya (ogt@)

Graduate Coordinator: Melissa Lawson - 310 CS Building - 258-5387 mml@cs.princeton.edu

Teaching Assistants: TBA

Course schedule

1. Feb 4 – Introduction to networks and biology. From Molecular to Modular Cell Biology. Biology primer. Why is it important to look at networks?  (Olga)
2. Feb. 11^th – Mechanistic models (Stas)
3. Feb 18^th  - Graphical and signal processing level models. Brief description of microarrays. Brief introduction to statistics. Identification of differentially expressed genes and pitfalls. Clustering to identify groups of genes that can be potentially linked into networks. (Olga)
4. Feb 25^th – Functions of small networks – taking a derivative, measuring the duration of a signal, switching, oscillators. (Stas)
5. March 3 – Robustness in all kinds of models. Computational and experimental examples. Chemotaxis – experiment and modeling.(Stas)
6. March 10 – Gene function identification. Guilt by association and data integration.  Bayesian models (Olga). 
7. March 17 – Spring break - no class.
8. March 24 – Gene function identification from diverse data (cont.).  Modules from co-expression and promoters (Olga). 
9. March 31 – Comparative genomics, multi-species comparison (Olga)
10. April 7 – Davidson and Boluri and cis-regulatory logic, sea urchin experiments, and promoter bashing experiments. (Stas)
11. April 14 – Developmental networks. (Stas)

Last two lectures – student presentations and potentially outside speaker.

HOMEWORK

Homework 1, due Friday 3/26.  Also download the dataset for Problem 3.

Homework 2, due Wednesday 5/3. 

Final Project:

For the final project, each student must write a 5-page review of one of the topic areas below.  Your review should include an introduction, where the topic is presented, and the general goals and challenges of this research area are discussed.  The rest of the review should present and discuss the current "state of the art" in the area - what are the trends in resent publications, what problems are solved and which problems remain to be solved, what are advantages and disadvantages of each method.  You should include at least 5 recent (2-4 years old) papers in your review.  The papers are due on May 19th, please slide them under Stas's or Olga's office door by 5pm (on honor code) that day.

Review topics:

1.  Prediction of gene function on genomic level.

2.  Using microarray data to construct regulatory networks.

3.  Regulatory elements identification (computational and experimental).

4.  Dynamics of signal transduction networks

5.  Experimental analysis of noise in genetic networks.