CS 5984: Computational Systems Biology

T. M. Murali

Spring 2007, Tuesday and Thursday, 9:30AM-10:45AM, McBryde 223

Schedule (subject to change throughout the semester)
Date Topic and papers Presenter(s) (links point to the presentations)
Introduction
Jan 16, 2007 Introduction to Computational Systems Biology T. M. Murali
Jan 18, 2007 Course Topics and Schedule T. M. Murali
Jan 23, 2007 Course Projects
Group assignments
T. M. Murali
DNA Microarray Analysis
Jan 25, 2007 Basic clustering algorithms T. M. Murali
Jan 30, 2007 Basic clustering algorithms T. M. Murali, same lecture as the previous class.
Feb 1, 2007 Application to find cancer gene modules T. M. Murali
Feb 6, 2007 Application to find cancer gene modules T. M. Murali, same lecture as the previous class.
Feb 8, 2007 Biclustering gene expression data T. M. Murali
Feb 13, 2007 Applications of Biclustering to Data Integration in S. cerevisiae T. M. Murali
Feb 15, 2007 Applications of Biclustering to Disease Classification T. M. Murali (no slides)
Predicting Gene Function
Feb 20, 2007 Gene function prediction T. M. Murali
Feb 22, 2007 Gene function prediction T. M. Murali, same lecture as the previous class
Feb 27, 2007 Gene function prediction T. M. Murali, same lecture as the previous class
Mar 1, 2007 Class cancelled
Mar 13, 2007 Midterm project reviews
Mar 15, 2007 Gene function prediction T. M. Murali, no slides
Molecular Interaction Networks
Mar 20, 2007 Cross-species analysis of gene expression Henry Monti and Zhou Song
Mar 22, 2007 The structure of biological networks William Joe Allen and Arif Khokar
Mar 27, 2007 The structure of transcriptional regulatory networks Arjun Krishnan and Ying Jin
Mar 29, 2007 Comparative analysis of molecular interaction networks Mike Avery and David Beck
Apr 3, 2007 The structure of biological networks
Comparative analysis of PPI networks
Bryan Lewis
Chris Lasher
Apr 5, 2007 The structure of biological networks
Data integration to predict molecular interactions
Mahima Gopalakrishnan
David Badger
Apr 10, 2007 Data integration to predict molecular interactions Srinivasa Santhanam
Apr 12, 2007 New paradigms for top-down modeling from systems biology data Invited lecture by Brett Tyler
Data Integration
Apr 17, 2007 Integrated analysis of molecular interactions and gene expression profiles using ActiveNetworks T. M. Murali
Apr 19, 2007 Cross-condition analysis of cellular networks using NetworkLego T. M. Murali
Apr 24, 2007 Redescription Mining and Storytelling Invited lecture by Naren Ramakrishnan
Project Presentations
Apr 26, 2007
May 1, 2007 Final project reports due

About the Course

What is Computational Systems Biology?

Cells, tissues, organs and organisms are systems of components whose interactions have been defined, refined, and optimised over hundreds of millions of years of evolution. Computational systems biology is a field that aims at a system-level understanding of biological systems by analysing biological data using computational techniques. Systems biology aims to answer the following key questions by integrating experimental and computational approaches:

  1. What are the basic structures and properties of the biological networks in a living cell?
  2. How does a biological system behave over time under various conditions?
  3. How does a biological system maintain its robustness and stability?
  4. How can we modify or construct biological systems to achieve desired properties?
Answers to these questions require breakthroughs in the fields of biology, chemistry, computer science, engineering, mathematics and other fields. The explosive progress of genome sequencing projects and the massive amounts of data that high-throughput experiments in DNA microarrays, proteomics, and metabolomics yield drive advances in this field. Sophisticated computational ideas process these data sources in an effort to systematically analyse and unravel the complex biological phenomena that take place in a cell.

Who should take this course?

You should take this course if you are curious to find out how the latest research is shaping our understanding of how the living cell behaves as a system. The course will cover the latest research in computational systems biology, primarily in the context of biological networks (regulatory, protein-protein interactions, metabolic, and signalling). We will spend a significant part of the course on examining how the analysis of DNA microarray data and other high-throughput data is crucial to progress in this area. The course is geared towards graduate students whose main research interest is bioinformatics or who use bioinformatic tools and techniques in their research.

There are many exciting and profound issues that researchers in this area are actively investigating, such as the robustness of biological systems, network structures and dynamics, and applications to drug discovery. During this course, we will come across many interesting open research problems. Taking this course might be an excellent way to create research topics and projects for your Master's or Ph.D. thesis in the area of bioinformatics/computational biology. In this course, you will be able to communicate and work with students and researchers with varied backgrounds. In addition, Virginia Tech is humming with research activities in this area.

Pre-requisites

The course is open to students with graduate standing. I hope that both students with computational backgrounds and students with experience in the life sciences will take this course. If you find this course interesting but are not sure whether your background matches the pre-requisites, please talk to me! My contact information is listed on my home page.

Computer Science graduate students: the Data and Algorithm Analysis (CS 4104) or similar course is a pre-requisite. It will help if you also have taken Algorithms in Bioinformatics (CS 5124) and a course on combinatorics and graph theory such as Applied Combinatorics (MATH 3134). An introductory molecular biology course such as Paradigms for Bioinformatics will provide extremely useful biological background.

Life science graduate students: I expect that you have taken courses in biochemistry, cell biology, and molecular biology. A course like Computation for Life Sciences (CS 5045) provides very useful computational background.

Course structure

The course will primarily be driven by lectures and by seminars where groups of students present a related group of papers from literature. I will try to arrange papers that cover both biological and computational aspects. Ideally, I would like a group to contain students with backgrounds in computer science, mathematics, and/or statistics and students with backgrounds in biology and chemistry.

Your grade will depend on your presentation (20%), on class participation (30%), and a final project (50%). The final project is a group software project. I will define software projects that are inspired by the papers you present in class. The project will involve creating some new software or using existing software innovatively combined with some intensive biological analysis of the results. You are welcome to suggest a project to me.

Papers to be covered

Below, I have listed a superset of the papers that we will discuss. The actual set of papers we will cover will depend on the interests of the students. The general articles and surveys provide an overview of the field of systems biology, If you can, please read these papers before the first class so that you can be familiar with this area.

Introduction to Computational Systems Biology

These articles provide very good introductions to the subject of (computational) systems biology.

Gene Expression Analysis

Functional Annotation

Comparative Systems Biology

Structure of Molecular Interaction Networks

Transcriptional Regulatory Networks

Data Integration

Protein-Protein Interaction (PPI) Networks

Metabolic Networks

Designer Networks

Useful Links

Research Groups

Databases

Software

Related Courses

Journals, Conferences, and Workshops

Last modified: Tue Apr 3 10:31:03 EDT 2007