CS 6104: Advanced Topics Course on Computational Systems Biology

T. M. Murali

Fall 2003, Thursdays, 5-7:45, McBryde 307

One page description of the course (PostScript, PDF)

Date Topic and papers Presenter(s) (links point to the presentations) Scribe notes
Aug 26, 2003 Introduction to Computational Systems Biology T. M. Murali
Aug 28, 2003 Topics in the course
Group assignments
T. M. Murali
DNA Microarray Analysis
Sep 4, 2003 Basic clustering algorithms

Applications to yeast gene expression data
T. M. Murali

Symphony (Kris Ghosh, Konstantinos Krampis, Kichol Lee)
David Farrar
Alison Fraser
Sep 11, 2003 Biclustering of gene expression data Nupur and Associates (David Farrar, Jonathan Myers, Nupur Pande) James Garst
Krishnendu Ghosh
Transcriptional Regulatory Networks
Sep 18, 2003 Reconstructing transcriptional regulatory modules and networks Symphony (Kris Ghosh, Konstantinos Krampis, Kichol Lee) Kiran Indukuri
Robert St. Clair
Sep 25, 2003 The structure of transcriptional regulatory networks Group One (Alison Fraser, Kiran Indukuri, Ranjit Ranjit, Rob StClair) T. M. Murali
T. M. Murali
Oct 2, 2003 Developmental regulatory networks Group One (Alison Fraser, Kiran Indukuri, Ranjit Randhawa, Rob StClair) Karsten Klage
Konstantinos Krampis
Protein-Protein Interaction Networks
Oct 9, 2003 Generating protein-protein interaction data Fab Four (Deept Kumar, Jamie Garst, Graham Jack, Maulik Shukla, Vibha Singhal) T. M. Murali
T. M. Murali
Oct 16, 2003 Reliability and structure of protein-protein interaction data T. M. Murali Deept Kumar
Kichol Lee
Oct 23, 2003 Functional annotation of proteins Nupur and Associates (David Farrar, Jonathan Myers, Nupur Pande) Harsha Rajasimha
Ranjit Randhawa
Metabolic Networks
Oct 30, 2003 Structural properties of metabolic networks The Four Musketeers (Karsten Klage, Harsha Rajasimha, Allan Sioson, Shivaram Narayanan) Graham Jack
Nupur Pande
Nov 6, 2003 Reconstruction of metabolic networks Fab Four (Deept Kumar, Jamie Garst, Graham Jack, Maulik Shukla) Jonathan Myers
T. M. Murali
Nov 13, 2003 Modelling metabolic networks The Four Musketeers (Karsten Klage, Harsha Rajasimha, Allan Sioson, Shivaram Narayanan) Maulik Shukla
T. M. Murali
Nov 20, 2003 Designer networks Jian He and Raghavendra Nyamagoudar Allan Sioson
T. M. Murali
Dec 4, 2003 Modelling metabolic networks Invited presentations T. M. Murali
T. M. Murali
Tuesday, Dec 9, 2003 Wrap-up T. M. Murali

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 (for instance, the Virginia Bioinformatics Institute and Prof. John Tyson in the Department of Biology).


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%). You have two options for the final project:

  1. a group software project: I will try to 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.
  2. a term paper: You can write an individual term paper or write one as a group. In either case, the paper should survey a superset of the papers you present in class.

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

Transcriptional Regulatory Networks

Protein-Protein Interaction (PPI) Networks

Metabolic Networks

Designer Networks

Useful Links

Research Groups



Related Courses

Journals, Conferences, and Workshops

Data Sets For Course Projects

Last modified: Tue Jun 29 14:07:05 EDT 2004