CS 6104: Advanced Topics Course on Systems Biology and Drug Discovery

T. M. Murali

Fall 2004, Thursdays, 5-7:45pm, McBryde 316

One page description of the course

Schedule
Date Topic and papers Presenter(s) (links point to the presentations)
Aug 26, 2003 Introduction to Systems Biology and Drug Discovery T. M. Murali
Sep 2, 2004 No class
Sep 9, 2004 Topics and Projects in the course
Group assignments
T. M. Murali
Sep 16, 2004 Finding Diagnostic genes Using Gene Expression Data Andrew Fenley, John Gordon, and Eric Nordberg
Sep 23, 2004 Disease Classification Using Gene Expression Data Greg Grothaus and Jonathan Myers
Sep 30, 2004 Outcome Prediction and Personalised Medicine Part I Deept Kumar, Nilanjan Saha, and Maulik Shukla
Oct 7, 2004 Comparative Systems Biology Chaitanya Kommidi, Kiran Kumar Indukuri, Shenghua Li, and Rob StClair
Oct 14, 2004 Chemical Genomics and Pharmacogenomics
Project reports due.
Shivaram Narayanan and Corban Rivera
Oct 21, 2004 Whole-Genome Functional Annotation Satish Tadepalli and Srinivas Venkataraghavan
Oct 28, 2004 Mid-term Project Reviews
Nov 4, 2004 Stem cells and Stemness
Outcome Prediction and Personalised Medicine Part II
Andrew Fenley, John Gordon, and Eric Nordberg
Deept Kumar, Nilanjan Saha, and Maulik Shukla
Nov 11, 2004 Malaria
RNA Interference Part I
Invited lecture by Dr. Dharmendar Rathore
Rajat Singhania and Naga Srinivas Vemuri
Nov 18, 2004 Genome Variation and Disease
RNA Interference Part II
Invited Presentation by Dr. Liqing Zhang
Rajat Singhania and Naga Srinivas Vemuri
Dec 2, 2004 Pharmacology Invited Presentation by Dr. Peter Eyre
Dec 9, 2004 Project Presentations
Dec 16, 2004 Project Presentations

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.

Systems Biology and Drug Discovery

The sequencing of the human genome and the genomes of model eukaryotic organisms promises to have a significant impact on our understanding of human diseases. Systems Biology lays the foundation for this improved understanding by systematically modelling, probing, untangling, and comprehending the complex interplay between the different cellular pathways that cause or are affected by disease. In this course, we will study a variety of new high-throughput biotechnological methods and computational techniques to analyse the massive quantities of different types of data generated by these methods.

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 and contributing to an improved understanding of the genes and pathways that are perturbed in a disease state. The course will cover the latest research in computational systems biology in the context of drug discovery, primarily in the context of disease classification, prediction of treatment outcome, chemical genomics and pharmacogenomics, and individualised medicine. 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.

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.

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. Although some papers belong to multiple categories, I have listed them only under one category. These papers may move categories by the time someone presents them.

Introduction to Systems Biology and Drug Discovery

The Fall 2003 course on Computational Systems Biology has pointers to many papers that provide a very good introduction to the subject of (computational) systems biology. The general articles and surveys listed below provide an overview of the field of systems biology, in the context of drug discovery. Read these papers before the second or third class so that you can be familiar with this area.

Finding Diagnostic Genes Using Gene Expression Data

The Fall 2003 course on Computational Systems Biology has pointers to many papers that provide a good introduction to clustering algorithms for gene expression data.

Stem Cells and Stemness

Disease Classification Using Gene Expression Data

The Fall 2003 course on Computational Systems Biology has pointers to many papers that provide a good introduction to clustering algorithms for gene expression data.

Outcome Prediction and Personalised Medicine

Comparative Systems Biology

Chemical Genomics and Pharmacogenomics

Whole-Genome Functional Annotation

RNA Interference