Computational Systems Biology


Professor B. Mishra

Office Hours: Tue 2:00 PM - 2:45 PM
Office Phone: 212.998.3464
Email Address:

Day and Time:
Tuesday, 7:10 PM - 9:00 PM EST, Room 1221, 719 Broadway.

Credits for Course:

Syllabus for Course: Syllabus: Biology X


Few Ideas for the class projects:
(1) GWAS -- WTCCC Study: See the URL:
(2) Mendelian Diseases: See the URL:
(3) Indian Population: See the URL:
(4) Mutation Rates in Humans: See URL:
(5) Quartet Analysis: See URL:

[ Lecture 1 || [ Lecture 2 || [ Lecture 3 || [ Lecture 4 || [ Lecture 5 || [ Lecture 6 || [ Lecture 7 || [ Lecture 8 || [ Lecture 9 || [ Lecture 10 ]
Course Prerequisites: Basic Algorithms and High-Level Languages.
We are expecting students with diverse backgrounds (CS, Math, Biology, Biomedicine, Engineering, etc.), and hence will try to make the course as self-contained as possible...

The course focuses on statistically determining the relations between genotypes and phenotypes. We now know that human genome contains millions of SNPs (single-nucleotide polymorphisms), and thousands more variations in the number of copies of large and small segments of the genome (CNVs: copy number variation), which may either directly cause changes in phenotype (e.g., TAS) or which tag nearby mutations containing the key differences that influence individual variation (e.g., TASPs) and susceptibility to disease.

GWA (Genome-Wide Association) studies allow one to sample large number of SNPs from many patients, thus, capturing variation uniformly across the genome. Recently, there has been an enormous interest in such studies as they have succeeded in identifying risk and protective factors for asthma, cancer, diabetes, heart disease, mental illness and other human differences. For instance, in 2005, it was learned through a small scale GWAS that age-related macular degeneration is associated with variation in the gene for complement factor H, which produces a protein that regulates inflammation. One expects the GWAS to play a significant role in drug discovery and personalized medicine, and will be important in the modern models of health-care (e.g., evidence-based medicine). For instance, it was found that the genetic variants have different responses to various anti-hepatitis C virus treatments: for genotype 1 hepatitis C, treated with Pegasys combined with ribavirin, genetic polymorphisms near the human IL28B gene are associated strongly with responses to the treatment. One expects to find and catalogue many such facts.

This course will focus on the algorithmic, statistical and genetic aspects of this problem. Thus, we will develop specialized methods for Machine Learning (supervised and unsupervised), Classification, Model Selection, Multiple Hypotheses Testing and Experiment Design (pooling and group-testing).

Text Books
Required Textbooks:
(1) Applied Statistical Genetics with R: For Population-based Association Studies (Use R); Author: Andrea S. Foulkes; Publisher/Edition (Yr. or No.): Springer; 1 edition (April 17, 2009).

Recommended textbooks:
(2) Mathematical and Statistical Methods for Genetic Analysis; Author: Kenneth Lange; Publisher/Edition (Yr. or No.): Springer; 2nd edition (June 3, 2003).

(3) Statistical Genetics of Quantitative Traits: Linkage, Maps and QTL; Authors: Rongling Wu, Changxing Ma, George Casella; Publisher/Edition (Yr. or No.): Springer; 1 edition (July 31, 2007).

(4) Essentials of Genomic and Personalized Medicine; Authors: Geoffrey S. Ginsburg and Huntington Ph.D Willard; Publisher/Edition (Yr. or No.): Academic Press; 1 edition (October 8, 2009).

(5) Genetics: Analysis of Genes and Genomes; Authors: Daniel Hartl and Elizabeth Jones; Publisher/Edition (Yr. or No.): Jones & Bartlett Publishers; 7 edition (August 1, 2008).

Midterm Date:
No Midterm.
Final Date:
Class Project.
Class Presentation.

Bud Mishra
January 1 2010