Introduction

Participants

Papers

Links

Credits

REPORTS

First Year Report: Executive Summary

Full Report

NEWS:

(1) "Genome Wide Analysis of the Endothelial Transcriptome Under Short-Term Chronic Hypoxia". [abstract]

(2) "Identifying genes altered by a drug in temporal microarray data: A case study." Winnder, American Statistical Association 2003 Biopharm Student Paper Competition Award. [pdf]

(3) Influence of age, sex, and strength training on human muscle gene expression determined by microarray [abstract]

(4) Experiments on Accuracy of Algorithms for Inferring Structure of Regulatory Networks [pdf]

(5) Learning from SAGE Data [pdf]

(6) Analysis of Microarray Data for Treated Fat Cells [pdf]

Computational Systems Biology Group

Purpose:

To design, implement, and evaluate methods for modeling and discovering causal relationships among genes, proteins, and other factors, based on backgrousnd biological knowledge and available cellular data.

Introduction:

The Computational Systems Biology Group is an association of statisticians, computer scientists and biologists at Carnegie Mellon University, the University of Pittsburgh and the University of West Florida Institute for Human and Machine Cognition. Funded by the National Aeronautics and Space Administration's NASA Ames Research Center, we investigate statistical, algorithmic, experimental design and biological issues surrounding the interpretation of expression data, especially with SAGE and microarray techniques.

Our work includes:

  • Sage measurements of gene expression in cells lining blood vessels, under variations of shear flow and hydrostatic pressure
  • Microarray measurements of gene expression in cells lining blood vessels, under variations of shear flow and hydrostatic pressure
  • Microarray measurements of gene expression in shocked mouse lipid cells
  • Implementation of most of the algorithms proposed for extracting gene regulation networks from expression data
  • Implementation of a program for simulating microarray measurements from known regulatory systems
  • Development of new algorithms
  • Testing of proposed algorithms on simulated and real data
  • Studies of the statistical foundations of SAGE estimates of gene expression
  • Studies of the statistical foundations of microarrary estimates of gene expression

jdramsey@andrew.cmu.edu