MIT Department of Electrical Engineering & Computer Science

Using DNA Chips to Statistically Validate Computational Models of Genetic Regulatory Networks

David Gifford*
EECS and LCS

Monday, February 28, 2000
4:00 PM (refreshments 3:30)
Edgerton Hall, Room 34-101
EECS Colloquium

Abstract

We present a simple illustration of how graphical models can be used to represent biological theories and to compare alternative hypotheses about particular mechanisms in genetic regulatory networks with statistical rigor. Existing techniques for analyzing high-density DNA array (HDA)data do not focus on the statistical testing of hypotheses about either the functioning of complex multi-variate systems or the form of complex regulatory networks. We address these problems by representing biological theories in computational form.

Theories represented computationally have the advantage that statistical metrics can be used to compare the predictive power of different theories in the presence of observed data. Once a theory about a biological system is represented computationally, the theory can be automatically tested, refined,stored, used as a component in other models, and communicated to others.

As an illustration, we examine two published hypotheses regarding the role of Gal80 protein in the yeast galactose system. Using 52 genomes worth of yeast HDA data, we show how our statistical metrics disambiguate the two hypotheses, in favor of the currently accepted one.

*Joint work with Alex Hartemink, Prof. Tommi Jaakkola, Tarjei Mikkelson, Prof. Rick Young