Transcription factor proteins play an important role in altering genes' expression levels. microRNA genes constitute another important mechanism of gene (down)regulation and they have been connected to many abnormal phenotypes, including cancer. We are interested in understanding the information flow of key players during cells' responses. To that extend, we develop probabilistic models towards deduction of the general properties of the biological networks.

We are interested in understanding the general properties of cis-regulatory sites and modules and their role in the expression of the genes. In order to do so, we use comparative genomics approaches combined with statistical motif finding algorithms on transcription array and ChIP data. Many transcription factors exhibit core specificities that -if used properly- can lead to better understanding of cells' proliferation, differentiation, etc.

The differences observed between individuals as well as between certain species are due to DNA polymorphisms. Some of these polymorphisms are known to have no effect in the structure of proteins but rather alter the expression of certain "key" genes. By performing analysis of genomic data we aim to develop methods that will be able to detect such regulatory polymorphisms.

University of Pittsburgh has initiate a Center to model pulmonary immunity in mammals (http://cmpi.cs.pitt.edu/). Our laboratory participates in this initiative. Our aim is to study the DNA regulatory signals that drive immune responses to various pathogens.