Modeling the Combinatorial Functions of Multiple Transcription Factors

Abstract

A considerable fraction of gene promoters are bound by multiple transcription factors. It is therefore important to understand how such factors interact in regulating the genes. In this paper, we propose a computational method to identify groups of co-regulated genes and the corresponding regulatory programs of multiple transcription factors from protein- DNA binding and gene expression data. The key concept is to characterize a regulatory program in terms of two properties of individual transcription factors: the function of a regulator as an activator or a repressor, and its direction of effectiveness as necessary or sufficient. We apply a greedy algorithm to find the regulatory models which best explain the available data. Empirical analysis indicates that the inferred regulatory models agree with known combinatorial interactions between regulators and are robust against various parameter choices.