Analysis of Combinatorial Gene Regulation with Thermodynamic Models

Abstract

Transcriptional control is a key regulatory mechanism for cells to direct their destinies. A large number of transcription factors (TFs) could simultaneously bind to a regulatory sequence. With the constellation of TFs bound, the expression level of a target gene is usually determined by the combinatorial control of a number of TFs. The interactions among regulatory proteins and their regulatory sequences collectively form a regulatory network. A major challenge in the study of gene regulation is to identify the interaction relationships within a regulatory network and further to reconstruct gene regulatory networks.In this thesis, we developed an analytical method, Interaction-Identifier, to identify a thermodynamic model that best describes the form of TF-TF interaction among a set of TFs for every target gene. Applying this approach to time-course microarray data in mouse embryonic stem cells, we have inferred five interaction patterns among three regulators: Oct4, Sox2 and Nanog on ten target genes. We further proposed a computational framework, Network-Identifier, ii