An Interaction-Dependent Model for Transcription Factor Binding

TitleAn Interaction-Dependent Model for Transcription Factor Binding
Publication TypeBook Chapters
Year of Publication2006
AuthorsWang L-S, Jensen S, Hannenhalli S
EditorEskin E, Ideker T, Raphael B, Workman C
Book TitleSystems Biology and Regulatory GenomicsSystems Biology and Regulatory Genomics
Series TitleLecture Notes in Computer Science
Pagination225 - 234
PublisherSpringer Berlin / Heidelberg
ISBN Number978-3-540-48293-2

Transcriptional regulation is accomplished by several transcription factor proteins that bind to specific DNA elements in the relative vicinity of the gene, and interact with each other and with Polymerase enzyme. Thus the determination of transcription factor-DNA binding is an important step toward understanding transcriptional regulation. An effective way to experimentally determine the genomic regions bound by a transcription factor is by a ChIP-on-chip assay. Then, given the putative genomic regions, computational motif finding algorithms are applied to estimate the DNA binding motif or positional weight matrix for the TF. The a priori expectation is that the presence or absence of the estimated motif in a promoter should be a good indicator of the binding of the TF to that promoter. This association between the presence of the transcription factor motif and its binding is however weak in a majority of cases where the whole genome ChIP experiments have been performed. One possible reason for this is that the DNA binding of a particular transcription factor depends not only on its own motif, but also on synergistic or antagonistic action of neighboring motifs for other transcription factors. We believe that modeling this interaction-dependent binding with linear regression can better explain the observed binding data. We assess this hypothesis based on the whole genome ChIP-on-chip data for Yeast. The derived interactions are largely consistent with previous results that combine ChIP-on-chip data with expression data. We additionally apply our method to determine interacting partners for CREB and validate our findings based on published experimental results.