title: Integrative analysis of time course microarray data and DNA sequence data via log-linear models for identifying dynamic transcriptional regulatory networks.
abstract: Since eukaryotic transcription is regulated by sets of Transcription Factors (TFs) having various transcriptional time delays, identification of temporal combinations of activated TFs is important to reconstruct Transcriptional Regulatory Networks (TRNs). Our methods combine time course microarray data, information on physical binding between the TFs and their targets and the regulatory sequences of genes using a log-linear model to reconstruct dynamic functional TRNs of the yeast cell cycle and human apoptosis. In conclusion, our results suggest that the proposed dynamic motif search method is more effective in reconstructing TRNs than the static motif search method.
authors: Bio & Health Group, Future IT R&D Lab., LG Electronics Inc., 221 Yangjae-Dong, Seocho-Gu, Seoul 137-130, Korea. hyungseok.choi@lge.com
authors: Hyung-Seok
authors: HS
authors: Choi
authors: Youngchul
authors: Y
authors: Kim
authors: Kwang-Hyun
authors: KH
authors: Cho
authors: Taesung
authors: T
authors: Park
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D002453:Cell Cycle; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D016014:Linear Models; D020411:Oligonucleotide Array Sequence Analysis; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism
chemical_list: D014157:Transcription Factors