Predictive modeling of genome-wide mRNA expression: from modules to molecules.
Various algorithms are available for predicting mRNA expression and modeling gene regulatory processes. They differ in whether they rely on the existence of modules of coregulated genes or build a model that applies to all genes, whether they represent regulatory activities as hidden variables or as mRNA levels, and whether they implicitly or explicitly model the complex cis-regulatory logic of multiple interacting transcription factors binding the same DNA. The fact that functional genomics data of different types reflect the same molecular processes provides a natural strategy for integrative computational analysis. One promising avenue toward an accurate and comprehensive model of gene regulation combines biophysical modeling of the interactions among proteins, DNA, and RNA with the use of large-scale functional genomics data to estimate regulatory network connectivity and activity parameters. As the ability of these models to represent complex cis-regulatory logic increases, the need for approaches based on cross-species conservation may diminish.
Bussemaker
Harmen J
HJ
Department of Biological Sciences, Columbia University, New York, New York 10027, USA. hjb2004@columbia.edu
Foat
Barrett C
BC
Ward
Lucas D
LD
D000465:Algorithms; D000818:Animals; D001483:Base Sequence; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D016678:Genome; D006801:Humans; D046911:Macromolecular Substances; D008962:Models, Theoretical; D008968:Molecular Conformation; D008969:Molecular Sequence Data; D012323:RNA Processing, Post-Transcriptional; D012333:RNA, Messenger / Q000378:metabolism*; D014157:Transcription Factors / Q000378:metabolism; D014158:Transcription, Genetic
D046911:Macromolecular Substances; D012333:RNA, Messenger; D014157:Transcription Factors