title: Predictive models of gene regulation: application of regression methods to microarray data.
abstract: Eukaryotic transcription is a complex process. A myriad of biochemical signals cause activators and repressors to bind specific cis-elements on the promoter DNA, which help to recruit the basal transcription machinery that ultimately initiates transcription. In this chapter, we discuss how regression techniques can be effectively used to infer the functional cis-regulatory elements and their cooperativity from microarray data. Examples from yeast cell cycle are drawn to demonstrate the power of these techniques. Periodic regulation of the cell cycle, connection with underlying energetics, and the inference of combinatorial logic are also discussed. An implementation based on regression splines is discussed in detail.
authors.affiliation: Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
authors.forename: Debopriya
authors.initials: D
authors.lastname: Das
authors.forename: Michael Q
authors.initials: MQ
authors.lastname: Zhang
mesh_terms: D002453:Cell Cycle / Q000235:genetics; D004734:Energy Metabolism; D005786:Gene Expression Regulation; D015966:Gene Expression Regulation, Fungal; D008957:Models, Genetic; D008967:Molecular Biology / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D011401:Promoter Regions, Genetic; D012044:Regression Analysis; D050436:Regulatory Elements, Transcriptional / Q000235:genetics; D012441:Saccharomyces cerevisiae / Q000166:cytology / Q000235:genetics / Q000378:metabolism / Q000502:physiology; D014158:Transcription, Genetic