title: Stochastic analysis of the GAL genetic switch in Saccharomyces cerevisiae: modeling and experiments reveal hierarchy in glucose repression.
abstract: BACKGROUND
  Transcriptional regulation involves protein-DNA and protein-protein interactions. Protein-DNA interactions involve reactants that are present in low concentrations, leading to stochastic behavior. In addition, multiple regulatory mechanisms are typically involved in transcriptional regulation. In the GAL regulatory system of Saccharomyces cerevisiae, the inhibition of glucose is accomplished through two regulatory mechanisms: one through the transcriptional repressor Mig1p, and the other through regulating the amount of transcriptional activator Gal4p. However, the impact of stochasticity in gene expression and hierarchy in regulatory mechanisms on the phenotypic level is not clearly understood.
  
  
  RESULTS
  We address the question of quantifying the effect of stochasticity inherent in these regulatory mechanisms on the performance of various genes under the regulation of Mig1p and Gal4p using a dynamic stochastic model. The stochastic analysis reveals the importance of both the mechanisms of regulation for tight expression of genes in the GAL network. The mechanism involving Gal4p is the dominant mechanism, yielding low variability in the expression of GAL genes. The mechanism involving Mig1p is necessary to maintain the switch-like response of certain GAL genes. The number of binding sites for Mig1p and Gal4p further influences the expression of the genes, with extra binding sites lowering the variability of expression. Our experiments involving growth on various substrates show that the trends predicted in mean expression and its variability are transmitted to the phenotypic level.
  
  
  CONCLUSIONS
  The mechanisms involved in the transcriptional regulation and their variability set up a hierarchy in the phenotypic response to growth on various substrates. Structural motifs, such as the number of binding sites and the mechanism of regulation, determine the level of stochasticity and eventually, the phenotypic response.
authors.affiliation: Department of Chemical Engineering, Center for Catalytic Science and Technology, University of Delaware, Newark, DE 19716-3110, USA. vprasad@udel.edu
authors.forename: Vinay
authors.initials: V
authors.lastname: Prasad
authors.forename: K V
authors.initials: KV
authors.lastname: Venkatesh
mesh_terms: D004268:DNA-Binding Proteins / Q000235:genetics / Q000378:metabolism; D015966:Gene Expression Regulation, Fungal; D053263:Gene Regulatory Networks / Q000235:genetics*; D005800:Genes, Fungal / Q000235:genetics*; D005812:Genes, Switch / Q000235:genetics*; D005947:Glucose / Q000378:metabolism*; D008957:Models, Genetic; D009154:Mutation; D010641:Phenotype; D012097:Repressor Proteins / Q000235:genetics / Q000378:metabolism; D012441:Saccharomyces cerevisiae / Q000235:genetics* / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism; D013269:Stochastic Processes; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism; D015533:Transcriptional Activation
chemical_list: D004268:DNA-Binding Proteins; C052130:GAL4 protein, S cerevisiae; C085976:MIG1 protein, S cerevisiae; D012097:Repressor Proteins; D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors; D005947:Glucose