title: Analysing GCN4 translational control in yeast by stochastic chemical kinetics modelling and simulation.
abstract: BACKGROUND
  The yeast Saccharomyces cerevisiae responds to amino acid starvation by inducing the transcription factor Gcn4. This is mainly mediated via a translational control mechanism dependent upon the translation initiation eIF2·GTP·Met-tRNAiMet ternary complex, and the four short upstream open reading frames (uORFs) in its 5' mRNA leader. These uORFs act to attenuate GCN4 mRNA translation under normal conditions. During amino acid starvation, levels of ternary complex are reduced. This overcomes the GCN4 translation attenuation effect via a scanning/reinitiation control mechanism dependent upon uORF spacing.
  
  
  RESULTS
  Using published experimental data, we have developed and validated a probabilistic formulation of GCN4 translation using the Chemical Master Equation (Model 1). Model 1 explains GCN4 translation's nonlinear dependency upon uORF placements, and predicts that an as yet unidentified factor, which was proposed to regulate GCN4 translation under some conditions, only has pronounced effects upon GCN4 translation when intercistronic distances are unnaturally short. A simpler Model 2 that does not include this unidentified factor could well represent the regulation of a natural GCN4 mRNA. Using parameter values optimised for this algebraic Model 2, we performed stochastic simulations by Gillespie algorithm to investigate the distribution of ribosomes in different sections of GCN4 mRNA under distinct conditions. Our simulations demonstrated that ribosomal loading in the 5'-untranslated region is mainly determined by the ratio between the rates of 5'-initiation and ribosome scanning, but was not significantly affected by rate of ternary complex binding. Importantly, the translation rate for codons starved of cognate tRNAs is predicted to be the most significant contributor to the changes in ribosomal loading in the coding region under repressing and derepressing conditions.
  
  
  CONCLUSIONS
  Our integrated probabilistic Models 1 and 2 explained GCN4 translation and helped to elucidate the role of a yet unidentified factor. The ensuing stochastic simulations evaluated different factors that may impact on the translation of GCN4 mRNA, and integrated translation status with ribosomal density.
authors.affiliation: School of Natural and Computing Sciences, University of Aberdeen, Institute of Complex System and Mathematical Biology, Aberdeen, UK.
authors.forename: Tao
authors.initials: T
authors.lastname: You
authors.forename: Ian
authors.initials: I
authors.lastname: Stansfield
authors.forename: M Carmen
authors.initials: MC
authors.lastname: Romano
authors.forename: Alistair J P
authors.initials: AJ
authors.lastname: Brown
authors.forename: George M
authors.initials: GM
authors.lastname: Coghill
mesh_terms: D000596:Amino Acids / Q000172:deficiency*; D050976:Basic-Leucine Zipper Transcription Factors / Q000378:metabolism*; D003198:Computer Simulation; D015966:Gene Expression Regulation, Fungal / Q000502:physiology*; D007700:Kinetics; D008954:Models, Biological; D014176:Protein Biosynthesis / Q000502:physiology; D012441:Saccharomyces cerevisiae / Q000502:physiology*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*; D013269:Stochastic Processes
chemical_list: D000596:Amino Acids; D050976:Basic-Leucine Zipper Transcription Factors; C501391:GCN4 protein, S cerevisiae; D029701:Saccharomyces cerevisiae Proteins