The GAL switch in saccharomyces cerevisiae is a prototypical eukaryotic genetic switch, and is well characterized at the biochemical and genetic levels. In this switch, galactose (an inducer) elicits a sigmoidal switch-like response for the expression of GAL genes. GAL genes are switched on by a transcriptional activator, Gal4p, which is under glucose repression. In a wild type strain, Gal4p itself is regulated by a repressor (Gal80p) and an inducer (Gal3p). It is noteworthy that both the regulators, Gal80p and Gal3p, are under the control of the GAL switch, constituting autoregulation. Since information is well documented for the GAL switch, numerous quantitative analyses have been carried out. Most of the analyses are carried out using models based on differential equations for the protein synthesis from genes under the GAL switch. However, the GAL genetic switch in a mutant yeast strain lacking Gal80p responds only to changes in Gal4p, which is in turn regulated by glucose concentration in the medium. The Gal4p synthesis is regulated by a kinase, Mig1p, whose unphosphorylated form enters the nucleus to repress the synthesis of Gal4p. The phosphorylation cycle is tightly controlled by glucose concentration in the cytoplasm. Thus, the levels of glucose determine the fraction of Mig1p available in the nucleus. In this contribution, we have modeled the mechanisms involved in the regulation of the GAL genes. We consider genes with one binding site for Gal4p (such as GAL3 and MEL1) and genes with two binding sites (such as GAL1 andGAL2). Also, Mig1p, the inhibitor controlled by glucose, has two binding sites for SUC2 and one binding site for GAL3 and GAL1. It is important to note that the GAL genes are regulated by Gal4p and also through Mig1p.

We seek to investigate the significance of two levels of control on gene expression. The other question we address is the relevance of inherent noise due to stochasticity on gene expression. In order to do this, we present results based on stochastic modeling using the stochastic simulation algorithm (SSA) [1] at the genetic scale. We combine the stochastic model with metabolic models through the chemical Langevin equation (CLE) approach, and with hybrid multiscale approaches using tau-leaping [2]. Finally, we present experimental results at the cellular level describing the variation in the phenotypic response, and characterize the effect of the stochastic variation at the genetic scale on the larger scales. This analysis is significant in that it provides a direct evaluation of the effect of intrinsic noise at different scales in saccharomyces cerevisiae, and on how it is modified by regulation.

References:

1. D.T. Gillespie, ‘Exact stochastic simulation of coupled chemical reactions', J. Phys. Chem., 81(25), 2340 (1977).

2. A. Chatterjee, K. Mayawala, J. S. Edwards, and D. G. Vlachos, ‘Time accelerated Monte Carlo simulations of biological networks using the binomial t-leap method', Bioinformatics, 21(9), 2136 (2005).

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