Stochasticity in regulatory networks

From Ilya Nemenman: Theoretical Biophysics @ Emory
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Biochemical networks, and more specifically transcriptional regulatory networks, are known to exhibit stochasticity, and the stochasticity has been implicated in a variety of phenomena, from switching between multistable states, to limiting fidelity of regulatory responses. Most work on stochasticity has focused on bacteria (E. coli), however, some papers on stochastic effects in yeast, dictistilium, etc. have appeared as well

Relevant References

  1. S Paliwal, P Iglesias, K Campbell, Z Hilioti, A Groisman, and A Levchenko. MAPK-mediated bimodal gene expression and adaptive gradient sensing in yeast. Nature 446, 46, 2007. PDF.
    The mating pathway in Saccharomyces cerevisiae has been the focus of considerable research effort, yet many quantitative aspects of its regulation still remain unknown. Using an integrated approach involving experiments in microfluidic chips and computational modelling, we studied gene expression and phenotypic changes associated with the mating response under well-defined pheromone gradients. Here we report a combination of switch-like and graded pathway responses leading to stochastic phenotype determination in a specific range of pheromone concentrations. Furthermore, we show that these responses are critically dependent on mitogen-activated protein kinase (MAPK)-mediated regulation of the activity of the pheromone-response-specific transcription factor, Ste12, as well as on the autoregulatory feedback of Ste12. In particular, both the switch-like characteristics and sensitivity of gene expression in shmooing cells to pheromone concentration were significantly diminished in cells lacking Kss1, one of the MAP kinases activated in the mating pathway. In addition, the dynamic range of gradient sensing of Kss1-deficient cells was reduced compared with wild type. We thus provide unsuspected functional significance for this kinase in regulation of the mating response.
    A nice platform for microfluidic studies is introduced, yeasts in multiple phenotypes are studied on the platform. Stochasticity in yeast response to the fluid stimulus is studied. A model is introduced for the relevant MAPK pathway. Interestingly, it's observed that gradient sensing in yeast is adaptive, so that the east responds to fractional gradients (I think it's different for E. coli). Many nice experiments can be done with this platform.