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Issue No.04 - July-Aug. (2012 vol.9)
pp: 973-979
E. Gelenbe , Dept. of Electr. & Electron. Eng., Imperial Coll. London, London, UK
ABSTRACT
Gene expression models play a key role to understand the mechanisms of gene regulation whose aspects are grade and switch-like responses. Though many stochastic approaches attempt to explain the gene expression mechanisms, the Gillespie algorithm which is commonly used to simulate the stochastic models requires additional gene cascade to explain the switch-like behaviors of gene responses. In this study, we propose a stochastic gene expression model describing the switch-like behaviors of a gene by employing Hill functions to the conventional Gillespie algorithm. We assume eight processes of gene expression and their biologically appropriate reaction rates are estimated based on published literatures. We observed that the state of the system of the toggled switch model is rarely changed since the Hill function prevents the activation of involved proteins when their concentrations stay below a criterion. In ScbA-ScbR system, which can control the antibiotic metabolite production of microorganisms, our modified Gillespie algorithm successfully describes the switch-like behaviors of gene responses and oscillatory expressions which are consistent with the published experimental study.
INDEX TERMS
stochastic processes, biochemistry, cellular biophysics, genetics, microorganisms, molecular biophysics, proteins, oscillatory expressions, stochastic gene expression modeling, Hill function, switch-like gene response, Gillespie algorithm, biologically appropriate reaction rates, toggled switch model, proteins, ScbA-ScbR system, antibiotic metabolite production, microorganisms, Proteins, Mathematical model, Switches, Stochastic processes, Gene expression, Equations, Degradation, Gillespie algorithm., Stochastic gene expression modeling, gene regulatory networks, switch-like gene responses
CITATION
E. Gelenbe, "Stochastic Gene Expression Modeling with Hill Function for Switch-Like Gene Responses", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 4, pp. 973-979, July-Aug. 2012, doi:10.1109/TCBB.2011.153
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