title: A general modeling strategy for gene regulatory networks with stochastic dynamics.
abstract: A stochastic genetic toggle switch model that consists of two identical, mutually repressive genes is built using the Gillespie algorithm with time delays as an example of a simple stochastic gene regulatory network. The stochastic kinetics of this model is investigated, and it is found that the delays for the protein productions can highly weaken the global fluctuations for the expressions of the two genes, making the two mutually repressive genes coexist for a long time. Starting from this model, we propose a practical modeling strategy for more complex gene regulatory networks. Unlike previous applications of the Gillespie algorithm to simulate specific genetic networks dynamics, this modeling strategy is proposed for an ensemble approach to study the dynamical properties of these networks. The model allows any combination of gene expression products, forming complex multimers, and each one of the multimers is assigned to a randomly chosen gene promoter site as an activator or inhibitor. In addition, each gene, although it has only one promoter site, can have multiple regulatory sites and distinct rates of translation and transcription. Also, different genes have different time delays for transcription and translation and all reaction constant rates are initially randomly chosen from a range of values. Therefore, the general strategy here proposed may be used to simulate real genetic networks.
authors.affiliation: Institute for Biocomplexity and Informatics, Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada. ARibeiro@ucalgary.ca
authors.forename: Andre
authors.initials: A
authors.lastname: Ribeiro
authors.forename: Rui
authors.initials: R
authors.lastname: Zhu
authors.forename: Stuart A
authors.initials: SA
authors.lastname: Kauffman
mesh_terms: D000465:Algorithms; D001699:Biometry; D005786:Gene Expression Regulation; D015894:Genome, Human; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D011401:Promoter Regions, Genetic; D013269:Stochastic Processes