M. Mahmoud (USA)
Gene Regulatory Network, Biomedical Imaging, Stochastic
Upon the completion of the sequencing of the human genome, an urgent need emerged to develop tools capable of unraveling the interaction and functionality of genes. With the aid of microarray technology, large scale gene expres sion analyses have been conducted and several gene regu latory network models have been proposed. To establish a framework that better integrates and regulates such models, the stochastic stability properties of these models have to be defined and investigated. Such a study would uncover regimes of stable behavior under both intrinsic and extrinsic noise as well as random changes in model parameters due to abruptly changing environment conditions. In this article, stochastic stability for a class of Hybrid Gene Regulatory Network (HGRN) Models is defined. A particular interest is to study and derive conditions that guarantee asymptotic exponential stochastic stability when several random switch ing processes are effecting the behavior of the GRN model. A numerical example is included to demonstrate the theoret ical analysis.
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