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Updated: Jun 23, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
Published on: December 7, 2021
Stochastic models and numerical algorithms for a class of regulatory gene networks
Thomas Fournier1, Jean-Pierre Gabriel, Jerôme Pasquier
1Department of Mathematics, University of Fribourg, Chemin du Musée 23, 1700 Fribourg, Switzerland.
Abstract:
Regulatory gene networks contain generic modules, like those involving feedback loops, which are essential for the regulation of many biological functions (Guido et al. in Nature 439:856-860, 2006). We consider a class of self-regulated genes which are the building blocks of many regulatory gene networks, and study the steady-state distribution of the associated Gillespie algorithm by providing efficient numerical algorithms. We also study a regulatory gene network of interest in gene therapy, using mean-field models with time delays. Convergence of the related time-nonhomogeneous Markov chain is established for a class of linear catalytic networks with feedback loops.
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