Stochastic models and numerical algorithms for a class of regulatory gene networks.
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.
Fournier
Thomas
T
Department of Mathematics, University of Fribourg, Chemin du Musée 23, 1700 Fribourg, Switzerland.
Gabriel
Jean-Pierre
JP
Pasquier
Jerôme
J
Mazza
Christian
C
Galbete
José
J
Mermod
Nicolas
N
D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D004318:Doxycycline / Q000378:metabolism; D025461:Feedback, Physiological / Q000235:genetics; D005786:Gene Expression Regulation / Q000502:physiology; D053263:Gene Regulatory Networks / Q000502:physiology*; D015316:Genetic Therapy; D049452:Green Fluorescent Proteins / Q000235:genetics; D006801:Humans; D007700:Kinetics; D016014:Linear Models; D008390:Markov Chains; D008957:Models, Genetic; D055503:Protein Multimerization / Q000502:physiology; D012097:Repressor Proteins / Q000378:metabolism; D013269:Stochastic Processes; D015534:Trans-Activators / Q000378:metabolism; D019076:Transgenes / Q000235:genetics
D012097:Repressor Proteins; D015534:Trans-Activators; C485184:enhanced green fluorescent protein; C034811:tetracycline resistance-encoding transposon repressor protein; D049452:Green Fluorescent Proteins; D004318:Doxycycline