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The chemical master equation is often used to simulate the Markov process and enable computational studies of GRN.
The chemical master equation is solved by a hybrid method coupling a macroscopic, deterministic description with a mesoscopic, stochastic model.
We can model as a Markov process with discrete states, where the time evolution of the state probabilities is given by the chemical master equation (1).
In this paper, we consider models of GRN based on chemical master equations and study the problem of estimating stochastic rate constants therein.
Section 2 describes the chemical master equation model of a gene regulatory network and its approximation by a chemical Langevin equation.
We investigated a general model of a two-gene network, using the chemical master equation and a moment generating function approach.
We use an analytical and systematic method, the linear noise approximation of the chemical master equation along with the decoupling of a stoichiometric matrix.
At the mesoscopic scale, chemical processes have probability distributions that evolve according to an infinite set of linear ordinary differential equations known as the chemical master equation (CME).
We use the chemical master equation to solve the reaction kinetics, neglecting molecular aspects underlying nucleation and growth.
The exact method to model chemical reactions, when diffusion is not taken into account, is the discrete-state chemical master equation (CME, [41]).
The chemical master equation is the fundamental equation of stochastic chemical kinetics.
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