Control of stochastic master equation models of genetic regulatory networks by approximating their average behavior

Stochastic master equation (SME) models can provide detailed representation of genetic regulatory system but their use is restricted by the large data requirements for parameter inference and inherent computational complexity involved in its simulation. In this paper, we approximate the expected value of the output distribution of the SME by the output of a deterministic Differential Equation (DE) model. The mapping provides a technique to simulate the average behavior of the system in a computationally inexpensive manner and enables us to use existing tools for DE models to control the system. The effectiveness of the mapping and the subsequent intervention policy design was evaluated through a biological example.

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