Grey-box techniques for the identification of a controlled gene expression model

The aim of this paper is to propose a computationally efficient technique for the identification of stochastic biochemical networks, involving only zero and first order reactions, from distribution measurements of the cell population. The moments of the species in such networks evolve according to an affine system, hence the use of grey-box identification methods is suggested. The performance of existing methods and of a new method, based on the transfer function computation, is compared using as benchmark a standard gene expression model. The developed discussion is of interest for the general grey-box identification problem.