Optimal perturbations for the identification of stochastic reaction dynamics

Abstract

Identification of stochastic reaction dynamics inside the cell is hampered by the low-dimensional readouts available with today's measurement technologies. Moreover, such processes are poorly excited by standard experimental protocols, making identification even more ill-posed. Recent technological advances provide means to design and apply complex extra-cellular stimuli. Based on an information-theoretic setting we present novel Monte Carlo sampling techniques to determine optimal temporal excitation profiles for such stochastic processes. We give a new result for the controlled birth-death process and provide a proof of principle by considering a simple model of regulated gene expression.