Title: Control of Gene Regulatory Networks with Noisy Measurements and Uncertain Inputs,
Abstract: This paper is concerned with the problem of stochastic control of gene
regulatory networks (GRNs) observed indirectly through noisy measurements and
with uncertainty in the intervention inputs. The partial observability of the
gene states and uncertainty in the intervention process are accounted for by
modeling GRNs using the partially-observed Boolean dynamical system (POBDS)
signal model with noisy gene expression measurements. Obtaining the optimal
infinite-horizon control strategy for this problem is not attainable in
general, and we apply reinforcement learning and Gaussian process techniques to
find a near-optimal solution. The POBDS is first transformed to a
directly-observed Markov Decision Process in a continuous belief space, and the
Gaussian process is used for modeling the cost function over the belief and
intervention spaces. Reinforcement learning then is used to learn the cost
function from the available gene expression data. In addition, we employ
sparsification, which enables the control of large partially-observed GRNs. The
performance of the resulting algorithm is studied through a comprehensive set
of numerical experiments using synthetic gene expression data generated from a
melanoma gene regulatory network.