Towards Real-Time Control of Gene Expression at the Single Cell Level: A Stochastic Control Approach

Lakshmeesh R.M. Maruthi1, Ilya Tkachev1, Alfonso Carta2, Eugenio Cinquemani3, Pascal Hersen4, Gregory Batt5, and Alessandro Abate6,1

1 - Delft Center for Systems and Control, TU Delft, NL
2 - INRIA Sophia-Antipolis - Méditerrané, France
3 - INRIA Grenoble Rhône-Alpes, France
4 - Laboratoire Matière et Systèmes Complexes, UMR 7057, Paris, France
5 - INRIA Paris-Rocquencourt, France
6 - Department of Computer Science, University of Oxford, UK

Abstract

Recent works have demonstrated the experimental feasibility of real-time gene expression control based on deterministic controllers. By taking control of the level of intracellular proteins, one can probe single-cell dynamics with unprecedented flexibility. However, single-cell dynamics are stochastic in nature, and a control framework explicitly accounting for this variability is presently lacking. Here we devise a stochastic Model Predictive Control framework that fills this gap. Based on a stochastic modelling of the gene response dynamics, our approach combines a full state-feedback receding-horizon controller with a real-time estimation method that compensates for unobserved state variables. Using previously developed models of osmostress-inducible gene expression in yeast, we show in silico that our stochastic control approach outperforms deterministic control design in the regulation of single cells. Through this new contribution the application of the proposed framework to wetlab experiments on yeast is envisioned.

Back to accepted papers