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

Lakshmeesh Maruthi 1 Ilya Tkachev 1 Alfonso Carta 2 Eugenio Cinquemani 3 Pascal Hersen 4 Gregory Batt 5 Alessandro Abate 6, 1
2 BIOCORE - Biological control of artificial ecosystems
INRA - Institut National de la Recherche Agronomique, CRISAM - Inria Sophia Antipolis - Méditerranée , LOV - Laboratoire d'océanographie de Villefranche
3 IBIS - Modeling, simulation, measurement, and control of bacterial regulatory networks
LAPM - Laboratoire Adaptation et pathogénie des micro-organismes [Grenoble], Inria Grenoble - Rhône-Alpes, Institut Jean Roget
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 control framework, based on Model Predictive Control, which 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. The present new contribution leads to envision the application of the proposed framework to wetlab experiments on yeast.
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Submitted on : Thursday, December 18, 2014 - 3:11:20 PM
Last modification on : Friday, November 19, 2021 - 10:02:03 AM

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Lakshmeesh Maruthi, Ilya Tkachev, Alfonso Carta, Eugenio Cinquemani, Pascal Hersen, et al.. Towards Real-Time Control of Gene Expression at the Single Cell Level: A Stochastic Control Approach. Computational Methods in Systems Biology, 8859, Springer International Publishing, pp.155-172, 2014, Lecture Notes in Computer Science, 978-3-319-12981-5. ⟨10.1007/978-3-319-12982-2_12⟩. ⟨hal-01096959⟩



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