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Section: New Results

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

Participants : Grégory Batt, Pascal Hersen.

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. In [21] , we devised 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 combined a full state-feedback receding-horizon controller with a real-time estimation method that compensated for unobserved state variables. Using previously developed models of osmostress-inducible gene expression in yeast, we showed in silico that our stochastic control approach outperformed deterministic control design in the regulation of single cells. This contribution lead to envision the application of the proposed framework to wet lab experiments in yeast.

This work was done in collaboration with Alfonso Carta (EPI BIOCORE), Eugenio Cinquemani (EPI IBIS), Lakshmeesh Maruthi and Ilya Tkachev (TU Delft), and Alessandro Abate (Oxford U).