A computational framework for optimal and Model Predictive Control of stochastic gene regulatory networks
Hamza Faquir, Manuel P\'ajaro, Irene Otero-Muras

TL;DR
This paper introduces a computational framework for optimal and Model Predictive Control of stochastic gene regulatory networks, enabling precise regulation of molecular noise and complex cell population behaviors with high efficiency.
Contribution
It presents a novel, efficient approximation of the Chemical Master Equation for controlling stochastic biomolecular systems, allowing fine-tuned population shaping and noise management.
Findings
Effective control of bimodal cell populations
Ability to track moving target distributions
High computational efficiency in control design
Abstract
Engineering biology requires precise control of biomolecular circuits, and Cybergenetics is the field dedicated to achieving this goal. A significant challenge in developing controllers for cellular functions is designing systems that can effectively manage molecular noise. To address this, there has been increasing effort to develop model-based controllers for stochastic biomolecular systems, where a major difficulty lies in accurately solving the chemical master equation. In this work we develop a framework for optimal and Model Predictive Control of stochastic gene regulatory networks with three key advantageous features: high computational efficiency, the capacity to control the overall probability density function enabling the fine-tuning of the cell population to obtain complex shapes and behaviors (including bimodality and other emergent properties), and the capacity to handle…
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Taxonomy
TopicsGene Regulatory Network Analysis · Advanced Control Systems Optimization
