A Computational Framework for Optimal and Model Predictive Control of Stochastic Gene Regulatory Networks

This framework tackles the hard problem of intrinsic molecular noise in cybergenetics by building a powerful computational control system for stochastic gene‑regulatory networks. Conventional model‑based controllers for biomolecular circuits often stall because solving the Chemical Master Equation (CME) is very computationally demanding. To get around this, the method uses a fast approximation of the CME that is based on Partial Integro‑Differential Equations (PIDEs). This makes it possible to run an efficient adjoint‑based optimization. The framework can work with both optimal‑control and Model Predictive Control (MPC) approaches. It lets you shape the probability density functions of whole cell populations with high precision. With it you can robustly regulate emergent cellular traits,

such as creating and tuning bimodal distributions or tracking moving target distributions in inducible gene‑regulatory circuits. Aimed at M.Tech‑level research, the project gives step‑by‑step guidance for: – Simulating stochastic biomolecular dynamics – Building the PIDE‑based CME approximation – Testing controller performance under different noise levels The resulting simulation environment provides a solid platform for studying cybergenetic control strategies in synthetic‑biology applications.

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