Predictive-Switching Control of Stochastic Gene Regulatory Networks: A Contractive PIDE Framework

TL;DR

This paper introduces a control algorithm for stochastic gene networks modeled by PIDEs, using neural networks and contraction analysis to ensure stability and convergence of probability densities.

Contribution

It presents a novel predictive switching control framework with a contraction-based stability analysis for high-dimensional stochastic gene regulatory networks.

Findings

Establishes $L^1$-contractivity and stability guarantees.

Demonstrates exponential convergence under positive leakage.

Validates approach through numerical simulations on complex examples.

Abstract

This paper develops a predictive switching control algorithm for stochastic gene regulatory networks described by a Partial Integro-Differential Equation (PIDE) model, which enables direct shape control of the probability density function. Control inputs are selected from a finite candidate set to minimize a prescribed cost functional. A hybrid framework is proposed for scalability in higher-dimensional systems, using neural networks to approximate the control policy. A central theoretical contribution is a contraction-based analysis of the closed-loop PIDE dynamics. The paper establishes $L^ 1$-contractivity under the proposed control scheme, yielding formal stability guarantees and showing that the evolution of the probability density becomes progressively independent of the initial condition. Moreover, under strictly positive leakage terms, exponential convergence is obtained. The effectiveness and flexibility of the approach, together with the theoretical contractivity results, are illustrated through numerical simulations on three representative examples of increasing dimensionality.