Reconfiguring Gene Regulatory Neural Network Computing for Regulating Biofilm Formation

The Gene Regulatory Network (GRN) in biological cells orchestrates essential functions for adaptation and survival in diverse environments, drawing on structural similarities with the Artificial Neural Network (ANN), which can be transformed into a Gene Regulatory Neural Network (GRNN). This transformation enables exploration of their natural computing capabilities regarding network reconfigurability and controllability, facilitating dynamic adjustments of gene-gene interaction weights to regulate biological processes. In this paper, we present a control-theoretic model for the GRNN that determines optimal chemical input concentrations, steering the GRNN towards desired weight configurations using the Linear Quadratic Regulator (LQR) approach. This method enhances network robustness by balancing stability and reconfigurability, ensuring responsive weight adjustments in dynamic environments. We develop mathematical models to identify critical genes using a Continuous-Time Markov Chain (CTMC) and derive temporal weight configurations, providing insights into the system's reconfiguration dynamics, while also quantifying stability and reconfigurability. Our findings demonstrate the effectiveness of the control model in mitigating Clostridioides difficile biofilm formation, outperforming sub-optimal and stochastic perturbation inputs, and highlighting the importance of determining optimal inputs for robust network behavior across diverse complexities.