Individual-Based Modeling of Microbial Communities Integrating Genetic Mechanisms: A Case Study of LuxS-Mediated Quorum Sensing in Salmonella Typhimurium

We present a digital twin framework for simulating microbial communities at the single-cell level, integrating genetic mechanisms through individual-based modeling (IBM). This in silico approach enables the study of bacterial populations with bio-sensing capabilities and stochastic virulence expression, facilitating the design of biotechnological applications such as targeted drug delivery. By combining kinetic modeling with IBM, we capture the regulatory interplay between quorum sensing (QS) and virulence, allowing for predictive simulations before costly wet-lab experiments with state-of-the-art technologies like organ-on-chip models. To demonstrate the power of this approach, we focus on Salmonella Typhimurium, where the LuxS/Autoinducer-2 QS system controls the expression of the type three secretion system (TTSS) encoded by Salmonella pathogenicity island-1 (SPI-1). TTSS-1 effectors are not only key virulence factors but also promising tools for precise intracellular delivery of therapeutic agents. These bacterial-derived effectors function as cell-penetrating effectors (CPEs), autonomously translocating into host cells and overcoming major hurdles in pharmacology by enabling targeted drug delivery. Our digital twin framework enables the simulation of Salmonella cells engineered to sense their environment and dynamically regulate virulence expression for the controlled secretion of effectors, including potential applications in delivering surrogate drugs to cancerous cells. This work establishes an advanced computational platform for optimizing bacterial therapies and accelerating the development of next-generation biomedical solutions.