Stabilizing Target Microbial Community-Level Behaviors via External Modulation of Quorum-Sensing Communication Networks

Efforts are underway to develop quantitative approaches for predictively controlling the activity level of complex microbial communities. One strategy involves targeting microbial chemical communication networks, which often serve as hubs for regulating global gene expression. A well-known example of such a network is quorum sensing (QS), whereby cells use small molecules called autoinducers (AI) to regulate density-dependent gene expression. Recent work (Yusufaly and Boedicker, Physical Biology 14, 046002 (2017)) has shown that multispecies QS circuits, with multiple chemically distinct AI molecules, can be mapped onto a Hopfield neural network. Here, we extend this result, and show that, by artificially pumping excess AI molecules into a community, we can define a perceptron, where the external AI current serves as a tunable input that drives the community to a certain gene expression output. This perceptron can be interpreted as a control system, allowing the methods of control theory to be applied to rationally design input current strategies that drive a community to ‘target’ output gene expression levels. This formalism is observed to be especially useful for analyzing the tradeoffs between performance and robustness for different strategies.