C: Parametric Evolution of a Bacterial Signalling System Formalized by Membrane Computing. IEEE CEC 2010: Congress on Evolutionary Computation

Abstract — In this work, we use particle swarm optimization (PSO) to adjust the parameters of a membrane computing (MC) model of synthetic autoinducer-2 (AI-2) signalling system in genetically engineered Escherichia coli bacteria. Bacteria release, receive and recognize signalling molecules in their immediate environment in order to exchange information. The information shared by the bacterial population plays a role in coordinating gene expression in response to various stimuli such as size of the population, nutrient availability and other biochemical signals. This form of AI-2 driven cell-to-cell communication is known as Quorum Sensing (QS). We present a non-deterministic in silico model of Quorum Sensing that is formalized by membrane computing (MC). The model is driven by 23 interaction rules that define biochemical interactions between independent compartments known as membranes. Due to the high dimensionality of this problem as well as lack of biological laboratory data, we utilized a generic particle swarm optimization (PSO) algorithm to discover optimal solutions for the rule stochasticity constants. Our results were compared to expected patterns of protein expression that occur during quorum sensing. Ultimately, the results obtained from the PSO closely mimic expected quorum sensing protein expression patterns in E. coli