In this work, we use particle swarm optimization (PSO) to adjust the parameters of a membrane computing (MC) model of a synthetic autoinducer-2 (AI-2) signalling system in genetically engineered Escherichia coli bacteria.Bacteria release, receive and recognize signalling molecules in order to exchange information. These signalling molecules are responsible for coordinating gene expression at the population level in response to various stimuli such as size of the population, nutrient availability and other biochemical signals. This bacterial cell-to-cell communication is known as Quorum Sensing (QS). AI-2, from Vibrio harveyi, is the signaling molecule of interest in this study.We present a non-deterministic in silico model of Autoinducer-2 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 data relating to the biochemical parameters of this signalling system, we used a generic particle swarm optimization (PSO) algorithm to discover optimal solutions for the rule stochasticity constants. Our results were compared to the expected trends in quorum sensing behaviour. Ultimately, the results obtained from the PSO are thought to be in accordance with the predicted behaviour of the synthetic AI-2 signalling system.
BackgroundWe are creating software for agent-based simulation and visualization of bio-molecular processes in bacterial and eukaryotic cells. As a first example, we have built a 3-dimensional, interactive computer model of an Escherichia coli bacterium and its associated biomolecular processes. Our illustrative model focuses on the gene regulatory processes that control the expression of genes involved in the lactose operon. Prokaryo, our agent-based cell simulator, incorporates cellular structures, such as plasma membranes and cytoplasm, as well as elements of the molecular machinery, including RNA polymerase, messenger RNA, lactose permease, and ribosomes.ResultsThe dynamics of cellular ’agents’ are defined by their rules of interaction, implemented as finite state machines. The agents are embedded within a 3-dimensional virtual environment with simulated physical and electrochemical properties. The hybrid model is driven by a combination of (1) mathematical equations (DEQs) to capture higher-scale phenomena and (2) agent-based rules to implement localized interactions among a small number of molecular elements. Consequently, our model is able to capture phenomena across multiple spatial scales, from changing concentration gradients to one-on-one molecular interactions.We use the classic gene regulatory mechanism of the lactose operon to demonstrate our model’s resolution, visual presentation, and real-time interactivity. Our agent-based model expands on a sophisticated mathematical E. coli metabolism model, through which we highlight our model’s scientific validity.ConclusionWe believe that through illustration and interactive exploratory learning a model system like Prokaryo can enhance the general understanding and perception of biomolecular processes. Our agent-DEQ hybrid modeling approach can also be of value to conceptualize, illustrate, and—eventually—validate cell experiments in the wet lab.
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