Entrainment and Control of Bacterial Populations: An in Silico Study over a Spatially Extended Agent Based Model.
We extend a spatially explicit agent based model (ABM) developed previously to investigate entrainment and control of the emergent behavior of a population of synchronized oscillating cells in a microfluidic chamber. Unlike most of the work in models of control of cellular systems which focus on temporal changes, we model individual cells with spatial dependencies which may contribute to certain behavioral responses. We use the model to investigate the response of both open loop and closed loop strategies, such as proportional control (P-control), proportional-integral control (PI-control) and proportional-integral-derivative control (PID-control), to heterogeinities and growth in the cell population, variations of the control parameters and spatial effects such as diffusion in the spatially explicit setting of a microfluidic chamber setup. We show that, as expected from the theory of phase locking in dynamical systems, open loop control can only entrain the cell population in a subset of forcing periods, with a wide variety of dynamical behaviors obtained outside these regions of entrainment. Closed-loop control is shown instead to guarantee entrainment in a much wider region of control parameter space although presenting limitations when the population size increases over a certain threshold. In silico tracking experiments are also performed to validate the ability of classical control approaches to achieve other reference behaviors such as a desired constant output or a linearly varying one. All simulations are carried out in BSim, an advanced agent-based simulator of microbial population which is here extended ad hoc to include the effects of control strategies acting onto the population.
Petros
P
Mina
Department of Mathematics, University of Exeter , Exeter, United Kingdom.
Krasimira
K
Tsaneva-Atanasova
Department of Information and Computer Engineering, University of Naples Federico II , Naples, Italy.
Mario di
Md
Bernardo
D000465:Algorithms; D001426:Bacterial Proteins / Q000235:genetics / Q000378:metabolism; D003198:Computer Simulation; D025461:Feedback, Physiological; D015964:Gene Expression Regulation, Bacterial; D053263:Gene Regulatory Networks; D049452:Green Fluorescent Proteins / Q000235:genetics; D008666:Metalloendopeptidases / Q000235:genetics / Q000378:metabolism; D044085:Microfluidics; D008954:Models, Biological; D053038:Quorum Sensing; D014157:Transcription Factors / Q000235:genetics
D001426:Bacterial Proteins; C085427:LuxI protein, Bacteria; D014157:Transcription Factors; D049452:Green Fluorescent Proteins; C406735:AiiA protein, Bacillus; D008666:Metalloendopeptidases
agent based modeling; control theory; genetic regulatory network; microfluidics; quorum sensing