Exploring chemotaxis in spatio-temporal stoichiometric models of metabolism: Pseudomonas simiae as a case study

Summary/AbstractChemotaxis is the movement of cells or organisms in the direction of an increasing or decreasing gradient of a chemical, i.e. a chemoattractant or chemorepellent, respectively. In bacteria, this movement is typically manifested through the presence of bias in the random walk generated by run-and-tumble motility. Often, the chemoattractant is consumed by the organism, thus coupling the biophysical, directed motion with the biochemical, metabolic activity in the growing bacterial colony. In this chapter, we describe our approach to simulating chemotaxis, coupled with detailed metabolic activity, within the framework of our modeling software COMETS (Computation of Microbial Ecosystem in Time and Space). By combining genome-scale metabolic models, dynamic flux balance analysis and the newly implemented biophysical chemotaxis model, we built and qualitatively tested a chemotaxis module for COMETS. We applied this model to an automatically constructed genome-scale model of the plant commensal bacterium Pseudomonas simiae WCS417, which we hypothesized would perform chemotaxis on amino acids similar to its close relative Pseudomonas putida. COMETS simulations recapitulated the typical ring colony pattern, which is due to propagation at the colony front, indicative of bacteria growing on a chemoattractant-filled plate. By allowing for detailed examination of spatial distributions and time dynamics of the biomass, as well as concentration profiles of the metabolites involved in the growth and chemotaxis in bacterial colonies, the software method described here enables simulations of complex microbial patterns involving metabolism and directed motility. Beyond testing the appearance of the ring, future model validation could include comparisons of local growth rates and metabolic fluxes, including rates of metabolite uptake and secretion. The newly added COMETS chemotaxis capability integrates flux balance modeling with gradient-induced biomass propagation, paving the way for more realistic simulations of microbial ecosystem dynamics in complex environments, such as plant roots, where chemotaxis may play a fundamental role in microbiome-host dynamics.