Three-dimensional stochastic simulation of chemoattractant-mediated excitability in cells

• Published in: PLoS computational biology Vol. 17; no. 7; p. e1008803
• Main Authors: Biswas, Debojyoti, Devreotes, Peter N, Iglesias, Pablo A
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 01.07.2021; Public Library of Science (PLoS)
• Subjects: Adaptation; Analysis; Biology and Life Sciences; Cell adhesion & migration; Cell migration; Cellular signal transduction; Chemotaxis; Computer and Information Sciences; Diffusion; Excitability; Finite element method; G protein-coupled receptors; G proteins; Mathematical models; Mathematics; Modular systems; Noise; Partial differential equations; Physical Sciences; Protein interaction; Proteins; Reaction-diffusion equations; Research and Analysis Methods; Signal transduction; Signaling; Simulation; Stochastic analysis; Stochasticity; United States
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1008803

During the last decade, a consensus has emerged that the stochastic triggering of an excitable system drives pseudopod formation and subsequent migration of amoeboid cells. The presence of chemoattractant stimuli alters the threshold for triggering this activity and can bias the direction of migration. Though noise plays an important role in these behaviors, mathematical models have typically ignored its origin and merely introduced it as an external signal into a series of reaction-diffusion equations. Here we consider a more realistic description based on a reaction-diffusion master equation formalism to implement these networks. In this scheme, noise arises naturally from a stochastic description of the various reaction and diffusion terms. Working on a three-dimensional geometry in which separate compartments are divided into a tetrahedral mesh, we implement a modular description of the system, consisting of G-protein coupled receptor signaling (GPCR), a local excitation-global inhibition mechanism (LEGI), and signal transduction excitable network (STEN). Our models implement detailed biochemical descriptions whenever this information is available, such as in the GPCR and G-protein interactions. In contrast, where the biochemical entities are less certain, such as the LEGI mechanism, we consider various possible schemes and highlight the differences between them. Our simulations show that even when the LEGI mechanism displays perfect adaptation in terms of the mean level of proteins, the variance shows a dose-dependence. This differs between the various models considered, suggesting a possible means for determining experimentally among the various potential networks. Overall, our simulations recreate temporal and spatial patterns observed experimentally in both wild-type and perturbed cells, providing further evidence for the excitable system paradigm. Moreover, because of the overall importance and ubiquity of the modules we consider, including GPCR signaling and adaptation, our results will be of interest beyond the field of directed migration.