Spatial Stochastic Dynamics Enable Robust Cell Polarization

Abstract

Although cell polarity is an essential feature of living cells, it is far from being well-understood. Using a combination of computational modeling and biological experiments we closely examine an important prototype of cell polarity: the pheromone-induced formation of the yeast polarisome. Focusing on the role of noise and spatial heterogeneity, we develop and investigate two mechanistic spatial models of polarisome formation, one deterministic and the other stochastic, and compare the contrasting predictions of these two models against experimental phenotypes of wild-type and mutant cells. We find that the stochastic model can more robustly reproduce two fundamental characteristics observed in wild-type cells: a highly polarized phenotype via a mechanism that we refer to as spatial stochastic amplification, and the ability of the polarisome to track a moving pheromone input. Moreover, we find that only the stochastic model can simultaneously reproduce these characteristics of the wild-type phenotype and the multi-polarisome phenotype of a deletion mutant of the scaffolding protein Spa2. Significantly, our analysis also demonstrates that higher levels of stochastic noise results in increased robustness of polarization to parameter variation. Furthermore, our work suggests a novel role for a polarisome protein in the stabilization of actin cables. These findings elucidate the intricate role of spatial stochastic effects in cell polarity, giving support to a cellular model where noise and spatial heterogeneity combine to achieve robust biological function.

Role of Polarized G Protein Signaling in Tracking Pheromone Gradients

“…Two-color imaging revealed that all of these markers concentrated in the vicinity of the polarity patch (Fig. 1D), but Spa2 and Sec4 formed a tighter cluster than Bem1, as previously noted (Lawson et al, 2013). In contrast, Abp1-marked endocytosis sites were clustered in a broader zone surrounding the tight Sec4 patch.…”

Macromolecular Crowding Regulates the Gene Expression Profile by Limiting Diffusion

“…We assume that the initial concentrations of mRNA and the target protein are zero, and use spatial stochastic simulation to investigate the gene expression pattern, a model that has been widely used and verified by both theoretical [36–39] and experimental [39, 40] observations. To account for crowding, we developed a modified next subvolume method (NSM) to approximately solve the reaction-diffusion master equation (RDME) [41] capable of explicitly treating the crowding agent amount, distribution, and interactions (Materials and Methods).…”

Pattern Formation at Cellular Membranes by Phosphorylation and Dephosphorylation of Proteins

“…The typical large concentration of proteins in cells permits the use of deterministic dynamics, however, the concentration of enzymes is smaller and stochastic effects may become relevant. The use of a stochastic model may enhance domain formation [34,35].…”