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      Particle-based simulations of polarity establishment reveal stochastic promotion of Turing pattern formation

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          Abstract

          Polarity establishment, the spontaneous generation of asymmetric molecular distributions, is a crucial component of many cellular functions. Saccharomyces cerevisiae (yeast) undergoes directed growth during budding and mating, and is an ideal model organism for studying polarization. In yeast and many other cell types, the Rho GTPase Cdc42 is the key molecular player in polarity establishment. During yeast polarization, multiple patches of Cdc42 initially form, then resolve into a single front. Because polarization relies on strong positive feedback, it is likely that the amplification of molecular-level fluctuations underlies the generation of multiple nascent patches. In the absence of spatial cues, these fluctuations may be key to driving polarization. Here we used particle-based simulations to investigate the role of stochastic effects in a Turing-type model of yeast polarity establishment. In the model, reactions take place either between two molecules on the membrane, or between a cytosolic and a membrane-bound molecule. Thus, we developed a computational platform that explicitly simulates molecules at and near the cell membrane, and implicitly handles molecules away from the membrane. To evaluate stochastic effects, we compared particle simulations to deterministic reaction-diffusion equation simulations. Defining macroscopic rate constants that are consistent with the microscopic parameters for this system is challenging, because diffusion occurs in two dimensions and particles exchange between the membrane and cytoplasm. We address this problem by empirically estimating macroscopic rate constants from appropriately designed particle-based simulations. Ultimately, we find that stochastic fluctuations speed polarity establishment and permit polarization in parameter regions predicted to be Turing stable. These effects can operate at Cdc42 abundances expected of yeast cells, and promote polarization on timescales consistent with experimental results. To our knowledge, our work represents the first particle-based simulations of a model for yeast polarization that is based on a Turing mechanism.

          Author summary

          Many cells need to generate and maintain biochemical signals in specific subcellular regions. This phenomenon is broadly called polarity establishment, and is important in fundamental processes such as cell migration and differentiation. A key polarity factor found in diverse organisms, including yeast and humans, is the protein Cdc42. In yeast, Cdc42-dependent polarization occurs through a self-reinforcing biochemical signaling loop. Directional cues can guide polarity establishment, but interestingly, yeast can polarize in the absence of such a cue. The mechanism thought to underlie this symmetry breaking involves the amplification of inhomogeneities in molecular distributions that arise from molecular-level fluctuations. We investigated the effects of random fluctuations on polarization by performing particle-based simulations of the Cdc42 signaling network. We found that fluctuations can facilitate polarization, allowing faster polarization, and polarization over a broader range of concentrations. Our observations may help understand how polarity works in other systems.

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          Most cited references48

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          The chemical Langevin equation

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            Stochastic simulation of chemical reactions with spatial resolution and single molecule detail.

            Methods are presented for simulating chemical reaction networks with a spatial resolution that is accurate to nearly the size scale of individual molecules. Using an intuitive picture of chemical reaction systems, each molecule is treated as a point-like particle that diffuses freely in three-dimensional space. When a pair of reactive molecules collide, such as an enzyme and its substrate, a reaction occurs and the simulated reactants are replaced by products. Achieving accurate bimolecular reaction kinetics is surprisingly difficult, requiring a careful consideration of reaction processes that are often overlooked. This includes whether the rate of a reaction is at steady-state and the probability that multiple reaction products collide with each other to yield a back reaction. Inputs to the simulation are experimental reaction rates, diffusion coefficients and the simulation time step. From these are calculated the simulation parameters, including the 'binding radius' and the 'unbinding radius', where the former defines the separation for a molecular collision and the latter is the initial separation between a pair of reaction products. Analytic solutions are presented for some simulation parameters while others are calculated using look-up tables. Capabilities of these methods are demonstrated with simulations of a simple bimolecular reaction and the Lotka-Volterra system.
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              Dynamics of Cdc42 network embodies a Turing-type mechanism of yeast cell polarity.

              Complex biochemical networks can be understood by identifying their principal regulatory motifs and mode of action. We model the early phase of budding yeast cellular polarization and show that the biochemical processes in the presumptive bud site comprise a Turing-type mechanism. The roles of the prototypical activator and substrate are played by GTPase Cdc42 in its active and inactive states, respectively. We demonstrate that the nucleotide cycling of Cdc42 converts cellular energy into a stable cluster of activated Cdc42. This energy drives a continuous membrane-cytoplasmic exchange of the cluster components to counteract diffusive spread of the cluster. This exchange explains why only one bud forms per cell cycle, because the winner-takes-all competition of candidate sites inevitably selects a single site.
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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                12 March 2018
                March 2018
                : 14
                : 3
                : e1006016
                Affiliations
                [1 ] Department of Chemistry, The University of North Carolina, Chapel Hill, NC, United States of America
                [2 ] Program in Molecular and Cellular Biophysics, The University of North Carolina, Chapel Hill, NC, United States of America
                [3 ] Department of Pharmacology, The University of North Carolina, Chapel Hill, NC, United States of America
                Rice University, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-9738-8109
                Article
                PCOMPBIOL-D-17-01575
                10.1371/journal.pcbi.1006016
                5864077
                29529021
                8cc06bac-d596-48cb-85e7-30f65b41a315
                © 2018 Pablo et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 20 September 2017
                : 31 January 2018
                Page count
                Figures: 10, Tables: 3, Pages: 25
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: T32 GM008570-21A1
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01 GM079271
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01 GM114136
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000183, Army Research Office;
                Award ID: W911NF-15-1-063
                Award Recipient :
                This work was supported in part by National Institutes of Health ( www.nih.gov) grants T32 GM008570-21A1 (MP), R01 GM079271 (TCE), and R01 GM114136 (TCE), as well as Army Research Office ( www.arl.army.mil) grant W911NF-15-1-063 (TCE). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Cell Biology
                Cellular Structures and Organelles
                Cell Membranes
                Biology and Life Sciences
                Biochemistry
                Biochemical Simulations
                Biology and Life Sciences
                Computational Biology
                Biochemical Simulations
                Biology and Life Sciences
                Cell Biology
                Cell Physiology
                Cell Polarity
                Biology and Life Sciences
                Cell Biology
                Cellular Structures and Organelles
                Cytoplasm
                Research and Analysis Methods
                Simulation and Modeling
                Research and Analysis Methods
                Experimental Organism Systems
                Model Organisms
                Saccharomyces Cerevisiae
                Research and Analysis Methods
                Model Organisms
                Saccharomyces Cerevisiae
                Biology and Life Sciences
                Organisms
                Eukaryota
                Fungi
                Yeast
                Saccharomyces
                Saccharomyces Cerevisiae
                Research and Analysis Methods
                Experimental Organism Systems
                Yeast and Fungal Models
                Saccharomyces Cerevisiae
                Physical Sciences
                Chemistry
                Chemical Reactions
                Reactants
                Research and Analysis Methods
                Experimental Organism Systems
                Yeast and Fungal Models
                Custom metadata
                vor-update-to-uncorrected-proof
                2018-03-22
                All relevant data are within the paper and its Supporting Information files.

                Quantitative & Systems biology
                Quantitative & Systems biology

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