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      Deterministic Versus Stochastic Cell Polarisation Through Wave-Pinning

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          Abstract

          Cell polarization is an important part of the response of eukaryotic cells to stimuli, and forms a primary step in cell motility, differentiation, and many cellular functions. Among the important biochemical players implicated in the onset of intracellular asymmetries that constitute the early phases of polarization are the Rho GTPases, such as Cdc42, Rac, and Rho, which present high active concentration levels in a spatially localized manner. Rho GTPases exhibit positive feedback-driven interconversion between distinct active and inactive forms, the former residing on the cell membrane, and the latter predominantly in the cytosol. A deterministic model of the dynamics of a single Rho GTPase described earlier by Mori et al. exhibits sustained polarization by a wave-pinning mechanism. It remained, however, unclear how such polarization behaves at typically low cellular concentrations, as stochasticity could significantly affect the dynamics. We therefore study the low copy number dynamics of this model, using a stochastic kinetics framework based on the Gillespie algorithm, and propose statistical and analytic techniques which help us analyse the equilibrium behaviour of our stochastic system. We use local perturbation analysis to predict parameter regimes for initiation of polarity and wave-pinning in our deterministic system, and compare these predictions with deterministic and stochastic spatial simulations. Comparing the behaviour of the stochastic with the deterministic system, we determine the threshold number of molecules required for robust polarization in a given effective reaction volume. We show that when the molecule number is lowered wave-pinning behaviour is lost due to an increasingly large transition zone as well as increasing fluctuations in the pinning position, due to which a broadness can be reached that is unsustainable, causing the collapse of the wave, while the variations in the high and low equilibrium levels are much less affected.

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          A Flexible Growth Function for Empirical Use

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            Stochastic mechanisms in gene expression.

            In cellular regulatory networks, genetic activity is controlled by molecular signals that determine when and how often a given gene is transcribed. In genetically controlled pathways, the protein product encoded by one gene often regulates expression of other genes. The time delay, after activation of the first promoter, to reach an effective level to control the next promoter depends on the rate of protein accumulation. We have analyzed the chemical reactions controlling transcript initiation and translation termination in a single such "genetically coupled" link as a precursor to modeling networks constructed from many such links. Simulation of the processes of gene expression shows that proteins are produced from an activated promoter in short bursts of variable numbers of proteins that occur at random time intervals. As a result, there can be large differences in the time between successive events in regulatory cascades across a cell population. In addition, the random pattern of expression of competitive effectors can produce probabilistic outcomes in switching mechanisms that select between alternative regulatory paths. The result can be a partitioning of the cell population into different phenotypes as the cells follow different paths. There are numerous unexplained examples of phenotypic variations in isogenic populations of both prokaryotic and eukaryotic cells that may be the result of these stochastic gene expression mechanisms.
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              Control, exploitation and tolerance of intracellular noise.

              Noise has many roles in biological function, including generation of errors in DNA replication leading to mutation and evolution, noise-driven divergence of cell fates, noise-induced amplification of signals, and maintenance of the quantitative individuality of cells. Yet there is order to the behaviour and development of cells. They operate within strict parameters and in many cases this behaviour seems robust, implying that noise is largely filtered by the system. How can we explain the use, rejection and sensitivity to noise that is found in biological systems? An exploration of the sources and consequences of noise calls for the use of stochastic models.
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                Author and article information

                Contributors
                keshet@math.ubc.ca
                Veronica.Grieneisen@jic.ac.uk
                Journal
                Bull Math Biol
                Bull. Math. Biol
                Bulletin of Mathematical Biology
                Springer-Verlag (New York )
                0092-8240
                1522-9602
                7 September 2012
                7 September 2012
                November 2012
                : 74
                : 11
                : 2570-2599
                Affiliations
                [1 ]Computational & Systems Biology, John Innes Centre, Norwich Research Park, Norwich, NR4 7UH UK
                [2 ]Mathematics Department, The University of British Columbia, Vancouver, BC V6T 1Z2 Canada
                Article
                9766
                10.1007/s11538-012-9766-5
                3480592
                22956290
                eb640958-b164-477d-aca4-d3271a5feb1b
                © The Author(s) 2012
                History
                : 4 October 2011
                : 2 August 2012
                Categories
                Original Article
                Custom metadata
                © Society for Mathematical Biology 2012

                Quantitative & Systems biology
                local perturbation analysis,rho gtpase,stochastic model,wave-pinning,polarization

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