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      Detailed Simulations of Cell Biology with Smoldyn 2.1

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          Abstract

          Most cellular processes depend on intracellular locations and random collisions of individual protein molecules. To model these processes, we developed algorithms to simulate the diffusion, membrane interactions, and reactions of individual molecules, and implemented these in the Smoldyn program. Compared to the popular MCell and ChemCell simulators, we found that Smoldyn was in many cases more accurate, more computationally efficient, and easier to use. Using Smoldyn, we modeled pheromone response system signaling among yeast cells of opposite mating type. This model showed that secreted Bar1 protease might help a cell identify the fittest mating partner by sharpening the pheromone concentration gradient. This model involved about 200,000 protein molecules, about 7000 cubic microns of volume, and about 75 minutes of simulated time; it took about 10 hours to run. Over the next several years, as faster computers become available, Smoldyn will allow researchers to model and explore systems the size of entire bacterial and smaller eukaryotic cells.

          Author Summary

          We developed a general-purpose biochemical simulation program, called Smoldyn. It represents proteins and other molecules of interest with point-like particles that diffuse, interact with surfaces, and react, all in continuous space. This high level of detail allows users to investigate spatial organization within cells and natural stochastic variability. Although similar to the MCell and ChemCell programs, Smoldyn is more accurate and runs faster. Smoldyn also supports many unique features, such as commands that a “virtual experimenter” can execute during simulations and automatic reaction network expansion for simulating protein complexes. We illustrate Smoldyn's capabilities with a model of signaling between yeast cells of opposite mating type. It investigates the role of the secreted protease Bar1, which inactivates mating pheromone. Intuitively, it might seem that inactivating most of the pheromone would make a cell less able to detect the local pheromone concentration gradient. In contrast, we found that Bar1 secretion improves pheromone gradient detectability: the local gradient is sharpened because pheromone is progressively inactivated as it diffuses through a cloud of Bar1. This result helps interpret experiments that showed that Bar1 secretion helped cells distinguish between potential mates, and suggests that Bar1 helps yeast cells identify the fittest mating partners.

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          Most cited references39

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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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            Chemotaxis in Escherichia coli analysed by three-dimensional tracking.

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              FAST MONTE CARLO SIMULATION METHODS FOR BIOLOGICAL REACTION-DIFFUSION SYSTEMS IN SOLUTION AND ON SURFACES.

              Many important physiological processes operate at time and space scales far beyond those accessible to atom-realistic simulations, and yet discrete stochastic rather than continuum methods may best represent finite numbers of molecules interacting in complex cellular spaces. We describe and validate new tools and algorithms developed for a new version of the MCell simulation program (MCell3), which supports generalized Monte Carlo modeling of diffusion and chemical reaction in solution, on surfaces representing membranes, and combinations thereof. A new syntax for describing the spatial directionality of surface reactions is introduced, along with optimizations and algorithms that can substantially reduce computational costs (e.g., event scheduling, variable time and space steps). Examples for simple reactions in simple spaces are validated by comparison to analytic solutions. Thus we show how spatially realistic Monte Carlo simulations of biological systems can be far more cost-effective than often is assumed, and provide a level of accuracy and insight beyond that of continuum methods.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                March 2010
                March 2010
                12 March 2010
                : 6
                : 3
                : e1000705
                Affiliations
                [1 ]Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
                [2 ]National Centre for Biological Sciences, Tata Institute for Fundamental Research, Bangalore, India
                [3 ]Molecular Sciences Institute, Berkeley, California, United States of America
                [4 ]Department of Bioengineering, and Howard Hughes Medical Institute, University of California, Berkeley, California, United States of America
                [5 ]Virtual Institute for Microbial Stress and Survival, http://vimss.lbl.gov
                University of Washington, United States of America
                Author notes
                [¤]

                Current address: Division of Basic Science, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America

                Conceived and designed the experiments: SSA. Performed the experiments: SSA. Analyzed the data: SSA. Wrote the paper: SSA RB. Wrote the software: SSA NJA. Provided support and guidance: RB APA.

                Article
                09-PLCB-RA-0947R2
                10.1371/journal.pcbi.1000705
                2837389
                20300644
                6792b615-2086-418f-8b1d-2f2786ac8eb1
                Andrews 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
                : 10 August 2009
                : 4 February 2010
                Page count
                Pages: 10
                Categories
                Research Article
                Biochemistry/Theory and Simulation
                Biophysics/Theory and Simulation
                Cell Biology/Cell Signaling
                Computational Biology/Computational Neuroscience
                Computational Biology/Synthetic Biology
                Computational Biology/Systems Biology

                Quantitative & Systems biology
                Quantitative & Systems biology

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