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Detailed Simulations of Cell Biology with Smoldyn 2.1

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  • Steven S Andrews
  • Nathan J Addy
  • Roger Brent
  • Adam P Arkin

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.

Suggested Citation

  • Steven S Andrews & Nathan J Addy & Roger Brent & Adam P Arkin, 2010. "Detailed Simulations of Cell Biology with Smoldyn 2.1," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-10, March.
  • Handle: RePEc:plo:pcbi00:1000705
    DOI: 10.1371/journal.pcbi.1000705
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    References listed on IDEAS

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    1. Alejandro Colman-Lerner & Andrew Gordon & Eduard Serra & Tina Chin & Orna Resnekov & Drew Endy & C. Gustavo Pesce & Roger Brent, 2005. "Regulated cell-to-cell variation in a cell-fate decision system," Nature, Nature, vol. 437(7059), pages 699-706, September.
    2. Naama Barkai & Mark D. Rose & Ned S. Wingreen, 1998. "Protease helps yeast find mating partners," Nature, Nature, vol. 396(6710), pages 422-423, December.
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    Cited by:

    1. James C Schaff & Fei Gao & Ye Li & Igor L Novak & Boris M Slepchenko, 2016. "Numerical Approach to Spatial Deterministic-Stochastic Models Arising in Cell Biology," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-23, December.
    2. Brian Drawert & Andreas Hellander & Ben Bales & Debjani Banerjee & Giovanni Bellesia & Bernie J Daigle Jr. & Geoffrey Douglas & Mengyuan Gu & Anand Gupta & Stefan Hellander & Chris Horuk & Dibyendu Na, 2016. "Stochastic Simulation Service: Bridging the Gap between the Computational Expert and the Biologist," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-15, December.
    3. Johannes Schöneberg & Frank Noé, 2013. "ReaDDy - A Software for Particle-Based Reaction-Diffusion Dynamics in Crowded Cellular Environments," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-14, September.
    4. Rory M Donovan & Jose-Juan Tapia & Devin P Sullivan & James R Faeder & Robert F Murphy & Markus Dittrich & Daniel M Zuckerman, 2016. "Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-25, February.

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