Detailed Simulations of Cell Biology with Smoldyn 2.1

Andrews, Steven S.; Addy, Nathan J.; Brent, Roger; Arkin, Adam P.

doi:10.1371/journal.pcbi.1000705

Title: Detailed Simulations of Cell Biology with Smoldyn 2.1

Journal Article · 12 March 2010 · PLoS Computational Biology (Online)

DOI: https://doi.org/10.1371/journal.pcbi.1000705 · OSTI ID:1627198

Andrews, Steven S. ^[1]; Addy, Nathan J. ^[2]; Brent, Roger ^[2]; Arkin, Adam P. ^[3]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Physical Biosciences Division; Tata Inst. of Fundamental Research, Bangalore (India). National Centre for Biological Sciences; Molecular Sciences Inst., Berkeley, CA (United States); DOE/OSTI
Molecular Sciences Inst., Berkeley, CA (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Physical Biosciences Division; Univ. of California, Berkeley, CA (United States). Howard Hughes Medical Inst. Dept. of Bioengineering; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Virtual Inst. for Microbial Stress and Survival, http://vimss.lbl.gov

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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1627198

Journal Information:: PLoS Computational Biology (Online), Journal Name: PLoS Computational Biology (Online) Journal Issue: 3 Vol. 6; ISSN 1553-7358

Publisher:: Public Library of ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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A Density-Dependent Switch Drives Stochastic Clustering and Polarization of Signaling Molecules Jilkine, Alexandra; Angenent, Sigurd B.; Wu, Lani F. PLoS Computational Biology, Vol. 7, Issue 11 https://doi.org/10.1371/journal.pcbi.1002271	journal	November 2011
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Cell death as a trigger for morphogenesis Aguilar, Boris; Ghaffarizadeh, Ahmadreza; Johnson, Christopher D. PLOS ONE, Vol. 13, Issue 3 https://doi.org/10.1371/journal.pone.0191089	journal	March 2018
GutLogo: Agent-based modeling framework to investigate spatial and temporal dynamics in the gut microbiome Lin, Charlie; Culver, Joshua; Weston, Bronson PLOS ONE, Vol. 13, Issue 11 https://doi.org/10.1371/journal.pone.0207072	journal	November 2018
Yeast Dynamically Modify Their Environment to Achieve Better Mating Efficiency W., Mather,; C., Elston, T.; S., Nayak, The University of North Carolina at Chapel Hill University Libraries https://doi.org/10.17615/xm1p-fj49	text	January 2011
How Important Is Protein Diffusion in Prokaryotes? Schavemaker, Paul E.; Boersma, Arnold J.; Poolman, Bert Frontiers in Molecular Biosciences, Vol. 5 https://doi.org/10.3389/fmolb.2018.00093	journal	November 2018
Computational Models for Calcium-Mediated Astrocyte Functions Manninen, Tiina; Havela, Riikka; Linne, Marja-Leena Frontiers in Computational Neuroscience, Vol. 12 https://doi.org/10.3389/fncom.2018.00014	journal	April 2018
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Spatial Simulations in Systems Biology: From Molecules to Cells Klann, Michael; Koeppl, Heinz International Journal of Molecular Sciences, Vol. 13, Issue 6 https://doi.org/10.3390/ijms13067798	journal	June 2012
Spatial Simulations in Systems Biology: From Molecules to Cells Klann, Michael; Koeppl, Heinz ETH Zurich https://doi.org/10.3929/ethz-b-000051659	text	January 2012
Nucleolar tethering mediates pairing between the IgH and Myc loci Strongin, Daniel E.; Groudine, Mark; Politz, Joan C. Ritland Nucleus, Vol. 5, Issue 5 https://doi.org/10.4161/nucl.36233	journal	August 2014
Input-output relations in biological systems: measurement, information and the Hill equation Frank, Steven A. arXiv https://doi.org/10.48550/arxiv.1312.2838	text	January 2013
Accurate Reaction-Diffusion Operator Splitting on Tetrahedral Meshes for Parallel Stochastic Molecular Simulations Hepburn, I.; Chen, W.; De Schutter, E. arXiv https://doi.org/10.48550/arxiv.1512.03126	text	January 2015

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