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StochPy: A Comprehensive, User-Friendly Tool for Simulating Stochastic Biological Processes

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  • Timo R Maarleveld
  • Brett G Olivier
  • Frank J Bruggeman

Abstract

Single-cell and single-molecule measurements indicate the importance of stochastic phenomena in cell biology. Stochasticity creates spontaneous differences in the copy numbers of key macromolecules and the timing of reaction events between genetically-identical cells. Mathematical models are indispensable for the study of phenotypic stochasticity in cellular decision-making and cell survival. There is a demand for versatile, stochastic modeling environments with extensive, preprogrammed statistics functions and plotting capabilities that hide the mathematics from the novice users and offers low-level programming access to the experienced user. Here we present StochPy (Stochastic modeling in Python), which is a flexible software tool for stochastic simulation in cell biology. It provides various stochastic simulation algorithms, SBML support, analyses of the probability distributions of molecule copy numbers and event waiting times, analyses of stochastic time series, and a range of additional statistical functions and plotting facilities for stochastic simulations. We illustrate the functionality of StochPy with stochastic models of gene expression, cell division, and single-molecule enzyme kinetics. StochPy has been successfully tested against the SBML stochastic test suite, passing all tests. StochPy is a comprehensive software package for stochastic simulation of the molecular control networks of living cells. It allows novice and experienced users to study stochastic phenomena in cell biology. The integration with other Python software makes StochPy both a user-friendly and easily extendible simulation tool.

Suggested Citation

  • Timo R Maarleveld & Brett G Olivier & Frank J Bruggeman, 2013. "StochPy: A Comprehensive, User-Friendly Tool for Simulating Stochastic Biological Processes," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-10, November.
    • Handle: RePEc:plo:pone00:0079345
    DOI: 10.1371/journal.pone.0079345
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    References listed on IDEAS

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    1. Pineda-Krch, Mario, 2008. "GillespieSSA: Implementing the Gillespie Stochastic Simulation Algorithm in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i12).
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    1. Stephanie S. Johnson & Katelin C. Jackson & Matthew S. Mietchen & Samir Sbai & Elissa J. Schwartz & Eric T. Lofgren, 2021. "Excess Risk of COVID-19 to University Populations Resulting from In-Person Sporting Events," IJERPH, MDPI, vol. 18(16), pages 1-13, August.
    2. Christopher T. Short & Matthew S. Mietchen & Eric T. Lofgren, 2020. "Assessing the Potential Impact of a Long-Acting Skin Disinfectant in the Prevention of Methicillin-Resistant Staphylococcus aureus Transmission," IJERPH, MDPI, vol. 17(5), pages 1-8, February.
    3. James A R Marshall & Andreagiovanni Reina & Thomas Bose, 2019. "Multiscale Modelling Tool: Mathematical modelling of collective behaviour without the maths," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-16, September.
    4. Benito Chen-Charpentier, 2021. "Stochastic Modeling of Plant Virus Propagation with Biological Control," Mathematics, MDPI, vol. 9(5), pages 1-16, February.

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