IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0079345.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0079345
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0079345&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0079345?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Benito Chen-Charpentier, 2021. "Stochastic Modeling of Plant Virus Propagation with Biological Control," Mathematics, MDPI, vol. 9(5), pages 1-16, February.
    3. 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.
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kamara, Abdul A. & Wang, Xiangjun & Mouanguissa, Lagès Nadège, 2020. "Analytical solution for post-death transmission model of Ebola epidemics," Applied Mathematics and Computation, Elsevier, vol. 367(C).
    2. zu Dohna, Heinrich & Pineda-Krch, Mario, 2010. "Fitting parameters of stochastic birth–death models to metapopulation data," Theoretical Population Biology, Elsevier, vol. 78(2), pages 71-76.
    3. Laperrière, Vincent & Brugger, Katharina & Rubel, Franz, 2016. "Cross-scale modeling of a vector-borne disease, from the individual to the metapopulation: The seasonal dynamics of sylvatic plague in Kazakhstan," Ecological Modelling, Elsevier, vol. 342(C), pages 34-48.
    4. repec:jss:jstsof:33:i03 is not listed on IDEAS
    5. Soetaert, Karline & Petzoldt, Thomas, 2010. "Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i03).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0079345. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.