Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1009035.html
   My bibliography  Save this article

Unsupervised logic-based mechanism inference for network-driven biological processes

Author

Listed:
  • Martina Prugger
  • Lukas Einkemmer
  • Samantha P Beik
  • Perry T Wasdin
  • Leonard A Harris
  • Carlos F Lopez
Abstract
Modern analytical techniques enable researchers to collect data about cellular states, before and after perturbations. These states can be characterized using analytical techniques, but the inference of regulatory interactions that explain and predict changes in these states remains a challenge. Here we present a generalizable, unsupervised approach to generate parameter-free, logic-based models of cellular processes, described by multiple discrete states. Our algorithm employs a Hamming-distance based approach to formulate, test, and identify optimized logic rules that link two states. Our approach comprises two steps. First, a model with no prior knowledge except for the mapping between initial and attractor states is built. We then employ biological constraints to improve model fidelity. Our algorithm automatically recovers the relevant dynamics for the explored models and recapitulates key aspects of the biochemical species concentration dynamics in the original model. We present the advantages and limitations of our work and discuss how our approach could be used to infer logic-based mechanisms of signaling, gene-regulatory, or other input-output processes describable by the Boolean formalism.Author summary: Mechanisms of biological processes that explain and predict biological behaviors continue to be challenging to attain. In this context, logic-based models with few parameters can be formulated to describe experimental data. However, constructing such networks based on the available evidence is often done in an ad-hoc, error-prone manner that reflects the bias of the modeler. Here we present an algorithm that infers Boolean logic models from mappings of initial states to steady states, from available experimental data, and without human supervision. Moreover, our methodology enables users to incorporate additional biological information (expert knowledge) to further refine Boolean models of cellular processes.

Suggested Citation

  • Martina Prugger & Lukas Einkemmer & Samantha P Beik & Perry T Wasdin & Leonard A Harris & Carlos F Lopez, 2021. "Unsupervised logic-based mechanism inference for network-driven biological processes," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-30, June.
    • Handle: RePEc:plo:pcbi00:1009035
    DOI: 10.1371/journal.pcbi.1009035
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009035
    Download Restriction: no
    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009035&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pcbi.1009035?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. Eshan D. Mitra & Raquel Dias & Richard G. Posner & William S. Hlavacek, 2018. "Using both qualitative and quantitative data in parameter identification for systems biology models," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
    2. Shu Wang & Jia-Ren Lin & Eduardo D Sontag & Peter K Sorger, 2019. "Inferring reaction network structure from single-cell, multiplex data, using toric systems theory," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-25, December.
    3. Loïc Paulevé & Juri Kolčák & Thomas Chatain & Stefan Haar, 2020. "Publisher Correction: Reconciling qualitative, abstract, and scalable modeling of biological networks," Nature Communications, Nature, vol. 11(1), pages 1-2, December.
    4. Misbah Razzaq & Loïc Paulevé & Anne Siegel & Julio Saez-Rodriguez & Jérémie Bourdon & Carito Guziolowski, 2018. "Computational discovery of dynamic cell line specific Boolean networks from multiplex time-course data," PLOS Computational Biology, Public Library of Science, vol. 14(10), pages 1-23, October.
    5. Loïc Paulevé & Juri Kolčák & Thomas Chatain & Stefan Haar, 2020. "Reconciling qualitative, abstract, and scalable modeling of biological networks," Nature Communications, Nature, vol. 11(1), pages 1-7, December.
    Full references (including those not matched with items on IDEAS)

    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.

      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:pcbi00:1009035. 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: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

      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.