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Nested Event Representation for Automated Assembly of Cell Signaling Network Models

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Formal Methods. FM 2019 International Workshops (FM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12233))

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Abstract

The rate at which biological literature is published far outpaces the current capabilities of modeling experts. In order to facilitate the automation of model assembly, we improve upon methods for converting machine reading output obtained from papers studying intracellular networks into discrete element rule-based models. We introduce a graph representation that can capture the complicated semantics found in machine reading output. Specifically, we focus on extracting change-of-rate information available when network elements are found to inhibit or catalyze other interactions (nested events). We demonstrate the viability of this approach by measuring the prevalence of these nested events in cancer literature, as well as the success rates of two machine readers in capturing them. Finally, we show how our algorithm can translate between machine reading output and the new graphical form. By incorporating these more detailed interactions into the model, we can more accurately predict cellular dynamics on a broad scale, leading to improvements in experimental design and disease treatment discovery.

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Notes

  1. 1.

    http://trips.ihmc.us/parser/api.html.

  2. 2.

    http://agathon.sista.arizona.edu:8080/odinweb/api/nxml.

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Acknowledgements

This work was partially supported by DARPA award W911NF-17-1-0135, and by the Swanson School of Engineering and the Office of the Provost at the University of Pittsburgh. The authors would like to thank Dr. Cheryl Telmer of the Molecular Bio-sensor and Imaging Center at Carnegie Mellon University for her constructive feedback.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, USA

    Evan W. Becker, Kara N. Bocan & Natasa Miskov-Zivanov

  2. Department of Bioengineering, University of Pittsburgh, Pittsburgh, USA

    Natasa Miskov-Zivanov

  3. Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, USA

    Natasa Miskov-Zivanov

Authors
  1. Evan W. Becker
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  2. Kara N. Bocan
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  3. Natasa Miskov-Zivanov
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Corresponding author

Correspondence to Evan W. Becker .

Editor information

Editors and Affiliations

  1. McMaster University, Hamilton, ON, Canada

    Emil Sekerinski

  2. University of Porto, Porto, Portugal

    Nelma Moreira

  3. University of Minho, Braga, Portugal

    José N. Oliveira

  4. Argo Ai, Munich, Germany

    Daniel Ratiu

  5. University of Pisa, Pisa, Italy

    Riccardo Guidotti

  6. University of Liverpool, Liverpool, UK

    Marie Farrell

  7. University of Liverpool, Liverpool, UK

    Matt Luckcuck

  8. University of Exeter, Exeter, UK

    Diego Marmsoler

  9. University of Minho, Braga, Portugal

    José Campos

  10. University of Newcastle, Newcastle upon Tyne, UK

    Troy Astarte

  11. Claude Bernard University, Lyon, France

    Laure Gonnord

  12. Nazarbayev University, Nur-Sultan, Kazakhstan

    Antonio Cerone

  13. University of Surrey, Guildford, UK

    Luis Couto

  14. University of Surrey, Guildford, UK

    Brijesh Dongol

  15. University of Giessen, Giessen, Germany

    Martin Kutrib

  16. University of Lisbon, Lisbon, Portugal

    Pedro Monteiro

  17. Airbus Operations S.A.S., Toulouse, France

    David Delmas

Appendix

Appendix

Table 2. Precision and recall rates for nested event CLASSIFICATION from PMC1403772 (1) and PMC128294 (2) by REACH and TRIPS/DRUM systems.
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Table 3. Example 1 reading output from REACH: “Notch-IC expression blocks TCR-mediated phosphorylation of Akt”.
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Table 4. Example 2 reading output from REACH: “Expression of wild-type GSK3\(\beta \), which is inhibited by Akt”.
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Table 5. Example 3 reading output from REACH “Inactivation of GSK3\(\beta \) by the Wingless pathway enhanced Notch signals by preventing inhibitory phosphorylation of N-IC by GSK3”.
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Table 6. Example 4 reading output from REACH: “Akt construct was sufficient to potentiate the ability of pN1-IC to drive CBF1-dependent reporter”.
Full size table

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Becker, E.W., Bocan, K.N., Miskov-Zivanov, N. (2020). Nested Event Representation for Automated Assembly of Cell Signaling Network Models. In: Sekerinski, E., et al. Formal Methods. FM 2019 International Workshops. FM 2019. Lecture Notes in Computer Science(), vol 12233. Springer, Cham. https://doi.org/10.1007/978-3-030-54997-8_30

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  • DOI: https://doi.org/10.1007/978-3-030-54997-8_30

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