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

Martina Prugger, Lukas Einkemmer, Samantha P. Beik, Leonard A. Harris, View ORCID ProfileCarlos F. Lopez
doi: https://doi.org/10.1101/2020.12.15.422874
Martina Prugger
1Department of Biochemistry, University of Innsbruck, Innsbruck, Tyrol, Austria
2Department of Biochemistry, School of Medicine, Vanderbilt University, Nashville, TN, USA
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Lukas Einkemmer
3Department of Mathematics, University of Innsbruck, Innsbruck, Tyrol, Austria
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Samantha P. Beik
2Department of Biochemistry, School of Medicine, Vanderbilt University, Nashville, TN, USA
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Leonard A. Harris
2Department of Biochemistry, School of Medicine, Vanderbilt University, Nashville, TN, USA
4Currently at Department of Biomedical Engineering, University of Arkansas, Fayetteville, AR, USA
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Carlos F. Lopez
2Department of Biochemistry, School of Medicine, Vanderbilt University, Nashville, TN, USA
5Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
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  • ORCID record for Carlos F. Lopez
  • For correspondence: c.lopez@vanderbilt.edu
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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 mechanistic hypotheses of cellular processes, described by multiple discrete states. Our algorithm employs a Hamming-distance based approach to formulate, test, and identify, the best mechanism that links 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. Second, we employ biological constraints to improve model fidelity. Our algorithm automatically recovers the relevant dynamics for the explored models and recapitulates all aspects of the original models biochemical species concentration dynamics. We then conclude by placing our results in the context of ongoing work in the field and discuss how our approach could be used to infer mechanisms of signaling, gene-regulatory, and any other input-output processes describable by logic-based mechanisms.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 15, 2020.
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Unsupervised logic-based mechanism inference for network-driven biological processes
Martina Prugger, Lukas Einkemmer, Samantha P. Beik, Leonard A. Harris, Carlos F. Lopez
bioRxiv 2020.12.15.422874; doi: https://doi.org/10.1101/2020.12.15.422874
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Unsupervised logic-based mechanism inference for network-driven biological processes
Martina Prugger, Lukas Einkemmer, Samantha P. Beik, Leonard A. Harris, Carlos F. Lopez
bioRxiv 2020.12.15.422874; doi: https://doi.org/10.1101/2020.12.15.422874

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