On the Power of Uncertainties in Microbial System Modeling: No Need To Hide Them Anymore
Abstract
For decades, microbiologists have considered uncertainties as an undesired side effect of experimental protocols. As a consequence, standard microbial system modeling strives to hide uncertainties for the sake of deterministic understanding. However, recent studies have highlighted greater experimental variability than expected and emphasized uncertainties not as a weakness but as a necessary feature of complex microbial systems. We therefore advocate that biological uncertainties need to be considered foundational facets that must be incorporated in models. Not only will understanding these uncertainties improve our understanding and identification of microbial traits, it will also provide fundamental insights on microbial systems as a whole. Taking into account uncertainties within microbial models calls for new validation techniques. Formal verification already overcomes this shortcoming by proposing modeling frameworks and validation techniques dedicated to probabilistic models. However, further work remains to extract the full potential of such techniques in the context of microbial models. Herein, we demonstrate how statistical model checking can enhance the development of microbial models by building confidence in the estimation of critical parameters and through improved sensitivity analyses.
- Authors:
-
[1];
[1];
- Centre National de la Recherche Scientifique (CNRS), Nantes (France)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); Centre National de la Recherche Scientifique (CNRS)
- OSTI Identifier:
- 1476559
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- mSystems
- Additional Journal Information:
- Journal Volume: 2; Journal Issue: 6; Journal ID: ISSN 2379-5077
- Publisher:
- American Society for Microbiology
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES
Citation Formats
Delahaye, Benoit, Eveillard, Damien, and Bouskill, Nicholas. On the Power of Uncertainties in Microbial System Modeling: No Need To Hide Them Anymore. United States: N. p., 2017.
Web. doi:10.1128/mSystems.00169-17.
Delahaye, Benoit, Eveillard, Damien, & Bouskill, Nicholas. On the Power of Uncertainties in Microbial System Modeling: No Need To Hide Them Anymore. United States. https://doi.org/10.1128/mSystems.00169-17
Delahaye, Benoit, Eveillard, Damien, and Bouskill, Nicholas. Tue .
"On the Power of Uncertainties in Microbial System Modeling: No Need To Hide Them Anymore". United States. https://doi.org/10.1128/mSystems.00169-17. https://www.osti.gov/servlets/purl/1476559.
@article{osti_1476559,
title = {On the Power of Uncertainties in Microbial System Modeling: No Need To Hide Them Anymore},
author = {Delahaye, Benoit and Eveillard, Damien and Bouskill, Nicholas},
abstractNote = {For decades, microbiologists have considered uncertainties as an undesired side effect of experimental protocols. As a consequence, standard microbial system modeling strives to hide uncertainties for the sake of deterministic understanding. However, recent studies have highlighted greater experimental variability than expected and emphasized uncertainties not as a weakness but as a necessary feature of complex microbial systems. We therefore advocate that biological uncertainties need to be considered foundational facets that must be incorporated in models. Not only will understanding these uncertainties improve our understanding and identification of microbial traits, it will also provide fundamental insights on microbial systems as a whole. Taking into account uncertainties within microbial models calls for new validation techniques. Formal verification already overcomes this shortcoming by proposing modeling frameworks and validation techniques dedicated to probabilistic models. However, further work remains to extract the full potential of such techniques in the context of microbial models. Herein, we demonstrate how statistical model checking can enhance the development of microbial models by building confidence in the estimation of critical parameters and through improved sensitivity analyses.},
doi = {10.1128/mSystems.00169-17},
journal = {mSystems},
number = 6,
volume = 2,
place = {United States},
year = {Tue Dec 05 00:00:00 EST 2017},
month = {Tue Dec 05 00:00:00 EST 2017}
}
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