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Open AccessFeature PaperReview

The Spectrum of Mechanism-Oriented Models and Methods for Explanations of Biological Phenomena

1
Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94143, USA
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Department of Biomedical Engineering and Computational Biomodeling Core, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
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Departments of Neurology and Physiology and Pharmacology, SUNY Downstate Medical Center, Department Neurology, Kings County Hospital Center, Brooklyn, NY 11203, USA
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Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA
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Department of Physics and Astronomy, Brigham Young University, Provo, UT 84602, USA
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InSilico Labs LLC, Houston, TX 77002, USA
*
Author to whom correspondence should be addressed.
Processes 2018, 6(5), 56; https://doi.org/10.3390/pr6050056
Received: 15 April 2018 / Revised: 5 May 2018 / Accepted: 6 May 2018 / Published: 14 May 2018
(This article belongs to the Special Issue Methods in Computational Biology)
Developing and improving mechanism-oriented computational models to better explain biological phenomena is a dynamic and expanding frontier. As the complexity of targeted phenomena has increased, so too has the diversity in methods and terminologies, often at the expense of clarity, which can make reproduction challenging, even problematic. To encourage improved semantic and methodological clarity, we describe the spectrum of Mechanism-oriented Models being used to develop explanations of biological phenomena. We cluster explanations of phenomena into three broad groups. We then expand them into seven workflow-related model types having distinguishable features. We name each type and illustrate with examples drawn from the literature. These model types may contribute to the foundation of an ontology of mechanism-based biomedical simulation research. We show that the different model types manifest and exert their scientific usefulness by enhancing and extending different forms and degrees of explanation. The process starts with knowledge about the phenomenon and continues with explanatory and mathematical descriptions. Those descriptions are transformed into software and used to perform experimental explorations by running and examining simulation output. The credibility of inferences is thus linked to having easy access to the scientific and technical provenance from each workflow stage. View Full-Text
Keywords: computational model; explanatory model; hybrid model; mechanism; mechanistic model; modeling methods; provenance; workflow; systems modeling; simulation computational model; explanatory model; hybrid model; mechanism; mechanistic model; modeling methods; provenance; workflow; systems modeling; simulation
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Hunt, C.A.; Erdemir, A.; Lytton, W.W.; Mac Gabhann, F.; Sander, E.A.; Transtrum, M.K.; Mulugeta, L. The Spectrum of Mechanism-Oriented Models and Methods for Explanations of Biological Phenomena. Processes 2018, 6, 56.

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