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OFFl Models: Novel Schema for Dynamical Modeling of Biological Systems

Flow diagrams are a common tool used to help build and interpret models of dynamical systems, often in biological contexts such as consumer-resource models and similar compartmental models. Typically, their usage is intuitive and informal. Here, we present a formalized version of flow diagrams as a...

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Autores principales: Ogbunugafor, C. Brandon, Robinson, Sean P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4896432/
https://www.ncbi.nlm.nih.gov/pubmed/27270918
http://dx.doi.org/10.1371/journal.pone.0156844
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author Ogbunugafor, C. Brandon
Robinson, Sean P.
author_facet Ogbunugafor, C. Brandon
Robinson, Sean P.
author_sort Ogbunugafor, C. Brandon
collection PubMed
description Flow diagrams are a common tool used to help build and interpret models of dynamical systems, often in biological contexts such as consumer-resource models and similar compartmental models. Typically, their usage is intuitive and informal. Here, we present a formalized version of flow diagrams as a kind of weighted directed graph which follow a strict grammar, which translate into a system of ordinary differential equations (ODEs) by a single unambiguous rule, and which have an equivalent representation as a relational database. (We abbreviate this schema of “ODEs and formalized flow diagrams” as OFFL.) Drawing a diagram within this strict grammar encourages a mental discipline on the part of the modeler in which all dynamical processes of a system are thought of as interactions between dynamical species that draw parcels from one or more source species and deposit them into target species according to a set of transformation rules. From these rules, the net rate of change for each species can be derived. The modeling schema can therefore be understood as both an epistemic and practical heuristic for modeling, serving both as an organizational framework for the model building process and as a mechanism for deriving ODEs. All steps of the schema beyond the initial scientific (intuitive, creative) abstraction of natural observations into model variables are algorithmic and easily carried out by a computer, thus enabling the future development of a dedicated software implementation. Such tools would empower the modeler to consider significantly more complex models than practical limitations might have otherwise proscribed, since the modeling framework itself manages that complexity on the modeler’s behalf. In this report, we describe the chief motivations for OFFL, carefully outline its implementation, and utilize a range of classic examples from ecology and epidemiology to showcase its features.
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spelling pubmed-48964322016-06-16 OFFl Models: Novel Schema for Dynamical Modeling of Biological Systems Ogbunugafor, C. Brandon Robinson, Sean P. PLoS One Research Article Flow diagrams are a common tool used to help build and interpret models of dynamical systems, often in biological contexts such as consumer-resource models and similar compartmental models. Typically, their usage is intuitive and informal. Here, we present a formalized version of flow diagrams as a kind of weighted directed graph which follow a strict grammar, which translate into a system of ordinary differential equations (ODEs) by a single unambiguous rule, and which have an equivalent representation as a relational database. (We abbreviate this schema of “ODEs and formalized flow diagrams” as OFFL.) Drawing a diagram within this strict grammar encourages a mental discipline on the part of the modeler in which all dynamical processes of a system are thought of as interactions between dynamical species that draw parcels from one or more source species and deposit them into target species according to a set of transformation rules. From these rules, the net rate of change for each species can be derived. The modeling schema can therefore be understood as both an epistemic and practical heuristic for modeling, serving both as an organizational framework for the model building process and as a mechanism for deriving ODEs. All steps of the schema beyond the initial scientific (intuitive, creative) abstraction of natural observations into model variables are algorithmic and easily carried out by a computer, thus enabling the future development of a dedicated software implementation. Such tools would empower the modeler to consider significantly more complex models than practical limitations might have otherwise proscribed, since the modeling framework itself manages that complexity on the modeler’s behalf. In this report, we describe the chief motivations for OFFL, carefully outline its implementation, and utilize a range of classic examples from ecology and epidemiology to showcase its features. Public Library of Science 2016-06-07 /pmc/articles/PMC4896432/ /pubmed/27270918 http://dx.doi.org/10.1371/journal.pone.0156844 Text en © 2016 Ogbunugafor, Robinson http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ogbunugafor, C. Brandon
Robinson, Sean P.
OFFl Models: Novel Schema for Dynamical Modeling of Biological Systems
title OFFl Models: Novel Schema for Dynamical Modeling of Biological Systems
title_full OFFl Models: Novel Schema for Dynamical Modeling of Biological Systems
title_fullStr OFFl Models: Novel Schema for Dynamical Modeling of Biological Systems
title_full_unstemmed OFFl Models: Novel Schema for Dynamical Modeling of Biological Systems
title_short OFFl Models: Novel Schema for Dynamical Modeling of Biological Systems
title_sort offl models: novel schema for dynamical modeling of biological systems
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4896432/
https://www.ncbi.nlm.nih.gov/pubmed/27270918
http://dx.doi.org/10.1371/journal.pone.0156844
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