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Simulation enabled search for explanatory mechanisms of the fracture healing process

A significant portion of bone fractures fail to heal properly, increasing healthcare costs. Advances in fracture management have slowed because translation barriers have limited generation of mechanism-based explanations for the healing process. When uncertainties are numerous, analogical modeling c...

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Detalles Bibliográficos
Autores principales: Kennedy, Ryan C., Marmor, Meir, Marcucio, Ralph, Hunt, C. Anthony
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5812655/
https://www.ncbi.nlm.nih.gov/pubmed/29394245
http://dx.doi.org/10.1371/journal.pcbi.1005980
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author Kennedy, Ryan C.
Marmor, Meir
Marcucio, Ralph
Hunt, C. Anthony
author_facet Kennedy, Ryan C.
Marmor, Meir
Marcucio, Ralph
Hunt, C. Anthony
author_sort Kennedy, Ryan C.
collection PubMed
description A significant portion of bone fractures fail to heal properly, increasing healthcare costs. Advances in fracture management have slowed because translation barriers have limited generation of mechanism-based explanations for the healing process. When uncertainties are numerous, analogical modeling can be an effective strategy for developing plausible explanations of complex phenomena. We demonstrate the feasibility of engineering analogical models in software to facilitate discovery of biomimetic explanations for how fracture healing may progress. Concrete analogical models—Callus Analogs—were created using the MASON simulation toolkit. We designated a Target Region initial state within a characteristic tissue section of mouse tibia fracture at day-7 and posited a corresponding day-10 Target Region final state. The goal was to discover a coarse-grain analog mechanism that would enable the discretized initial state to transform itself into the corresponding Target Region final state, thereby providing an alternative way to study the healing process. One of nine quasi-autonomous Tissue Unit types is assigned to each grid space, which maps to an 80×80 μm region of the tissue section. All Tissue Units have an opportunity each time step to act based on individualized logic, probabilities, and information about adjacent neighbors. Action causes transition from one Tissue Unit type to another, and simulation through several thousand time steps generates a coarse-grain analog—a theory—of the healing process. We prespecified a minimum measure of success: simulated and actual Target Region states achieve ≥ 70% Similarity. We used an iterative refinement protocol to explore many combinations of Tissue Unit logic and action constraints. Workflows progressed through four stages of analog mechanisms. Similarities of 73–90% were achieved for Mechanisms 2–4. The range of Upper-Level similarities increased to 83–94% when we allowed for uncertainty about two Tissue Unit designations. We have demonstrated how Callus Analog experiments provide domain experts with a fresh medium and tools for thinking about and understanding the fracture healing process.
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spelling pubmed-58126552018-02-28 Simulation enabled search for explanatory mechanisms of the fracture healing process Kennedy, Ryan C. Marmor, Meir Marcucio, Ralph Hunt, C. Anthony PLoS Comput Biol Research Article A significant portion of bone fractures fail to heal properly, increasing healthcare costs. Advances in fracture management have slowed because translation barriers have limited generation of mechanism-based explanations for the healing process. When uncertainties are numerous, analogical modeling can be an effective strategy for developing plausible explanations of complex phenomena. We demonstrate the feasibility of engineering analogical models in software to facilitate discovery of biomimetic explanations for how fracture healing may progress. Concrete analogical models—Callus Analogs—were created using the MASON simulation toolkit. We designated a Target Region initial state within a characteristic tissue section of mouse tibia fracture at day-7 and posited a corresponding day-10 Target Region final state. The goal was to discover a coarse-grain analog mechanism that would enable the discretized initial state to transform itself into the corresponding Target Region final state, thereby providing an alternative way to study the healing process. One of nine quasi-autonomous Tissue Unit types is assigned to each grid space, which maps to an 80×80 μm region of the tissue section. All Tissue Units have an opportunity each time step to act based on individualized logic, probabilities, and information about adjacent neighbors. Action causes transition from one Tissue Unit type to another, and simulation through several thousand time steps generates a coarse-grain analog—a theory—of the healing process. We prespecified a minimum measure of success: simulated and actual Target Region states achieve ≥ 70% Similarity. We used an iterative refinement protocol to explore many combinations of Tissue Unit logic and action constraints. Workflows progressed through four stages of analog mechanisms. Similarities of 73–90% were achieved for Mechanisms 2–4. The range of Upper-Level similarities increased to 83–94% when we allowed for uncertainty about two Tissue Unit designations. We have demonstrated how Callus Analog experiments provide domain experts with a fresh medium and tools for thinking about and understanding the fracture healing process. Public Library of Science 2018-02-02 /pmc/articles/PMC5812655/ /pubmed/29394245 http://dx.doi.org/10.1371/journal.pcbi.1005980 Text en © 2018 Kennedy et al 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
Kennedy, Ryan C.
Marmor, Meir
Marcucio, Ralph
Hunt, C. Anthony
Simulation enabled search for explanatory mechanisms of the fracture healing process
title Simulation enabled search for explanatory mechanisms of the fracture healing process
title_full Simulation enabled search for explanatory mechanisms of the fracture healing process
title_fullStr Simulation enabled search for explanatory mechanisms of the fracture healing process
title_full_unstemmed Simulation enabled search for explanatory mechanisms of the fracture healing process
title_short Simulation enabled search for explanatory mechanisms of the fracture healing process
title_sort simulation enabled search for explanatory mechanisms of the fracture healing process
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5812655/
https://www.ncbi.nlm.nih.gov/pubmed/29394245
http://dx.doi.org/10.1371/journal.pcbi.1005980
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