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Searching for Errors in Models of Complex Dynamic Systems
Mathematical modeling is seen as a key step to understand, predict, and control the temporal dynamics of interacting systems in such diverse areas like physics, biology, medicine, and economics. However, for large and complex systems we usually have only partial knowledge about the network, the coup...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7830364/ https://www.ncbi.nlm.nih.gov/pubmed/33505318 http://dx.doi.org/10.3389/fphys.2020.612590 |
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author | Kahl, Dominik Kschischo, Maik |
author_facet | Kahl, Dominik Kschischo, Maik |
author_sort | Kahl, Dominik |
collection | PubMed |
description | Mathematical modeling is seen as a key step to understand, predict, and control the temporal dynamics of interacting systems in such diverse areas like physics, biology, medicine, and economics. However, for large and complex systems we usually have only partial knowledge about the network, the coupling functions, and the interactions with the environment governing the dynamic behavior. This incomplete knowledge induces structural model errors which can in turn be the cause of erroneous model predictions or misguided interpretations. Uncovering the location of such structural model errors in large networks can be a daunting task for a modeler. Here, we present a data driven method to search for structural model errors and to confine their position in large and complex dynamic networks. We introduce a coherence measure for pairs of network nodes, which indicates, how difficult it is to distinguish these nodes as sources of an error. By clustering network nodes into coherence groups and inferring the cluster inputs we can decide, which cluster is affected by an error. We demonstrate the utility of our method for the C. elegans neural network, for a signal transduction model for UV-B light induced morphogenesis and for synthetic examples. |
format | Online Article Text |
id | pubmed-7830364 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78303642021-01-26 Searching for Errors in Models of Complex Dynamic Systems Kahl, Dominik Kschischo, Maik Front Physiol Physiology Mathematical modeling is seen as a key step to understand, predict, and control the temporal dynamics of interacting systems in such diverse areas like physics, biology, medicine, and economics. However, for large and complex systems we usually have only partial knowledge about the network, the coupling functions, and the interactions with the environment governing the dynamic behavior. This incomplete knowledge induces structural model errors which can in turn be the cause of erroneous model predictions or misguided interpretations. Uncovering the location of such structural model errors in large networks can be a daunting task for a modeler. Here, we present a data driven method to search for structural model errors and to confine their position in large and complex dynamic networks. We introduce a coherence measure for pairs of network nodes, which indicates, how difficult it is to distinguish these nodes as sources of an error. By clustering network nodes into coherence groups and inferring the cluster inputs we can decide, which cluster is affected by an error. We demonstrate the utility of our method for the C. elegans neural network, for a signal transduction model for UV-B light induced morphogenesis and for synthetic examples. Frontiers Media S.A. 2021-01-11 /pmc/articles/PMC7830364/ /pubmed/33505318 http://dx.doi.org/10.3389/fphys.2020.612590 Text en Copyright © 2021 Kahl and Kschischo. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Kahl, Dominik Kschischo, Maik Searching for Errors in Models of Complex Dynamic Systems |
title | Searching for Errors in Models of Complex Dynamic Systems |
title_full | Searching for Errors in Models of Complex Dynamic Systems |
title_fullStr | Searching for Errors in Models of Complex Dynamic Systems |
title_full_unstemmed | Searching for Errors in Models of Complex Dynamic Systems |
title_short | Searching for Errors in Models of Complex Dynamic Systems |
title_sort | searching for errors in models of complex dynamic systems |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7830364/ https://www.ncbi.nlm.nih.gov/pubmed/33505318 http://dx.doi.org/10.3389/fphys.2020.612590 |
work_keys_str_mv | AT kahldominik searchingforerrorsinmodelsofcomplexdynamicsystems AT kschischomaik searchingforerrorsinmodelsofcomplexdynamicsystems |