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A Bayesian framework for the analysis of systems biology models of the brain
Systems biology models are used to understand complex biological and physiological systems. Interpretation of these models is an important part of developing this understanding. These models are often fit to experimental data in order to understand how the system has produced various phenomena or be...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6505968/ https://www.ncbi.nlm.nih.gov/pubmed/31026277 http://dx.doi.org/10.1371/journal.pcbi.1006631 |
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author | Russell-Buckland, Joshua Barnes, Christopher P. Tachtsidis, Ilias |
author_facet | Russell-Buckland, Joshua Barnes, Christopher P. Tachtsidis, Ilias |
author_sort | Russell-Buckland, Joshua |
collection | PubMed |
description | Systems biology models are used to understand complex biological and physiological systems. Interpretation of these models is an important part of developing this understanding. These models are often fit to experimental data in order to understand how the system has produced various phenomena or behaviour that are seen in the data. In this paper, we have outlined a framework that can be used to perform Bayesian analysis of complex systems biology models. In particular, we have focussed on analysing a systems biology of the brain using both simulated and measured data. By using a combination of sensitivity analysis and approximate Bayesian computation, we have shown that it is possible to obtain distributions of parameters that can better guard against misinterpretation of results, as compared to a maximum likelihood estimate based approach. This is done through analysis of simulated and experimental data. NIRS measurements were simulated using the same simulated systemic input data for the model in a ‘healthy’ and ‘impaired’ state. By analysing both of these datasets, we show that different parameter spaces can be distinguished and compared between different physiological states or conditions. Finally, we analyse experimental data using the new Bayesian framework and the previous maximum likelihood estimate approach, showing that the Bayesian approach provides a more complete understanding of the parameter space. |
format | Online Article Text |
id | pubmed-6505968 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-65059682019-05-23 A Bayesian framework for the analysis of systems biology models of the brain Russell-Buckland, Joshua Barnes, Christopher P. Tachtsidis, Ilias PLoS Comput Biol Research Article Systems biology models are used to understand complex biological and physiological systems. Interpretation of these models is an important part of developing this understanding. These models are often fit to experimental data in order to understand how the system has produced various phenomena or behaviour that are seen in the data. In this paper, we have outlined a framework that can be used to perform Bayesian analysis of complex systems biology models. In particular, we have focussed on analysing a systems biology of the brain using both simulated and measured data. By using a combination of sensitivity analysis and approximate Bayesian computation, we have shown that it is possible to obtain distributions of parameters that can better guard against misinterpretation of results, as compared to a maximum likelihood estimate based approach. This is done through analysis of simulated and experimental data. NIRS measurements were simulated using the same simulated systemic input data for the model in a ‘healthy’ and ‘impaired’ state. By analysing both of these datasets, we show that different parameter spaces can be distinguished and compared between different physiological states or conditions. Finally, we analyse experimental data using the new Bayesian framework and the previous maximum likelihood estimate approach, showing that the Bayesian approach provides a more complete understanding of the parameter space. Public Library of Science 2019-04-26 /pmc/articles/PMC6505968/ /pubmed/31026277 http://dx.doi.org/10.1371/journal.pcbi.1006631 Text en © 2019 Russell-Buckland 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 Russell-Buckland, Joshua Barnes, Christopher P. Tachtsidis, Ilias A Bayesian framework for the analysis of systems biology models of the brain |
title | A Bayesian framework for the analysis of systems biology models of the brain |
title_full | A Bayesian framework for the analysis of systems biology models of the brain |
title_fullStr | A Bayesian framework for the analysis of systems biology models of the brain |
title_full_unstemmed | A Bayesian framework for the analysis of systems biology models of the brain |
title_short | A Bayesian framework for the analysis of systems biology models of the brain |
title_sort | bayesian framework for the analysis of systems biology models of the brain |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6505968/ https://www.ncbi.nlm.nih.gov/pubmed/31026277 http://dx.doi.org/10.1371/journal.pcbi.1006631 |
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