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Mathematical Identification of Critical Reactions in the Interlocked Feedback Model
Dynamic simulations are necessary for understanding the mechanism of how biochemical networks generate robust properties to environmental stresses or genetic changes. Sensitivity analysis allows the linking of robustness to network structure. However, it yields only local properties regarding a part...
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Formato: | Texto |
Lenguaje: | English |
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Public Library of Science
2007
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2040204/ https://www.ncbi.nlm.nih.gov/pubmed/17971866 http://dx.doi.org/10.1371/journal.pone.0001103 |
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author | Kurata, Hiroyuki Tanaka, Takayuki Ohnishi, Fumitaka |
author_facet | Kurata, Hiroyuki Tanaka, Takayuki Ohnishi, Fumitaka |
author_sort | Kurata, Hiroyuki |
collection | PubMed |
description | Dynamic simulations are necessary for understanding the mechanism of how biochemical networks generate robust properties to environmental stresses or genetic changes. Sensitivity analysis allows the linking of robustness to network structure. However, it yields only local properties regarding a particular choice of plausible parameter values, because it is hard to know the exact parameter values in vivo. Global and firm results are needed that do not depend on particular parameter values. We propose mathematical analysis for robustness (MAR) that consists of the novel evolutionary search that explores all possible solution vectors of kinetic parameters satisfying the target dynamics and robustness analysis. New criteria, parameter spectrum width and the variability of solution vectors for parameters, are introduced to determine whether the search is exhaustive. In robustness analysis, in addition to single parameter sensitivity analysis, robustness to multiple parameter perturbation is defined. Combining the sensitivity analysis and the robustness analysis to multiple parameter perturbation enables identifying critical reactions. Use of MAR clearly identified the critical reactions responsible for determining the circadian cycle in the Drosophila interlocked circadian clock model. In highly robust models, while the parameter vectors are greatly varied, the critical reactions with a high sensitivity are uniquely determined. Interestingly, not only the per-tim loop but also the dclk-cyc loop strongly affect the period of PER, although the dclk-cyc loop hardly changes its amplitude and it is not potentially influential. In conclusion, MAR is a powerful method to explore wide parameter space without human-biases and to link a robust property to network architectures without knowing the exact parameter values. MAR identifies the reactions critically responsible for determining the period and amplitude in the interlocked feedback model and suggests that the circadian clock intensively evolves or designs the kinetic parameters so that it creates a highly robust cycle. |
format | Text |
id | pubmed-2040204 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-20402042007-10-31 Mathematical Identification of Critical Reactions in the Interlocked Feedback Model Kurata, Hiroyuki Tanaka, Takayuki Ohnishi, Fumitaka PLoS One Research Article Dynamic simulations are necessary for understanding the mechanism of how biochemical networks generate robust properties to environmental stresses or genetic changes. Sensitivity analysis allows the linking of robustness to network structure. However, it yields only local properties regarding a particular choice of plausible parameter values, because it is hard to know the exact parameter values in vivo. Global and firm results are needed that do not depend on particular parameter values. We propose mathematical analysis for robustness (MAR) that consists of the novel evolutionary search that explores all possible solution vectors of kinetic parameters satisfying the target dynamics and robustness analysis. New criteria, parameter spectrum width and the variability of solution vectors for parameters, are introduced to determine whether the search is exhaustive. In robustness analysis, in addition to single parameter sensitivity analysis, robustness to multiple parameter perturbation is defined. Combining the sensitivity analysis and the robustness analysis to multiple parameter perturbation enables identifying critical reactions. Use of MAR clearly identified the critical reactions responsible for determining the circadian cycle in the Drosophila interlocked circadian clock model. In highly robust models, while the parameter vectors are greatly varied, the critical reactions with a high sensitivity are uniquely determined. Interestingly, not only the per-tim loop but also the dclk-cyc loop strongly affect the period of PER, although the dclk-cyc loop hardly changes its amplitude and it is not potentially influential. In conclusion, MAR is a powerful method to explore wide parameter space without human-biases and to link a robust property to network architectures without knowing the exact parameter values. MAR identifies the reactions critically responsible for determining the period and amplitude in the interlocked feedback model and suggests that the circadian clock intensively evolves or designs the kinetic parameters so that it creates a highly robust cycle. Public Library of Science 2007-10-31 /pmc/articles/PMC2040204/ /pubmed/17971866 http://dx.doi.org/10.1371/journal.pone.0001103 Text en Kurata 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, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Kurata, Hiroyuki Tanaka, Takayuki Ohnishi, Fumitaka Mathematical Identification of Critical Reactions in the Interlocked Feedback Model |
title | Mathematical Identification of Critical Reactions in the Interlocked Feedback Model |
title_full | Mathematical Identification of Critical Reactions in the Interlocked Feedback Model |
title_fullStr | Mathematical Identification of Critical Reactions in the Interlocked Feedback Model |
title_full_unstemmed | Mathematical Identification of Critical Reactions in the Interlocked Feedback Model |
title_short | Mathematical Identification of Critical Reactions in the Interlocked Feedback Model |
title_sort | mathematical identification of critical reactions in the interlocked feedback model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2040204/ https://www.ncbi.nlm.nih.gov/pubmed/17971866 http://dx.doi.org/10.1371/journal.pone.0001103 |
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