Identifying robust hysteresis in networks.
We present a new modeling and computational tool that computes rigorous summaries of network dynamics over large sets of parameter values. These summaries, organized in a database, can be searched for observed dynamics, e.g., bistability and hysteresis, to discover parameter regimes over which they...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2018-04-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1006121 |
| _version_ | 1818927012196122624 |
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| author | Tomáš Gedeon Bree Cummins Shaun Harker Konstantin Mischaikow |
| author_facet | Tomáš Gedeon Bree Cummins Shaun Harker Konstantin Mischaikow |
| author_sort | Tomáš Gedeon |
| collection | DOAJ |
| description | We present a new modeling and computational tool that computes rigorous summaries of network dynamics over large sets of parameter values. These summaries, organized in a database, can be searched for observed dynamics, e.g., bistability and hysteresis, to discover parameter regimes over which they are supported. We illustrate our approach on several networks underlying the restriction point of the cell cycle in humans and yeast. We rank networks by how robustly they support hysteresis, which is the observed phenotype. We find that the best 6-node human network and the yeast network share similar topology and robustness of hysteresis, in spite of having no homology between the corresponding nodes of the network. Our approach provides a new tool linking network structure and dynamics. |
| first_indexed | 2024-12-20T03:06:14Z |
| format | Article |
| id | doaj.art-b94e27828fda41e3a5baa1f1a6480c1b |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-20T03:06:14Z |
| publishDate | 2018-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-b94e27828fda41e3a5baa1f1a6480c1b2022-12-21T19:55:36ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582018-04-01144e100612110.1371/journal.pcbi.1006121Identifying robust hysteresis in networks.Tomáš GedeonBree CumminsShaun HarkerKonstantin MischaikowWe present a new modeling and computational tool that computes rigorous summaries of network dynamics over large sets of parameter values. These summaries, organized in a database, can be searched for observed dynamics, e.g., bistability and hysteresis, to discover parameter regimes over which they are supported. We illustrate our approach on several networks underlying the restriction point of the cell cycle in humans and yeast. We rank networks by how robustly they support hysteresis, which is the observed phenotype. We find that the best 6-node human network and the yeast network share similar topology and robustness of hysteresis, in spite of having no homology between the corresponding nodes of the network. Our approach provides a new tool linking network structure and dynamics.https://doi.org/10.1371/journal.pcbi.1006121 |
| spellingShingle | Tomáš Gedeon Bree Cummins Shaun Harker Konstantin Mischaikow Identifying robust hysteresis in networks. PLoS Computational Biology |
| title | Identifying robust hysteresis in networks. |
| title_full | Identifying robust hysteresis in networks. |
| title_fullStr | Identifying robust hysteresis in networks. |
| title_full_unstemmed | Identifying robust hysteresis in networks. |
| title_short | Identifying robust hysteresis in networks. |
| title_sort | identifying robust hysteresis in networks |
| url | https://doi.org/10.1371/journal.pcbi.1006121 |
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