Algorithms for discriminating between biochemical reaction network models: Towards systematic experimental design
We develop a methodology for distinguishing between biochemical reaction network models for biological systems modeled by nonlinear ordinary differential equations. We show how to choose the 'best' initial condition direction (the dependent variables, the state) so as to maximize the discr...
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| Fformat: | Conference item |
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2007
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| _version_ | 1826296210270453760 |
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| author | Papachristodoulou, A El-Samad, H IEEE |
| author_facet | Papachristodoulou, A El-Samad, H IEEE |
| author_sort | Papachristodoulou, A |
| collection | OXFORD |
| description | We develop a methodology for distinguishing between biochemical reaction network models for biological systems modeled by nonlinear ordinary differential equations. We show how to choose the 'best' initial condition direction (the dependent variables, the state) so as to maximize the discrepancy between the outputs of possible network models. This information could then be used to design new experiments with the hope that some of the networks would be invalidated from experimental data. The methodology is applied to discriminate between models of the network underlying chemoattractant-induced movement of Dictyostelium cells. © 2007 IEEE. |
| first_indexed | 2024-03-07T04:12:49Z |
| format | Conference item |
| id | oxford-uuid:c868fdfc-96cc-41e6-ab59-1f8480c1a841 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T04:12:49Z |
| publishDate | 2007 |
| record_format | dspace |
| spelling | oxford-uuid:c868fdfc-96cc-41e6-ab59-1f8480c1a8412022-03-27T06:51:56ZAlgorithms for discriminating between biochemical reaction network models: Towards systematic experimental designConference itemhttp://purl.org/coar/resource_type/c_5794uuid:c868fdfc-96cc-41e6-ab59-1f8480c1a841Symplectic Elements at Oxford2007Papachristodoulou, AEl-Samad, HIEEEWe develop a methodology for distinguishing between biochemical reaction network models for biological systems modeled by nonlinear ordinary differential equations. We show how to choose the 'best' initial condition direction (the dependent variables, the state) so as to maximize the discrepancy between the outputs of possible network models. This information could then be used to design new experiments with the hope that some of the networks would be invalidated from experimental data. The methodology is applied to discriminate between models of the network underlying chemoattractant-induced movement of Dictyostelium cells. © 2007 IEEE. |
| spellingShingle | Papachristodoulou, A El-Samad, H IEEE Algorithms for discriminating between biochemical reaction network models: Towards systematic experimental design |
| title | Algorithms for discriminating between biochemical reaction network models: Towards systematic experimental design |
| title_full | Algorithms for discriminating between biochemical reaction network models: Towards systematic experimental design |
| title_fullStr | Algorithms for discriminating between biochemical reaction network models: Towards systematic experimental design |
| title_full_unstemmed | Algorithms for discriminating between biochemical reaction network models: Towards systematic experimental design |
| title_short | Algorithms for discriminating between biochemical reaction network models: Towards systematic experimental design |
| title_sort | algorithms for discriminating between biochemical reaction network models towards systematic experimental design |
| work_keys_str_mv | AT papachristodouloua algorithmsfordiscriminatingbetweenbiochemicalreactionnetworkmodelstowardssystematicexperimentaldesign AT elsamadh algorithmsfordiscriminatingbetweenbiochemicalreactionnetworkmodelstowardssystematicexperimentaldesign AT ieee algorithmsfordiscriminatingbetweenbiochemicalreactionnetworkmodelstowardssystematicexperimentaldesign |