Title:
Identification of neutral sets of biochemical systems models from time series data
Identification of neutral sets of biochemical systems models from time series data
| dc.contributor.author | Vilela, Marco | en_US |
| dc.contributor.author | Vinga, Susana | en_US |
| dc.contributor.author | Grivet, Marco A. | en_US |
| dc.contributor.author | Maia, Mattoso | en_US |
| dc.contributor.author | Voit, Eberhard O. | en_US |
| dc.contributor.author | Almeida, Jonas S. | en_US |
| dc.contributor.corporatename | Universidade Nova de Lisboa. Instituto de Tecnologia Química e Biológica | en_US |
| dc.contributor.corporatename | Instituto de Engenharia de Sistemas e Computadores. Investigação e Desenvolvimento | en_US |
| dc.contributor.corporatename | Pontifícia Universidade Católica do Rio de Janeiro. Centro Técnico-Científico | en_US |
| dc.contributor.corporatename | Pontifícia Universidade Católica do Rio de Janeiro. Centro de Estudo em Telecomunicações | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. Integrative BioSystems Institute | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. Dept. of Biomedical Engineering | en_US |
| dc.contributor.corporatename | Emory University. Dept. of Biomedical Engineering | en_US |
| dc.contributor.corporatename | University of Texas M.D. Anderson Cancer Center. Dept. of Bioinformatics and Computational Biology | en_US |
| dc.contributor.corporatename | Universidade Nova de Lisboa. Faculdade Ciências Médicas | en_US |
| dc.date.accessioned | 2011-11-11T21:07:33Z | |
| dc.date.available | 2011-11-11T21:07:33Z | |
| dc.date.issued | 2009-05 | |
| dc.description | © 2009 Vilela et al. ; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | en_US |
| dc.description | DOI: 10.1186/1752-0509-3-47 | en_US |
| dc.description.abstract | Background The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. Results In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. Conclusion The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments | en_US |
| dc.identifier.citation | Vilela, M., S. Vinga, M. A. Grivet Mattoso Maia, E. O. Voit, J. S. Almeida, "Identification of neutral sets of biochemical systems models from time series data," BMC Systems Biology 3: 47, 2009. | en_US |
| dc.identifier.issn | 1752-0509 | |
| dc.identifier.uri | http://hdl.handle.net/1853/41997 | |
| dc.language.iso | en_US | en |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.publisher.original | BioMed Central | en_US |
| dc.subject | Biological systems | en_US |
| dc.subject | Mathematical models | en_US |
| dc.subject | Parameter estimation | en_US |
| dc.subject | Time series data | en_US |
| dc.title | Identification of neutral sets of biochemical systems models from time series data | en_US |
| dc.type | Text | |
| dc.type.genre | Article | |
| dspace.entity.type | Publication | |
| local.contributor.author | Voit, Eberhard O. | |
| local.contributor.corporatename | Wallace H. Coulter Department of Biomedical Engineering | |
| local.contributor.corporatename | College of Engineering | |
| relation.isAuthorOfPublication | c5624674-7b6a-4cc7-8078-4bea75def349 | |
| relation.isOrgUnitOfPublication | da59be3c-3d0a-41da-91b9-ebe2ecc83b66 | |
| relation.isOrgUnitOfPublication | 7c022d60-21d5-497c-b552-95e489a06569 |
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