Exploring the gap between dynamic and constraint-based models of metabolism
Systems biology provides new approaches for metabolic engineering through the development of models and methods for simulation and optimization of microbial metabolism. Here we explore the relationship between two modeling frameworks in common use namely, dynamic models with kinetic rate laws and co...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Elsevier
2016
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| Online Access: | http://hdl.handle.net/1721.1/101078 https://orcid.org/0000-0002-3320-3969 |
| _version_ | 1826213699596058624 |
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| author | Machado, Daniel Costa, Rafael S. Rocha, Isabel Tidor, Bruce Ferreira, Eugenio C. |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Machado, Daniel Costa, Rafael S. Rocha, Isabel Tidor, Bruce Ferreira, Eugenio C. |
| author_sort | Machado, Daniel |
| collection | MIT |
| description | Systems biology provides new approaches for metabolic engineering through the development of models and methods for simulation and optimization of microbial metabolism. Here we explore the relationship between two modeling frameworks in common use namely, dynamic models with kinetic rate laws and constraint-based flux models. We compare and analyze dynamic and constraint-based formulations of the same model of the central carbon metabolism of Escherichia coli. Our results show that, if unconstrained, the space of steady states described by both formulations is the same. However, the imposition of parameter-range constraints can be mapped into kinetically feasible regions of the solution space for the dynamic formulation that is not readily transferable to the constraint-based formulation. Therefore, with partial kinetic parameter knowledge, dynamic models can be used to generate constraints that reduce the solution space below that identified by constraint-based models, eliminating infeasible solutions and increasing the accuracy of simulation and optimization methods. |
| first_indexed | 2024-09-23T15:53:25Z |
| format | Article |
| id | mit-1721.1/101078 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:53:25Z |
| publishDate | 2016 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/1010782022-09-29T16:51:40Z Exploring the gap between dynamic and constraint-based models of metabolism Machado, Daniel Costa, Rafael S. Rocha, Isabel Tidor, Bruce Ferreira, Eugenio C. Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Biological Engineering Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Tidor, Bruce Systems biology provides new approaches for metabolic engineering through the development of models and methods for simulation and optimization of microbial metabolism. Here we explore the relationship between two modeling frameworks in common use namely, dynamic models with kinetic rate laws and constraint-based flux models. We compare and analyze dynamic and constraint-based formulations of the same model of the central carbon metabolism of Escherichia coli. Our results show that, if unconstrained, the space of steady states described by both formulations is the same. However, the imposition of parameter-range constraints can be mapped into kinetically feasible regions of the solution space for the dynamic formulation that is not readily transferable to the constraint-based formulation. Therefore, with partial kinetic parameter knowledge, dynamic models can be used to generate constraints that reduce the solution space below that identified by constraint-based models, eliminating infeasible solutions and increasing the accuracy of simulation and optimization methods. Fundacao para a Ciencia e a Tecnologia (PhD Grant SFRH/BD/35215/2007) Fundacao para a Ciencia e a Tecnologia (PhD Grant SFRH/BD/25506/2005) MIT-Portugal Program (MIT-Pt/BS-BB/0082/2008) 2016-02-03T15:34:48Z 2016-02-03T15:34:48Z 2012-01 2012-01 Article http://purl.org/eprint/type/JournalArticle 10967176 1096-7184 http://hdl.handle.net/1721.1/101078 Machado, Daniel, Rafael S. Costa, Eugenio C. Ferreira, Isabel Rocha, and Bruce Tidor. “Exploring the Gap Between Dynamic and Constraint-Based Models of Metabolism.” Metabolic Engineering 14, no. 2 (March 2012): 112–119. https://orcid.org/0000-0002-3320-3969 en_US http://dx.doi.org/10.1016/j.ymben.2012.01.003 Metabolic Engineering Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier PMC |
| spellingShingle | Machado, Daniel Costa, Rafael S. Rocha, Isabel Tidor, Bruce Ferreira, Eugenio C. Exploring the gap between dynamic and constraint-based models of metabolism |
| title | Exploring the gap between dynamic and constraint-based models of metabolism |
| title_full | Exploring the gap between dynamic and constraint-based models of metabolism |
| title_fullStr | Exploring the gap between dynamic and constraint-based models of metabolism |
| title_full_unstemmed | Exploring the gap between dynamic and constraint-based models of metabolism |
| title_short | Exploring the gap between dynamic and constraint-based models of metabolism |
| title_sort | exploring the gap between dynamic and constraint based models of metabolism |
| url | http://hdl.handle.net/1721.1/101078 https://orcid.org/0000-0002-3320-3969 |
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