Analytical Reduction of Nonlinear Metabolic Networks Accounting for Dynamics in Enzymatic Reactions
Metabolic modeling has been particularly efficient to understand the conditions affecting the metabolism of an organism. But so far, metabolic models have mainly considered static situations, assuming balanced growth. Some organisms are always far from equilibrium, and metabolic modeling must accoun...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/2342650 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850165672436301824 |
|---|---|
| author | Claudia López Zazueta Olivier Bernard Jean-Luc Gouzé |
| author_facet | Claudia López Zazueta Olivier Bernard Jean-Luc Gouzé |
| author_sort | Claudia López Zazueta |
| collection | DOAJ |
| description | Metabolic modeling has been particularly efficient to understand the conditions affecting the metabolism of an organism. But so far, metabolic models have mainly considered static situations, assuming balanced growth. Some organisms are always far from equilibrium, and metabolic modeling must account for their dynamics. This leads to high-dimensional models in which metabolic fluxes are no more constant but vary depending on the intracellular concentrations. Such metabolic models must be reduced and simplified so that they can be calibrated and analyzed. Reducing these models of large dimension down to a model of smaller dimension is very challenging, specially, when dealing with nonlinear metabolic rates. Here, we propose a rigorous approach to reduce metabolic models using quasi-steady-state reduction based on Tikhonov’s theorem, with a characterized and bounded reduction error. We assume that the metabolic network can be represented with Michaelis-Menten enzymatic reactions that evolve at different time scales. In this simplest approach, some metabolites can accumulate. We consider the case with a continuous varying input in the model, such as light for microalgae, so that the system is never at a steady state. Furthermore, our analysis proves that metabolites in the slow part of the metabolic system reach higher concentrations (by one order of magnitude) than metabolites in the fast part under some flux conditions. A simple example illustrates our approach and the resulting accuracy of the reduction method. |
| format | Article |
| id | doaj-art-ee8132482de34a5fb9f225adebae4ba2 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ee8132482de34a5fb9f225adebae4ba22025-08-20T02:21:41ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/23426502342650Analytical Reduction of Nonlinear Metabolic Networks Accounting for Dynamics in Enzymatic ReactionsClaudia López Zazueta0Olivier Bernard1Jean-Luc Gouzé2Université Côte d’Azur, Inria, INRA, CNRS, UPMC Univ Paris 06, Biocore Team, Sophia Antipolis, FranceUniversité Côte d’Azur, Inria, INRA, CNRS, UPMC Univ Paris 06, Biocore Team, Sophia Antipolis, FranceUniversité Côte d’Azur, Inria, INRA, CNRS, UPMC Univ Paris 06, Biocore Team, Sophia Antipolis, FranceMetabolic modeling has been particularly efficient to understand the conditions affecting the metabolism of an organism. But so far, metabolic models have mainly considered static situations, assuming balanced growth. Some organisms are always far from equilibrium, and metabolic modeling must account for their dynamics. This leads to high-dimensional models in which metabolic fluxes are no more constant but vary depending on the intracellular concentrations. Such metabolic models must be reduced and simplified so that they can be calibrated and analyzed. Reducing these models of large dimension down to a model of smaller dimension is very challenging, specially, when dealing with nonlinear metabolic rates. Here, we propose a rigorous approach to reduce metabolic models using quasi-steady-state reduction based on Tikhonov’s theorem, with a characterized and bounded reduction error. We assume that the metabolic network can be represented with Michaelis-Menten enzymatic reactions that evolve at different time scales. In this simplest approach, some metabolites can accumulate. We consider the case with a continuous varying input in the model, such as light for microalgae, so that the system is never at a steady state. Furthermore, our analysis proves that metabolites in the slow part of the metabolic system reach higher concentrations (by one order of magnitude) than metabolites in the fast part under some flux conditions. A simple example illustrates our approach and the resulting accuracy of the reduction method.http://dx.doi.org/10.1155/2018/2342650 |
| spellingShingle | Claudia López Zazueta Olivier Bernard Jean-Luc Gouzé Analytical Reduction of Nonlinear Metabolic Networks Accounting for Dynamics in Enzymatic Reactions Complexity |
| title | Analytical Reduction of Nonlinear Metabolic Networks Accounting for Dynamics in Enzymatic Reactions |
| title_full | Analytical Reduction of Nonlinear Metabolic Networks Accounting for Dynamics in Enzymatic Reactions |
| title_fullStr | Analytical Reduction of Nonlinear Metabolic Networks Accounting for Dynamics in Enzymatic Reactions |
| title_full_unstemmed | Analytical Reduction of Nonlinear Metabolic Networks Accounting for Dynamics in Enzymatic Reactions |
| title_short | Analytical Reduction of Nonlinear Metabolic Networks Accounting for Dynamics in Enzymatic Reactions |
| title_sort | analytical reduction of nonlinear metabolic networks accounting for dynamics in enzymatic reactions |
| url | http://dx.doi.org/10.1155/2018/2342650 |
| work_keys_str_mv | AT claudialopezzazueta analyticalreductionofnonlinearmetabolicnetworksaccountingfordynamicsinenzymaticreactions AT olivierbernard analyticalreductionofnonlinearmetabolicnetworksaccountingfordynamicsinenzymaticreactions AT jeanlucgouze analyticalreductionofnonlinearmetabolicnetworksaccountingfordynamicsinenzymaticreactions |