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Title:
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Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models
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Author:
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Pozo Fernández, Carlos; Marín-Sanguino, Alberto; Alves, Rui; Guillén Gosálbez, Gonzalo; Jiménez Esteller, Laureano; Sorribas Tello, Albert
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Notes:
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Background: Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As
appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA) models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization. Results: Based on the GMA canonical representation, we have developed in previous works a highly efficient
optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is
straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC) models that extend the power-law formalism to deal with saturation and cooperativity. Conclusions: Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task.
AS is funded by MICINN (Spain) (BFU2008-0196). RA is partially supported by MICINN (Spain) through Grants BFU2007-62772/BMC and BFU2010-17704). AS and RA are members of the 2009SGR809 research group of the Generalitat de Catalunya. GG-G and CP acknowledges support from the Spanish Ministry of Science and Innovation (Projects DPI2008-04099 and CTQ2009-14420-C02-01) and the Spanish Ministry of External Affairs and Cooperation (Projects A/023551/09 and A/031707/10). |
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Subject(s):
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-Metabolisme cel·lular
-Sistemes biològics -- Models matemàtics
-Equacions diferencials |
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Rights:
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cc-by, (c) Pozo et al., 2011
http://creativecommons.org/licenses/by/2.0/es/deed.ca
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Document type:
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article
publishedVersion |
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Published by:
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BioMed Central
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