Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models
Pozo Fernández, Carlos
Guillén Gosálbez, Gonzalo
Jiménez Esteller, Laureano
MetadataShow full item record
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.
Is part ofBMC Systems Biology, 2011, vol. 5, núm. 137, p. 1-12
The following license files are associated with this item:
Showing items related by title, author, creator and subject.
Identifying the preferred subset of enzymatic profiles in nonlinear kinetic metabolic models via multiobjective global optimization and pareto filters Pozo Fernández, Carlos; Guillén Gosálbez, Gonzalo; Sorribas Tello, Albert; Jiménez Esteller, Laureano (Public Library of Science (PLoS), 2012)Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear ...
Identification of regulatory structure and kinetic parameters of biochemical networks via mixed-integer dynamic optimization Guillén Gosálbez, Gonzalo; Miró, Antoni; Alves, Rui; Sorribas Tello, Albert; Jiménez Esteller, Laureano (BioMed Central, 2013)Background: Recovering the network topology and associated kinetic parameter values from time-series data are central topics in systems biology. Nevertheless, methods that simultaneously do both are few and lack ...
On the use of filters to facilitate the post-optimal analysis of the Pareto solutions in multi-objective optimization Antipova, Ekaterina; Pozo, C.; Guillén Gosálbez, Gonzalo; Boer, Dieter; Cabeza, Luisa F.; Jiménez Esteller, Laureano (Elsevier, 2015)Multi-objective optimization (MOO) has emerged recently as a useful technique in the design andplanning of engineering systems because it allows identifying alternatives leading to significant envi-ronmental savings. MOO ...