Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/11327

TitleEvolutionary approaches for strain optimization using dynamic models under a metabolic engineering perspective
Author(s)Evangelista, Pedro
Rocha, I.
Ferreira, E. C.
Rocha, Miguel
Issue date2009
PublisherSpringer Verlag
JournalLecture Notes in Computer Science
CitationPIZZUTI, Clara; RITCHIE, Marylyn D.; GIACOBINI, Mario, eds. – “Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics : proceedings of the 7th European Conference Evolutionary Computation… (EvoBIO 2009), Tübingen, Germany, 2009.” Berlin : Springer, 2009. ISBN 978-3-642-01183-2. p. 140-151.
Abstract(s)One of the purposes of Systems Biology is the quantitative modeling of biochemical networks. In this effort, the use of dynamical mathematical models provides for powerful tools in the prediction of the phenotypical behavior of microorganisms under distinct environmental conditions or subject to genetic modifications. The purpose of the present study is to explore a computational environment where dynamical models are used to support simulation and optimization tasks. These will be used to study the effects of two distinct types of modifications over metabolic models: deleting a few reactions (knockouts) and changing the values of reaction kinetic parameters. In the former case, we aim to reach an optimal knockout set, under a defined objective function. In the latter, the same objective function is used, but the aim is to optimize the values of certain enzymatic kinetic coefficients. In both cases, we seek for the best model modifications that might lead to a desired impact on the concentration of chemical species in a metabolic pathway. This concept was tested by trying to maximize the production of dihydroxyacetone phosphate, using Evolutionary Computation approaches. As a case study, the central carbon metabolism of Escherichia coli is considered. A dynamical model based on ordinary differential equations is used to perform the simulations. The results validate the main features of the approach.
TypeConference paper
URIhttp://hdl.handle.net/1822/11327
ISBN978-3-642-01183-2
DOI10.1007/978-3-642-01184-9_13
ISSN0302-9743
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CEB - Artigos em Livros de Atas / Papers in Proceedings


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