Evolutionary Approaches for Strain Optimization Using Dynamic Models under a Metabolic Engineering Perspective

Authors:
Pedro Evangelista;Isabel Rocha;Eugénio C. Ferreira;Miguel Rocha
Affiliations:
Departament of Informatics / CCTC, University of Minho, Braga, Portugal 4710-057 and IBB - Institute for Biotechnology and Bioengineering Center of Biological Engineering, University of Minho, Bra ...;IBB - Institute for Biotechnology and Bioengineering Center of Biological Engineering, University of Minho, Braga, Portugal 4710-057;IBB - Institute for Biotechnology and Bioengineering Center of Biological Engineering, University of Minho, Braga, Portugal 4710-057;Departament of Informatics / CCTC, University of Minho, Braga, Portugal 4710-057
Venue:
EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Year:
2009

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Abstract

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.