Natural computation meta-heuristics for the in silico optimization of microbial strains |
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| Authors: | Miguel Rocha Paulo Maia Rui Mendes José P Pinto Eugénio C Ferreira Jens Nielsen Kiran Raosaheb Patil Isabel Rocha |
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| Institution: | 1.Department of Informatics/CCTC,University of Minho,Braga,Portugal;2.IBB-Institute for Biotechnology and Bioengineering/Centre of Biological Engineering,Universidade do Minho,Braga,Portugal;3.Center for Microbial Biotechnology, Department of Systems Biology,Technical University of Denmark,Kgs. Lyngby,Denmark;4.Systems Biology, Dept. Chemical and Biological Engineering,Chalmers University of Technology,Gothenburg,Sweden |
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| Abstract: | Background One of the greatest challenges in Metabolic Engineering is to develop quantitative models and algorithms to identify a set
of genetic manipulations that will result in a microbial strain with a desirable metabolic phenotype which typically means
having a high yield/productivity. This challenge is not only due to the inherent complexity of the metabolic and regulatory
networks, but also to the lack of appropriate modelling and optimization tools. To this end, Evolutionary Algorithms (EAs)
have been proposed for in silico metabolic engineering, for example, to identify sets of gene deletions towards maximization of a desired physiological objective
function. In this approach, each mutant strain is evaluated by resorting to the simulation of its phenotype using the Flux-Balance
Analysis (FBA) approach, together with the premise that microorganisms have maximized their growth along natural evolution. |
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