In silico strain optimization by adding reactions to metabolic models

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doi doi:10.2390/biecoll-jib-2012-202
submission July 02, 2012
published July 24, 2012
NCBI PubMed PubMed ID 22829573

Sara Correia and Miguel Rocha

Correspondence should be addressed to:
Miguel Rocha
CCTC, University of Minho, Campus de Gualtar, Braga, Portugal
tp.ohnimu.id@nullahcorm


Abstract

Nowadays, the concerns about the environment and the needs to increase the productivity at low costs, demand for the search of new ways to produce compounds with industrial interest. Based on the increasing knowledge of biological processes, through genome sequencing projects, and high-throughput experimental techniques as well as the available computational tools, the use of microorganisms has been considered as an approach to produce desirable compounds. However, this usually requires to manipulate these organisms by genetic engineering and/ or changing the enviromental conditions to make the production of these compounds possible. In many cases, it is necessary to enrich the genetic material of those microbes with hereologous pathways from other species and consequently adding the potential to produce novel compounds. This paper introduces a new plug-in for the OptFlux Metabolic Engineering platform, aimed at finding suitable sets of reactions to add to the genomes of selected microbes (wild type strain), as well as finding complementary sets of deletions, so that the mutant becomes able to overproduce compounds with industrial interest, while preserving their viability. The necessity of adding reactions to the metabolic model arises from existing gaps in the original model or motivated by the productions of new compounds by the organism. The optimization methods used are metaheuristics such as Evolutionary Algorithms and Simulated Annealing. The usefulness of this plug-in is demonstrated by a case study, regarding the production of vanillin by the bacterium E. coli.

Reference

Sara Correia and Miguel Rocha. In silico strain optimization by adding reactions to metabolic models. Journal of Integrative Bioinformatics, 9(3):202, 2012. Online Journal: http://journal.imbio.de/index.php?paper_id=202
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