Evolutionary programming as a platform for in silico metabolic engineering

By Kiran Raosaheb Patil, Isabel Rocha, Jochen Förster, Jens Nielsen. BMC Bioinformatics, 2005

475 citations recorded by the source corpus.

BackgroundThrough genetic engineering it is possible to introduce targeted genetic changes and hereby engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, owing to the complexity of metabolic networks, both in terms of structure and regulation, it is often difficult to predict the effects of genetic modifications on the resulting phenotype. Recently genome-scale metabolic models have been compiled for several different microorganisms where structural and stoichiometric complexity is inherently accounted for. New algorithms are being developed by using genome-scale metabolic models that enable identification of gene knockout strategies for obtaining improved phenotypes. However, the problem of finding optimal gene deletion strategy is combinatorial and consequently the computational time increases exponentially with the size of the problem, and it is therefore interesting to develop new faster algorithms.ResultsIn this study we report an evolutionary programming based method to rapidly identify gene deletion strategies for optimization of a desired phenotypic objective function. We illustrate the proposed method for two important design parameters in industrial fermentations, one linear and other non-linear, by using a genome-scale model of the yeast Saccharomyces cerevisiae. Potential metabolic engineering targets for improved production of succinic acid, glycerol and vanillin are identified and underlying flux changes for the predicted mutants are discussed.ConclusionWe show that evolutionary programming enables solving large gene knockout problems in relatively short computational time. The proposed algorithm also allows the optimization of non-linear objective functions or incorporation of non-linear constraints and additionally provides a family of close to optimal solutions. The identified metabolic engineering strategies suggest that non-intuitive genetic modifications span several different pathways and may be necessary for solving challenging metabolic engineering problems.

Subfields: Biomedical Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d3d8dbe32308ae598081d374565862f002ed86b4

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Citations in the corpus

Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints (2017, background): “This indicates that classical metabolic engineering approaches are well suited to increase succinate production, such as knocking out genes and coupling production to growth (Patil et al, 2005; Raab et al, 2010).”
OptForce: An Optimization Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions (2010, methodology): “Evolutionary search procedures for solving the resulting combinatorial optimization problems were explored in OptGene [24] and applied for the production of succinic acid, glycerol and vanillin in yeast.”
Genome-scale models of bacterial metabolism: reconstruction and applications (2008, methodology): “Optimization methods based on genetic (Patil et al ., 2005) or linear programming (Burgard et al ., 2003) algorithms were introduced to circumvent this issue.”
Synthetic biology - putting engineering into biology (2006, background): “This is imperative as it was shown that stochasticity in combination with certain system architectures can result in different system states (Kærn et al ., 2005; Pedraza and van Oudenaarden, 2005).”
Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods (2012, methodology): “However, methods such as OptGene 71 and genetic design through local search (GDLS) 72 provide a more realistic view by modelling genetic modifications, as some genes catalyse multiple reactions and some reactions are spontaneous. μ max , predicted maximum growth rate.”
OptFlux: an open-source software platform for in silico metabolic engineering (2010, methodology): “The OptKnock algorithm [7] and two meta-heuristic optimization methods, EAs and SA [8], are currently available.”