PLoS ONE · 2011 · 96 citations · 44 references
EngineeringBioengineeringBiological EngineeringMetabolic EngineeringMetabolismMicrobial MetabolismGenome-scale Metabolic ModelingMetabolic Pathway EngineeringMicrobial Metabolic EngineeringStrain EngineeringStrain ImprovementSystems Metabolic EngineeringComputational Strain DesignComputational Strain OptimizationGene DeletionMixed-integer ProgrammingStrain Design OptimizationStrain OptimizationSystems Metabolic ModelingSystems-level Modeling
Computational models in metabolic engineering are increasingly used, but identifying large‑scale perturbation strategies remains limited by the size of genome‑scale models and combinatorial complexity. This study introduces two new bi‑level strain design approaches based on mixed‑integer programming and general solution techniques to enhance design performance. SimOptStrain simultaneously optimizes gene deletions and non‑native reaction additions, while BiMOMA applies minimization of metabolic adjustment to predict knockout behavior within a MIP‑based bi‑level framework for the first time. The proposed solution techniques cut CPU times from about ten days to five minutes for four‑gene deletion strategies, uncovered production routes for malate and serine that were previously inaccessible, and yielded higher‑yield designs for succinate, glycerol, pyruvate, and glutamate that other sequential or heuristic methods could not find, thereby expanding the scope of strain design.
The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering.
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Adam M. Feist, Christopher S. Henry, Jennifer L. Reed et al. · Molecular Systems Biology · 2007 · 1.4K citations · Full text