Abstract
BackgroundMetabolic engineering design methodology has evolved from using pathway-centric, random and empirical-based methods to using systems-wide, rational and integrated computational and experimental approaches. Persistent during these advances has been the desire to develop design strategies that address multiple simultaneous engineering goals, such as maximizing productivity, while minimizing raw material costs.ResultsHere, we use constraint-based modeling to systematically design multiple combinations of medium compositions and gene-deletion strains for three microorganisms (Escherichia coli, Saccharomyces cerevisiae, and Shewanella oneidensis) and six industrially important byproducts (acetate, D-lactate, hydrogen, ethanol, formate, and succinate). We evaluated over 435 million simulated conditions and 36 engineering metabolic traits, including product rates, costs, yields and purity.ConclusionsThe resulting metabolic phenotypes can be classified into dominant clusters (meta-phenotypes) for each organism. These meta-phenotypes illustrate global phenotypic variation and sensitivities, trade-offs associated with multiple engineering goals, and fundamental differences in organism-specific capabilities. Given the increasing number of sequenced genomes and corresponding stoichiometric models, we envisage that the proposed strategy could be extended to address a growing range of biological questions and engineering applications.
Highlights
Metabolic engineering design methodology has evolved from using pathway-centric, random and empirical-based methods to using systems-wide, rational and integrated computational and experimental approaches
Any biochemical reaction network and synthesized target metabolite can be incorporated into our methodology, we focus on three microorganisms (E. coli, S. cerevisiae and S. oneidensis) and six target metabolite by-products of industrial interest: acetate [27], ethanol [28,29], formate [30], hydrogen [31], D-lactate [27,32], and succinate [33,34,35]
Additional file 1: Table S1 lists the model attributes and perturbations associated with the three genome-scale metabolic models used in this study: E. coli, S. cerevisiae, and S. oneidensis
Summary
Metabolic engineering design methodology has evolved from using pathway-centric, random and empirical-based methods to using systems-wide, rational and integrated computational and experimental approaches. Persistent during these advances has been the desire to develop design strategies that address multiple simultaneous engineering goals, such as maximizing productivity, while minimizing raw material costs. Declining oil reserves, rising oil prices, and growing environmental concerns have prompted renewed interest in producing chemicals using microorganisms instead of fossil fuels [1]. Classical metabolic engineering methods use localized metabolic intuition and random mutagenesis screening to develop microbial strains that possess improved biochemical production capabilities. Escherichia coli does not naturally produce succinic acid as a major
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