Designing metabolic engineering strategies with genome-scale metabolic flux modeling

Abstract

New in silico tools that make use of genome-scale metabolic flux modeling are improving the design of metabolic engineering strategies. This review highlights the latest developments in this area, explains the interface between these in silico tools and the experimental implementation tools of metabolic engineers, and provides a way forward so that in silico predictions can better mimic reality and more experimental methods can be considered in simulation studies. The several methodologies for solving genome-scale models (eg, flux balance analysis [FBA], parsimonious FBA, flux variability analysis, and minimization of metabolic adjustment) all have unique advantages and applications. There are two basic approaches to designing metabolic engineering strategies in silico, and both have demonstrated success in the literature. The first involves: 1) making a genetic manipulation in a model; 2) testing for improved performance through simulation; and 3) iterating the process. The second approach has been used in more recently designed in silico tools and involves: 1) comparing metabolic flux profiles of a wild-type and ideally engineered state and 2) designing engineering strategies based on the differences in these flux profiles. Improvements in genome-scale modeling are anticipated in areas such as the inclusion of all relevant cellular machinery, the ability to understand and anticipate the results of combinatorial enrichment experiments, and constructing dynamic and flexible biomass equations that can respond to environmental and genetic manipulations. A brief introduction to genome-scale metabolic flux modelingA "genome-scale" metabolic flux model (GEM) consists of a network of biochemical reactions that is reconstructed based on the genomic sequence and annotation of a cell. Assuming a "steady-state" metabolism (ie, a snapshot of metabolism at one time point) is reached on a short time-scale, these reactions can be represented by a linear system of equations. Then, problems such as maximizing specific chemical production or growth can be solved efficiently by linear programming. GEMs and their uses have been reviewed thoroughly, and they are most basically used to predict reaction flux, which is the overall rate of metabolite conversion. 1,2 Often, laboratory measurements including the rates of substrate consumption, product formation, and growth are used as model constraints so calculations coincide with observations. Other model constraints can be derived from reaction thermodynamics, 3 cellular regulatory networks, 4 and -omics datasets. 5 GEMs have been constructed and utilized for intensively

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Advances in the Discovery and Engineering of Gene Targets for Carotenoid Biosynthesis in Recombinant Strains

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