The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities

Abstract

The Escherichia coli MG1655 genome has been completely sequenced. The annotated sequence, biochemical information, and other information were used to reconstruct the E. coli metabolic map. The stoichiometric coefficients for each metabolic enzyme in the E. coli metabolic map were assembled to construct a genome-specific stoichiometric matrix. The E. coli stoichiometric matrix was used to define the system's characteristics and the capabilities of E. coli metabolism. The effects of gene deletions in the central metabolic pathways on the ability of the in silico metabolic network to support growth were assessed, and the in silico predictions were compared with experimental observations. It was shown that based on stoichiometric and capacity constraints the in silico analysis was able to qualitatively predict the growth potential of mutant strains in 86% of the cases examined. Herein, it is demonstrated that the synthesis of in silico metabolic genotypes based on genomic, biochemical, and strain-specific information is possible, and that systems analysis methods are available to analyze and interpret the metabolic phenotype.

Figure 1

The feasible solution set for a hypothetical metabolic reaction network. (A) The steady-state operation of the metabolic network is restricted to the region within a cone, defined as the feasible set (8). The feasible set contains all flux vectors that satisfy the physicochemical constrains (Eqs. 1 and 2). Thus, the feasible set defines the capabilities of the metabolic network. All feasible metabolic flux distributions lie within the feasible set, and (B) in the limiting case, where all constraints on the metabolic network are known, such as the enzyme kinetics and gene regulation, the feasible set may be reduced to a single point. This single point must lie within the feasible set.

Figure 2

Gene deletions in E. coli MG1655 central intermediary metabolism; maximal biomass yields on glucose for all possible single gene deletions in the central metabolic pathways. The optimal value of the mutant objective function (Z_mutant) compared with the “wild-type” objective function (Z), where Z is defined in Eq. 3. The ratio of optimal growth yields (Z_mutant/Z). The results were generated in a simulated aerobic environment with glucose as the carbon source. The transport fluxes were constrained as follows: β_glucose = 10 mmol/g-dry weight (DW) per h; β_oxygen = 15 mmol/g-DW per h. The maximal yields were calculated by using FBA with the objective of maximizing growth. The biomass yields are normalized with respect to the results for the full metabolic genotype. The yellow bars represent gene deletions that reduced the maximal biomass yield to less than 95% of the in silico wild type.

Figure 3

Rerouting of metabolic fluxes. (Black) Flux distribution for the complete gene set. (Red) zwf^- mutant. Biomass yield is 99% of the results for the full metabolic genotype. (Blue) zwf^- pnt^- mutant. Biomass yield is 92% of the results for the full metabolic genotype (see text). The solid lines represent enzymes that are being used, with the corresponding flux value noted. The fluxes [substrates converted/h per g-dry weight (DW)] were calculated by using FBA with the input parameters of glucose uptake rate (β_glucose = 6.6 mmol glucose/h per g-DW) and oxygen uptake rate (β_oxygen = 12.4 mmol oxygen/h per g-DW) (41).