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 genomespecific 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.bioinformatics ͉ metabolism ͉ genotype-phenotype relation ͉ flux balance analysis T he complete genome sequence for a number of microorganisms has been established (The Institute for Genomic Research at www.tigr.org). The genome sequencing efforts and the subsequent bioinformatic analyses have defined the molecular ''parts catalogue'' for a number of living organisms. However, it is evident that cellular functions are multigeneic in nature, thus one must go beyond a molecular parts catalogue to elucidate integrated cellular functions based on the molecular cellular components (1). Therefore, to analyze the properties and the behavior of complex cellular networks, one needs to use methods that focus on the systemic properties of the network. Approaches to analyze, interpret, and ultimately predict cellular behavior based on genomic and biochemical data likely will involve bioinformatics and computational biology and form the basis for subsequent bioengineering analysis.In moving toward the goal of developing an integrated description of cellular processes, it should be recognized that there exists a history of studying the systemic properties of metabolic networks (2) and many mathematical methods have been developed to carry out such studies. These methods include approaches such as metabolic control analysis (3, 4), flux balance analysis (FBA) (5-7), metabolic pathway analysis (8-11, 69), cybernetic modeling (12), biochemical systems theory (13), temporal decomposition (14), and so on. Although many mathematical methods and approaches have been developed, there are few comprehensive metabolic systems for which detailed kinetic information is available and where such detailed analysis can be carried out (see refs. 15-17 for a few noteworthy exceptions).To analyze, interpret, and predict cellular behavior, each individual step in a biochemical network must be described, normally with a rate equati...

Flux balance analysis: A geometric perspective

“…This method allows us to identify the optimal path through the network in order to achieve a particular objective; quantitative predictions will then hold true if the cell optimizes its growth under the conditions considered. When applying LP to predict flux distributions it is assumed that the cell has found an ''optimal solution'' for survival through natural selection, where we equate survival with growth (Edwards and Palsson, 2000).…”

“…The genome sequencing projects are defining the molecular components within the cell, and describing the integrated function of these molecular components will be a challenging task (Edwards and Palsson, 2000). Ideally, one would like to use kinetic modelling to characterize fully the mechanics of each enzymatic reaction, in terms of how changes in metabolite concentrations affect local reaction rates.…”

The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

“…2), has culminated in a network containing a total number of 1,260 ORF metabolic functions [12][13][14][15][16][17][18][19] . This reconstruction represents 48% of the total experimentally annotated ORF functions in the E. coli genome (Table 1).…”

“…Shown are six milestone efforts contributing to the reconstruction of the E. coli metabolic network. For each of the six reconstructions [12][13][14][15][16][17][18][19] , the number of included reactions (blue diamonds), genes (green triangles) and metabolites (purple squares) are displayed. Also listed are noteworthy properties that each successive reconstruction provided over previous efforts.…”

“…The reconstruction of the E. coli metabolic network represents the best-developed genomescale network to date and it has proven to be a platform for a variety of computational analyses. Three successive E. coli GEMs [17][18][19] have been used as the basis for over 60 detailed studies reviewed below. …”