Genome-Scale Metabolic Model of Helicobacter pylori 26695

Abstract

A genome-scale metabolic model of Helicobacter pylori 26695 was constructed from genome sequence annotation, biochemical, and physiological data. This represents an in silico model largely derived from genomic information for an organism for which there is substantially less biochemical information available relative to previously modeled organisms such as Escherichia coli. The reconstructed metabolic network contains 388 enzymatic and transport reactions and accounts for 291 open reading frames. Within the paradigm of constraint-based modeling, extreme-pathway analysis and flux balance analysis were used to explore the metabolic capabilities of the in silico model. General network properties were analyzed and compared to similar results previously generated for Haemophilus influenzae. A minimal medium required by the model to generate required biomass constituents was calculated, indicating the requirement of eight amino acids, six of which correspond to essential human amino acids. In addition a list of potential substrates capable of fulfilling the bulk carbon requirements of H. pylori were identified. A deletion study was performed wherein reactions and associated genes in central metabolism were deleted and their effects were simulated under a variety of substrate availability conditions, yielding a number of reactions that are deemed essential. Deletion results were compared to recently published in vitro essentiality determinations for 17 genes. The in silico model accurately predicted 10 of 17 deletion cases, with partial support for additional cases. Collectively, the results presented herein suggest an effective strategy of combining in silico modeling with experimental technologies to enhance biological discovery for less characterized organisms and their genomes.Genomics has reshaped many approaches to studying the biology of an organism. It has made possible studies in comparative genomics (1, 2, 17, 34), genome-wide transcriptional profiling (16), and a host of integrated analyses aimed at understanding the systems biology of organisms (14,25,68).For model organisms such as Escherichia coli and Saccharomyces cerevisiae genome sequencing has generated a wealth of insight into the physiology of these microbes. However, the availability of extensive biological information and literature regarding these organisms has undoubtedly made it easier to reconcile data against the genome and interpret many characteristic properties of the genome and its genetic contents.To date most of the common model organisms have had their genomes sequenced and current sequencing efforts are naturally moving on to less-well characterized organisms. This begs the question: what can be learned about less characterized organisms from their genomic sequence in the relative absence of extensive previous laboratory research? Additionally, how can the genome be utilized to assist in guiding future research and discovery into the particular organism of interest?Herein we explore one aspect of these questions as it relates to metab...

OptStrain: A computational framework for redesign of microbial production systems

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“…These models, already available for Helicobacter pylori (Schilling et al 2002), Escherichia coli (Edwards and Palsson 2000;Reed et al 2003), Saccharomyces cerevisiae (Forster et al 2003), and other microorganisms (Van Dien and Lidstrom 2002;David et al 2003;Valdes et al 2003), provide successively refined abstractions of the microbial metabolic capabilities. An automated process to expedite the construction of stoichiometric models from annotated genomes (Segre et al 2003) promises further to accelerate the metabolic reconstructions of several microbial organisms.…”

Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network

“…The constraint-based approach (12)(13)(14)(15) has been productively used to study the properties of genome-scale reconstructions of bacterial metabolic networks including Haemophilus influenzae (24), Escherichia coli (25), and Helicobacter pylori (26). Here we apply this approach to the recently reconstructed genomescale S. cerevisiae metabolic network (8) to compute the prop- Fig.…”

Integrated Energy and Flux Balance Based Multiobjective Framework for Large-Scale Metabolic Networks

“…The quantification of intracellular metabolic fluxes is widely used for investigation of the metabolism in mioorganisms 9,10,[17][18][19][20][21] and mammalian systems. 1,[5][6][7][8][11][12][13][14]24,25 Flux balance analysis (FBA) uses stoichiometric and mass balance constraints to compute the intracellular fluxes.…”