Dataset Information


Raghunathan2009 - Genome-scale metabolic network of Salmonella typhimurium (iRR1083)

ABSTRACT: Raghunathan2009 - Genome-scale metabolic network of Salmonella typhimurium (iRR1083) This model is described in the article: Constraint-based analysis of metabolic capacity of Salmonella typhimurium during host-pathogen interaction. Raghunathan A, Reed J, Shin S, Palsson B, Daefler S. BMC Syst Biol 2009; 3: 38 Abstract: BACKGROUND: Infections with Salmonella cause significant morbidity and mortality worldwide. Replication of Salmonella typhimurium inside its host cell is a model system for studying the pathogenesis of intracellular bacterial infections. Genome-scale modeling of bacterial metabolic networks provides a powerful tool to identify and analyze pathways required for successful intracellular replication during host-pathogen interaction. RESULTS: We have developed and validated a genome-scale metabolic network of Salmonella typhimurium LT2 (iRR1083). This model accounts for 1,083 genes that encode proteins catalyzing 1,087 unique metabolic and transport reactions in the bacterium. We employed flux balance analysis and in silico gene essentiality analysis to investigate growth under a wide range of conditions that mimic in vitro and host cell environments. Gene expression profiling of S. typhimurium isolated from macrophage cell lines was used to constrain the model to predict metabolic pathways that are likely to be operational during infection. CONCLUSION: Our analysis suggests that there is a robust minimal set of metabolic pathways that is required for successful replication of Salmonella inside the host cell. This model also serves as platform for the integration of high-throughput data. Its computational power allows identification of networked metabolic pathways and generation of hypotheses about metabolism during infection, which might be used for the rational design of novel antibiotics or vaccine strains. This model is hosted on BioModels Database and identified by: MODEL1507180058. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

SUBMITTER: Nicolas Le Novère  

PROVIDER: MODEL1507180058 | BioModels | 2015-07-30


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