Comparative analysis of metabolic network of pathogens
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Metabolic networks are complex and system of highly connected chemical reactions and hence it needs a system level computational approach to identify the genotype- phenotype relationship. The study of essential genes and reactions and synthetic lethality of genes and reactions plays a crucial role in explaining functional links between genes and gene function predictions.
Flux balance analysis (FBA) has been developed as a powerful method for the in silico analyses of metabolic networks. In this study, we present the comparative analysis of the genomic scale metabolic networks of the four microorganisms i.e. Salmonella typhimurium, Mycobacterium tuberculosis, Staphylococcus aureus, and Helicobacter pylori. The fluxes of all reaction were obtained and the growth rate of the organism was calculated by setting the biomass reaction as the objective function.
Results & Conclusions
The average lethality fraction of all the four organisms studied ranged from 0.2 to 0.6. It was also observed that there are very few metabolites which are highly connected. Those metabolites that are highly connected are supposed to be the ‘global players’ similar to the hub protein in the protein–protein interaction network.
Keywordsessential genes synthetic lethal genes metabolite connectivity robustness analysis
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- Feist and Palsson (2008). The growing scope of applications of genomescale metabolic reconstructions using Escherichia coli. Nature Biotechnol, 226(6): 659–667Google Scholar
- Schellenberger J, Que R, Fleming R M, Thiele I, Orth J D, Feist A M, Zielinski D C, Bordbar A, Lewis N E, Rahmanian S, Kang J, Hyduke D R, Palsson B Ø (2011). Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc, 6(9): 1290–1307CrossRefPubMedPubMedCentralGoogle Scholar
- McClelland M, Sanderson K E, Spieth J, Clifton S W, Latreille P, Courtney L, Porwollik S, Ali J, Dante M, Du F, Hou S, Layman D, Leonard S, Nguyen C, Scott K, Holmes A, Grewal N, Mulvaney E, Ryan E, Sun H, Florea L, Miller W, Stoneking T, Nhan M, Waterston R, Wilson R K (2001). Complete genome sequence of Salmonella enterica serovar Typhimurium LT2. Nature, 413(6858): 852–856CrossRefPubMedGoogle Scholar
- Thiele I, Hyduke D R, Steeb B, Fankam G, Allen D K, Bazzani S, Charusanti P, Chen F C, Fleming R M, Hsiung C A, De Keersmaecker S C, Liao Y C, Marchal K, Mo M L, Özdemir E, Raghunathan A, Reed J L, Shin S I, Sigurbjörnsdóttir S, Steinmann J, Sudarsan S, Swainston N, Thijs I M, Zengler K, Palsson B O, Adkins J N, Bumann D (2011). A community effort towards a knowledgebase and mathematical model of the human pathogen Salmonella Typhimurium LT2. BMC Syst Biol, 5: 8CrossRefPubMedPubMedCentralGoogle Scholar