چكيده به لاتين
In recent decades, performance of biosystems has been enhanced using metabolic models which provide the details of cellular metabolism. In this study, iJO1366 genome-scale metabolic network including1366 genes, 2251 metabolic reactions, and 1136 unique metaboliteswas used to model growth and product formation of Escherichia coli (a gram-negative, facultative anaerobic bacteria capable of producing a diverse range of products as model microorganism) using dynamic metabolic flux analysis (dMFA). Previous experimental data with glucose and/or acetate as carbon sources obtained under aerobic and/or anaerobic conditions were used for dMFA. Model was solved using MATLAB software, the Systems Biology Markup Language (SBML) and also a set of Cobra Toolbox and GLPK (GNU Linear Programming Kit). Computer experiments were performed to obtain the time courses of growth and product formationfor wild-type E.coli and mutants of acs, ptaorldhA genedeletion based on dMFA and to compare the estimated and experimental results. In some cases, conventional static MFA was also applied to check the model validity. Results showed that despite the differences observed between the time courses of experimental and predicted concentrations of biomass and products, the trends of concentrations were in good agreement with experimental results. Using glucose compared to acetate as carbon source in all cases resulted in higher concentrations. Higher glucose consumption and hence cellular growth were obtained under aerobic compared to anaerobic condition. Formation of ethanol, formate, and acetate was predicted as products in all computer experiments while succinate formation was only observed under anaerobic condition, which is consistent with experimental results. Deletions of acs and pta genes adversely affected aerobic cell growth on both glucose and acetate while it had insignificant effect on the time of maximum biomass concentration. Successful predictions of trends of biomass and product formation with time using dMFA can have generic application for prediction of dynamic behavior of other bacteria.
Keywords: Dynamic metabolic flux analysis (dMFA), Genome-scale metabolic models, Gene deletion, Escherichia coli.
