چكيده به لاتين
In recent decades, development of functioning of biological systems has become possible using computational algorithms for analyzing and predicting the behavior of metabolic models. Using OptKnock (a gene deletion algorithm), growth alongside product formation for E. coli as a model microorganism were simultaneously optimized using iJO1366 genome scale model, including 95 reactions, 72 metabolites and 137 genes. Flux balance analysis was firstly applied to investigate the effect of various sources of carbon and nitrogen as well as oxygen limitation on growth. It was shown that glucose and ammonium with unlimited oxygen resulted in maximum growth. Additionally, 10 genes were identified as essential, i.e. their single or joint (with any other gene) deletion is lethal to growth. Different strategies for gene deletion (two-four genes) were subsequently proposed by applying OptKnock to separately optimize formation of lactate, ethanol and succinate as products. Formation of lactate with deletion of 4 genes (GLUN, ME1, PDH and PTAr) resulted in 98.7% of its maximum theoretical production. For ethanol, removing 3 genes led to 92.4% formation. Succinate reached 64.67% of its maximum value when 3 genes (PFL, PYK and TKT2) were deleted. Our results are almost in agreement with experimental results previously reported.
Keywords: Genome Scale Metabolic Model, Flux Balance Analysis (FBA), Gene Deletion, Product Production, OptKnock Algorithm, Escherichia coli (E. coli)
