The analysis of flux distributions in metabolic networks has become an important approach for understand-ing the fermentation characteristics of the process.A model of metabolic flux analysis of arachidonic acid(AA)sy...The analysis of flux distributions in metabolic networks has become an important approach for understand-ing the fermentation characteristics of the process.A model of metabolic flux analysis of arachidonic acid(AA)synthesis in Mortierella alpina ME-1 was established and carbon flux distributions were estimated in different fermentation phases with different concentrations of N-source.During the expo-nential,decelerating and stationary phase,carbon fluxes to AA were 3.28%,8.80%and 6.97%,respectively,with sufficient N-source broth based on the flux of glucose uptake,and those were increased to 3.95%,19.21%and 39.29%,respectively,by regulating the shifts of carbon fluxes via fermentation with limited N-source broth and adding 0.05% NaNO_(3) at 96 h.Eventually AA yield was increased from 1.3 to 3.5 g·L^(−1).These results suggest a way to improve AA fermentation,that is,fermentation with limited N-source broth and adding low concentration N-source during the stationary phase.展开更多
In silico approaches for metabolites optimization have been derived from the flood of sequenced and annotated genomes. However, there exist still numerous degrees of freedom in terms of optimization algorithm approach...In silico approaches for metabolites optimization have been derived from the flood of sequenced and annotated genomes. However, there exist still numerous degrees of freedom in terms of optimization algorithm approaches that can be exploited in order to enhance yield of processes which are based on biological reactions. Here, we propose an evolutionary approach aiming to suggest different mutant for augmenting ethanol yield using glycerol as substrate in Escherichia coli. We found that this algorithm, even though is far from providing the global optimum, is able to uncover genes that a global optimizer would be incapable of. By over-expressing accB, eno, dapE, and accA mutants in ethanol production was augmented up to 2 fold compared to its counterpart E. coli BW25113.展开更多
基金This work was supported by the National Natural Science Foundation of China(Grant No.20576054)Natural Science Foundation of Jiangsu(Grant No.BK2005114)Jiangsu Planned Projects for Postdoctoral Research Funds.
文摘The analysis of flux distributions in metabolic networks has become an important approach for understand-ing the fermentation characteristics of the process.A model of metabolic flux analysis of arachidonic acid(AA)synthesis in Mortierella alpina ME-1 was established and carbon flux distributions were estimated in different fermentation phases with different concentrations of N-source.During the expo-nential,decelerating and stationary phase,carbon fluxes to AA were 3.28%,8.80%and 6.97%,respectively,with sufficient N-source broth based on the flux of glucose uptake,and those were increased to 3.95%,19.21%and 39.29%,respectively,by regulating the shifts of carbon fluxes via fermentation with limited N-source broth and adding 0.05% NaNO_(3) at 96 h.Eventually AA yield was increased from 1.3 to 3.5 g·L^(−1).These results suggest a way to improve AA fermentation,that is,fermentation with limited N-source broth and adding low concentration N-source during the stationary phase.
基金the support of the National BioResource Project(NIG,Japan):E.coli Strain for kindly providing us with the Keio Collection using for our experimental sectionAlso this work is funded by Vicerrectoria de investigaciones at Universidad de los Andes.
文摘In silico approaches for metabolites optimization have been derived from the flood of sequenced and annotated genomes. However, there exist still numerous degrees of freedom in terms of optimization algorithm approaches that can be exploited in order to enhance yield of processes which are based on biological reactions. Here, we propose an evolutionary approach aiming to suggest different mutant for augmenting ethanol yield using glycerol as substrate in Escherichia coli. We found that this algorithm, even though is far from providing the global optimum, is able to uncover genes that a global optimizer would be incapable of. By over-expressing accB, eno, dapE, and accA mutants in ethanol production was augmented up to 2 fold compared to its counterpart E. coli BW25113.
