Stoichiometry-based analyses of meta- bolic networks have aroused significant interest of systems biology researchers in recent years. It is necessary to develop a more convenient modeling platform on which users can ...Stoichiometry-based analyses of meta- bolic networks have aroused significant interest of systems biology researchers in recent years. It is necessary to develop a more convenient modeling platform on which users can reconstruct their network models using completely graphical operations, and explore them with powerful analyzing modules to get a better understanding of the properties of metabolic systems. Herein, an in silico platform, FluxExplorer, for metabolic modeling and analyses based on stoichiometry has been developed as a publicly available tool for systems biology research. This platform integrates various analytic approaches, in- cluding flux balance analysis, minimization of meta- bolic adjustment, extreme pathways analysis, shadow prices analysis, and singular value decom- position, providing a thorough characterization of the metabolic system. Using a graphic modeling process, metabolic networks can be reconstructed and modi- fied intuitively and conveniently. The inconsistencies of a model with respect to the FBA principles can be proved automatically. In addition, this platform sup- ports systems biology markup language (SBML). FluxExplorer has been applied to rebuild a metabolic network in mammalian mitochondria, producing meaningful results. Generally, it is a powerful and very convenient tool for metabolic network modeling and analysis.展开更多
Based on the gene-protein-reaction (GPR) model of S. cerevisiae_iND750 and the method of constraint-based analysis, we first calculated the metabolic flux distribution of S. cere-visiae_iND750. Then we calculated the ...Based on the gene-protein-reaction (GPR) model of S. cerevisiae_iND750 and the method of constraint-based analysis, we first calculated the metabolic flux distribution of S. cere-visiae_iND750. Then we calculated the deletion impact of 438 calculable genes, one by one, on the metabolic flux redistribution of S. cere-visiae_iND750. Next we analyzed the correlation between v (describing deletion impact of one gene) and d (connection degree of one gene) and the correlation between v and Vgene (flux sum controlled by one gene), and found that both of them were not of linear relation. Furthermore, we sought out 38 important genes that most greatly affected the metabolic flux distribution, and determined their functional subsystems. We also found that many of these key genes were related to many but not several subsystems. Because the in silico model of S. cere-visiae_iND750 has been tested by many ex-periments, thus is credible, we can conclude that the result we obtained has biological sig-nificance.展开更多
Constraint-based models such as flux balance analysis （FBA） are a powerful tool to study biological metabolic networks. Under the hypothesis that cells operate at an optimal growth rate as the result of evolution an...Constraint-based models such as flux balance analysis （FBA） are a powerful tool to study biological metabolic networks. Under the hypothesis that cells operate at an optimal growth rate as the result of evolution and natural selection, this model successfully predicts most cellular behaviours in growth rate. However, the model ignores the fact that cells can change their cellular metabolic states during evolution, leaving optimal metabolic states unstable. Here, we consider all the cellular processes that change metabolic states into a single term‘noise＇, and assume that cells change metabolic states by randomly walking in feasible solution space. By simulating a state of a cell randomly walking in the constrained solution space of metabolic networks, we found that in a noisy environment cells in optimal states tend to travel away from these points. On considering the competition between the noise effect and the growth effect in cell evolution, we found that there exists a trade-off between these two effects. As a result, the population of the cells contains different cellular metabolic states, and the population growth rate is at suboptimal states.展开更多
The objective of this study was to improve the production of butyric acid by process optimization using the metabolically engineered mutant of Clostridium tyrobutyricum （PAK-Em）. First, the free-cell fermentation at...The objective of this study was to improve the production of butyric acid by process optimization using the metabolically engineered mutant of Clostridium tyrobutyricum （PAK-Em）. First, the free-cell fermentation at pH 6.0 produced butyric acid with concentration of 38.44 g/L and yield of 0.42 g/g. Second, the immobilized- cell fermentations using fibrous-bed bioreactor （FBB） were run at pHs of 5.0, 5.5, 6.0, 6.5 and 7.0 to optimize fermentation process and improve the butyric acid production. It was found that the highest titer of butyric acid, 63.02g/L, was achieved at pH 6.5. Finally, the metabolic flux balance analysis was performed to inves- tigate the carbon rebalance in C. tyrobutyricum. The results show both gene manipulation and fermentation pH change redistribute carbon between biomass, acetic acid and butyric acid. This study demonstrated that high butyric acid production could be obtained by integrating metabolic engineering and fermentation process optimization.展开更多
The metabolic network has become a hot topic in the area of system biology and flux-based analysis plays a very important role in understanding the characteristics of organism metabolic networks. We review mainly the ...The metabolic network has become a hot topic in the area of system biology and flux-based analysis plays a very important role in understanding the characteristics of organism metabolic networks. We review mainly the static methods for analyzing metabolic networks such as flux balance analysis (FBA), minimization of metabolic adjustment (MOMA), regulatory on / off minimization (ROOM), and dynamic flux balance analysis with linear quadratic regulator (DFBA-LQR). Then several kinds of commonly used software for flux analysis are introduced briefly and compared with each other. Finally, we highlight the applications of metabolic network flux analysis, especially its usage combined with other biological characteristics and its usage for drug design. The idea of combining the analysis of metabolic networks and other biochemical data has been gradually promoted and used in several aspects such as the combination of metabolic flux and the regulation of gene expression, the influence of protein evolution caused by metabolic flux, the relationship between metabolic flux and the topological characteristics, the optimization of metabolic engineering. More comprehensive and accurate properties of metabolic networks will be obtained by integrating metabolic flux analysis, network topological characteristics and dynamic modeling.展开更多
Flux balance analysis, based on the mass conservation law in a cellular organism, has been extensively employed to study the interplay between structures and functions of cellular metabolic networks. Consequently, the...Flux balance analysis, based on the mass conservation law in a cellular organism, has been extensively employed to study the interplay between structures and functions of cellular metabolic networks. Consequently, the phenotypes of the metabolism can be well elucidated. In this paper, we introduce the Expanded Flux Variability Analysis (EFVA) to characterize the intrinsic nature of metabolic reactions, such as flexibility, modularity and essentiality, by exploring the trend of the range, the maximum and the minimum flux of reactions. We took the metabolic network of Escherichia coli as an example and analyzed the variability of reaction fluxes under different growth rate constraints. The average variabil-ity of all reactions decreases dramatically when the growth rate increases. Consider the noise effect on the metabolic system, we thus argue that the microorganism may practically grow under a suboptimal state. Besides, under the EFVA framework, the reactions are easily to be grouped into catabolic and anabolic groups. And the anabolic groups can be further assigned to specific biomass constitute. We also discovered the growth rate dependent essentiality of reactions.展开更多
OBJECTIVE One of the long-expected goals of genome-scale metabolic modeling is to evaluate the influence of the perturbed enzymes to the yield of an expected end product.METHDOS Metabolic control analysis(MCA)performs...OBJECTIVE One of the long-expected goals of genome-scale metabolic modeling is to evaluate the influence of the perturbed enzymes to the yield of an expected end product.METHDOS Metabolic control analysis(MCA)performs such role to calculate the sensitivity of flux change upon that of enzymes under the framework of ordinary differential equation(ODE)models,which are restricted in small-scale networks and require explicit kinetic parameters.The constraint-based models,like flux balance analysis(FBA),lack of the room of performing MCA because they are parameters-free.In this study,we developed a hyper-cube shrink algorithm(HCSA)to incorporate the enzymatic properties to the FBA model by introducing a pair of parameters for each reaction.Our algorithm was able to handle not only prediction of knockout strains but also strains with an adjustment of expression level of certain enzymes.RESULTS We first showed the concept by applying HCSA to a simplest three-nodes network.Then we show the HCSA possesses Michaelis-Menten like behaviors characterized by steady state of ODE.We obtained good prediction of a synthetic network in Saccharomyces cerevisiae producing voilacein and analogues.Finally we showed its capability of predicting the flux distribution in genome-scale networks by applying it to sporulation in yeast.CONCLUSION We have developed an algorithm the impact on fluxes when certain enzymes were inhibited or activated.It provides us a powerful tool to evaluate the consequences of enzyme inhibitor or activator.展开更多
文摘Stoichiometry-based analyses of meta- bolic networks have aroused significant interest of systems biology researchers in recent years. It is necessary to develop a more convenient modeling platform on which users can reconstruct their network models using completely graphical operations, and explore them with powerful analyzing modules to get a better understanding of the properties of metabolic systems. Herein, an in silico platform, FluxExplorer, for metabolic modeling and analyses based on stoichiometry has been developed as a publicly available tool for systems biology research. This platform integrates various analytic approaches, in- cluding flux balance analysis, minimization of meta- bolic adjustment, extreme pathways analysis, shadow prices analysis, and singular value decom- position, providing a thorough characterization of the metabolic system. Using a graphic modeling process, metabolic networks can be reconstructed and modi- fied intuitively and conveniently. The inconsistencies of a model with respect to the FBA principles can be proved automatically. In addition, this platform sup- ports systems biology markup language (SBML). FluxExplorer has been applied to rebuild a metabolic network in mammalian mitochondria, producing meaningful results. Generally, it is a powerful and very convenient tool for metabolic network modeling and analysis.
文摘Based on the gene-protein-reaction (GPR) model of S. cerevisiae_iND750 and the method of constraint-based analysis, we first calculated the metabolic flux distribution of S. cere-visiae_iND750. Then we calculated the deletion impact of 438 calculable genes, one by one, on the metabolic flux redistribution of S. cere-visiae_iND750. Next we analyzed the correlation between v (describing deletion impact of one gene) and d (connection degree of one gene) and the correlation between v and Vgene (flux sum controlled by one gene), and found that both of them were not of linear relation. Furthermore, we sought out 38 important genes that most greatly affected the metabolic flux distribution, and determined their functional subsystems. We also found that many of these key genes were related to many but not several subsystems. Because the in silico model of S. cere-visiae_iND750 has been tested by many ex-periments, thus is credible, we can conclude that the result we obtained has biological sig-nificance.
基金Project supported by the National Natural Science Foundation of China (Grant No 10721403)the MOST of China (Grant No2009CB918500)the National Basic Research Program of China (Grant Nos 2006CB910706 and 2007CB814800)
文摘Constraint-based models such as flux balance analysis （FBA） are a powerful tool to study biological metabolic networks. Under the hypothesis that cells operate at an optimal growth rate as the result of evolution and natural selection, this model successfully predicts most cellular behaviours in growth rate. However, the model ignores the fact that cells can change their cellular metabolic states during evolution, leaving optimal metabolic states unstable. Here, we consider all the cellular processes that change metabolic states into a single term‘noise＇, and assume that cells change metabolic states by randomly walking in feasible solution space. By simulating a state of a cell randomly walking in the constrained solution space of metabolic networks, we found that in a noisy environment cells in optimal states tend to travel away from these points. On considering the competition between the noise effect and the growth effect in cell evolution, we found that there exists a trade-off between these two effects. As a result, the population of the cells contains different cellular metabolic states, and the population growth rate is at suboptimal states.
文摘The objective of this study was to improve the production of butyric acid by process optimization using the metabolically engineered mutant of Clostridium tyrobutyricum （PAK-Em）. First, the free-cell fermentation at pH 6.0 produced butyric acid with concentration of 38.44 g/L and yield of 0.42 g/g. Second, the immobilized- cell fermentations using fibrous-bed bioreactor （FBB） were run at pHs of 5.0, 5.5, 6.0, 6.5 and 7.0 to optimize fermentation process and improve the butyric acid production. It was found that the highest titer of butyric acid, 63.02g/L, was achieved at pH 6.5. Finally, the metabolic flux balance analysis was performed to inves- tigate the carbon rebalance in C. tyrobutyricum. The results show both gene manipulation and fermentation pH change redistribute carbon between biomass, acetic acid and butyric acid. This study demonstrated that high butyric acid production could be obtained by integrating metabolic engineering and fermentation process optimization.
基金supported by the National Natural Science Foundation of China (30800199, 20773085 and 30770502)National High-Tech Research and Development Program of China (2007AA02Z333)
文摘The metabolic network has become a hot topic in the area of system biology and flux-based analysis plays a very important role in understanding the characteristics of organism metabolic networks. We review mainly the static methods for analyzing metabolic networks such as flux balance analysis (FBA), minimization of metabolic adjustment (MOMA), regulatory on / off minimization (ROOM), and dynamic flux balance analysis with linear quadratic regulator (DFBA-LQR). Then several kinds of commonly used software for flux analysis are introduced briefly and compared with each other. Finally, we highlight the applications of metabolic network flux analysis, especially its usage combined with other biological characteristics and its usage for drug design. The idea of combining the analysis of metabolic networks and other biochemical data has been gradually promoted and used in several aspects such as the combination of metabolic flux and the regulation of gene expression, the influence of protein evolution caused by metabolic flux, the relationship between metabolic flux and the topological characteristics, the optimization of metabolic engineering. More comprehensive and accurate properties of metabolic networks will be obtained by integrating metabolic flux analysis, network topological characteristics and dynamic modeling.
基金Supported by the National Natural Science Foundation of China (Grant No. 10721403)National Basic Research Program of China (Grant Nos. 2006CB910706, 2007CB814800, 2009CB918500)Chun-Tsung endowment at Peking University and National Fund for Fostering Talents of Basic Science (Grant No. J0630311)
文摘Flux balance analysis, based on the mass conservation law in a cellular organism, has been extensively employed to study the interplay between structures and functions of cellular metabolic networks. Consequently, the phenotypes of the metabolism can be well elucidated. In this paper, we introduce the Expanded Flux Variability Analysis (EFVA) to characterize the intrinsic nature of metabolic reactions, such as flexibility, modularity and essentiality, by exploring the trend of the range, the maximum and the minimum flux of reactions. We took the metabolic network of Escherichia coli as an example and analyzed the variability of reaction fluxes under different growth rate constraints. The average variabil-ity of all reactions decreases dramatically when the growth rate increases. Consider the noise effect on the metabolic system, we thus argue that the microorganism may practically grow under a suboptimal state. Besides, under the EFVA framework, the reactions are easily to be grouped into catabolic and anabolic groups. And the anabolic groups can be further assigned to specific biomass constitute. We also discovered the growth rate dependent essentiality of reactions.
基金The project supported by 985 Startup Funding in PKU
文摘OBJECTIVE One of the long-expected goals of genome-scale metabolic modeling is to evaluate the influence of the perturbed enzymes to the yield of an expected end product.METHDOS Metabolic control analysis(MCA)performs such role to calculate the sensitivity of flux change upon that of enzymes under the framework of ordinary differential equation(ODE)models,which are restricted in small-scale networks and require explicit kinetic parameters.The constraint-based models,like flux balance analysis(FBA),lack of the room of performing MCA because they are parameters-free.In this study,we developed a hyper-cube shrink algorithm(HCSA)to incorporate the enzymatic properties to the FBA model by introducing a pair of parameters for each reaction.Our algorithm was able to handle not only prediction of knockout strains but also strains with an adjustment of expression level of certain enzymes.RESULTS We first showed the concept by applying HCSA to a simplest three-nodes network.Then we show the HCSA possesses Michaelis-Menten like behaviors characterized by steady state of ODE.We obtained good prediction of a synthetic network in Saccharomyces cerevisiae producing voilacein and analogues.Finally we showed its capability of predicting the flux distribution in genome-scale networks by applying it to sporulation in yeast.CONCLUSION We have developed an algorithm the impact on fluxes when certain enzymes were inhibited or activated.It provides us a powerful tool to evaluate the consequences of enzyme inhibitor or activator.
