The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Fumihide Shiraishi - One of the best experts on this subject based on the ideXlab platform.
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A Promising Method for Calculating True Steady-State Metabolite Concentrations in Large-Scale Metabolic Reaction Network Models
IEEE ACM Transactions on Computational Biology and Bioinformatics, 2018Co-Authors: Atsuko Miyawaki-kuwakado, Soichiro Komori, Fumihide ShiraishiAbstract:The calculation of steady-state metabolite concentrations in Metabolic Reaction network models is the first step in the sensitivity analysis of a Metabolic Reaction system described by differential equations. However, this calculation becomes very difficult when the number of differential equations is more than 100. In the present study, therefore, we investigated a calculation procedure for obtaining true steady-state metabolite concentrations both efficiently and accurately even in large-scale network models. For convenience, a linear pathway model composed of a simple Michaelis-Menten rate law and two TCA cycle models were used as case studies. The calculation procedure is as follows: first solve the differential equations by a numerical method for solving initial-value problems until the upper several digits of the calculated values stabilize, and then use these values as initial guesses for a root-finding technique. An intensive investigation indicates that the S-system technique, finding roots in logarithmic space and providing a broader convergence region, is superior to the Newton-Raphson technique, and the algorithm using the S-system technique successfully provides true steady-state values with machine accuracy even with 1,500 differential equations. The complex-step method is also shown to contribute to shortening the calculation time and enhancing the accuracy. The program code has been deposited to https://github.com/BioprocessdesignLab/Steadystateconc .
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evaluation of an s system root finding method for estimating parameters in a Metabolic Reaction model
Bellman Prize in Mathematical Biosciences, 2018Co-Authors: Michio Iwata, Atsuko Miyawakikuwakado, Erika Yoshida, Soichiro Komori, Fumihide ShiraishiAbstract:Abstract In a mathematical model, estimation of parameters from time-series data of Metabolic concentrations in cells is a challenging task. However, it seems that a promising approach for such estimation has not yet been established. Biochemical Systems Theory (BST) is a powerful methodology to construct a power-law type model for a given Metabolic Reaction system and to then characterize it efficiently. In this paper, we discuss the use of an S-system root-finding method (S-system method) to estimate parameters from time-series data of metabolite concentrations. We demonstrate that the S-system method is superior to the Newton–Raphson method in terms of the convergence region and iteration number. We also investigate the usefulness of a translocation technique and a complex-step differentiation method toward the practical application of the S-system method. The results indicate that the S-system method is useful to construct mathematical models for a variety of Metabolic Reaction networks.
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investigation of kinetic order sensitivities in Metabolic Reaction networks
Journal of Theoretical Biology, 2017Co-Authors: Masatsugu Yamada, Kansuporn Sriyudthsak, Masami Yokota Hirai, Masashi Iwanaga, Fumihide ShiraishiAbstract:Abstract Kinetic-order sensitivity (the ratio of relative change in a dependent variable to the relative change in a kinetic order in a power-law–type differential equation) has recently become an important indicator in Metabolic pathway analysis using mathematical models with parameter values determined from time-series data on cellular metabolite concentrations. Here, we discuss a potential problem in calculating kinetic-order sensitivities. When the steady-state metabolite concentration is less than unity, a slight increase in the kinetic order changes the metabolite concentration in the incorrect direction, yielding a kinetic-order sensitivity value with an incorrect sign. This is caused by a property of the power-law function ( y = X n ): when X is less than unity, y decreases for a larger positive n or for a smaller absolute value of negative n . We propose two solutions. The first is to directly calculate the kinetic-order sensitivities and then reverse the sign of the relevant value if a steady-state metabolite concentration less than unity is involved. The second involves calculation of the kinetic-order sensitivities after setting all metabolite concentrations to values greater than unity (e.g., by changing the units from mM to μM). The latter method changes the absolute values of the kinetic-order sensitivities according to the magnitude of a multiplication factor, because kinetic-order sensitivities do not have unique values. Nevertheless, since the normalized absolute values exhibit an almost identical distribution, it should not be difficult to identify which kinetic order has greater effect, although kinetic order rankings may change slightly under different calculation conditions.
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mathematical modeling and dynamic simulation of Metabolic Reaction systems using metabolome time series data
Frontiers in Molecular Biosciences, 2016Co-Authors: Kansuporn Sriyudthsak, Fumihide Shiraishi, Masami Yokota HiraiAbstract:The high-throughput acquisition of metabolome data is greatly anticipated for the complete understanding of cellular metabolism in living organisms. A variety of analytical technologies have been developed to acquire large-scale Metabolic profiles under different biological or environmental conditions. Time series data are useful for predicting the most likely Metabolic pathways because they provide important information regarding the accumulation of metabolites, which implies causal relationships in the Metabolic Reaction network. Considerable effort has been undertaken to utilize these data for constructing a mathematical model merging system properties and quantitatively characterizing a whole Metabolic system in toto. However, there are technical difficulties between benchmarking the provision and utilization of data. Although hundreds of metabolites can be measured, which provide information on the Metabolic Reaction system, simultaneous measurement of thousands of metabolites is still challenging. In addition, it is nontrivial to logically predict the dynamic behaviors of unmeasurable metabolite concentrations without sufficient information on the Metabolic Reaction network. Yet, consolidating the advantages of advancements in both metabolomics and mathematical modeling remain to be accomplished. This review outlines the conceptual basis of and recent advances in technologies in both the research fields. It also highlights the potential for constructing a large-scale mathematical model by estimating model parameters from time series metabolome data in order to comprehensively understand metabolism at the systems level.
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using dynamic sensitivities to characterize Metabolic Reaction systems
Bellman Prize in Mathematical Biosciences, 2015Co-Authors: Kansuporn Sriyudthsak, Rudiyanto Gunawan, Fumihide ShiraishiAbstract:Metabolite concentrations in cells are governed by enzyme kinetics in the Metabolic Reaction system. One can analyze how these concentrations depend on system variables such as enzyme activities by computing system sensitivities, which generally vary with time. Dynamic sensitivities, i.e., time-varying sensitivities, reflect the time-dependent response of the Metabolic Reaction network to perturbations. Unfortunately, dynamic sensitivity profiles are not commonly used in the analysis of Metabolic Reaction systems. In the present study, we demonstrate the use of dynamic logarithmic gains, i.e., normalized time-varying sensitivities, to gain insights into the dynamic behavior of Metabolic networks. A biosynthetic Reaction model of aromatic amino acids proposed by other researchers is used as a case study. The model system is analyzed using the dynamic logarithmic gains in parallel with simulations of the time-transient behavior of metabolite concentrations and Metabolic fluxes. The result indicates that the influences of independent variables are most pronounced just after perturbations and the effects of perturbations on metabolite concentration at early times can be larger than those at steady state. These findings suggest that it is important to perform dynamic sensitivity analysis in addition to steady-state analysis. Furthermore, the rankings of the bottleneck ranking indicators, defined as the product of dynamic logarithmic gain and metabolite concentration, for three desired amino acids reveal that the degree of bottleneck for each enzyme changes with time. In conclusion, the dynamic logarithmic gains are not only useful for analyzing Metabolic Reaction systems but also can offer additional insights on the transient behavior of the system over steady state sensitivities, leading to a proper design of Metabolic systems.
Kansuporn Sriyudthsak - One of the best experts on this subject based on the ideXlab platform.
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investigation of kinetic order sensitivities in Metabolic Reaction networks
Journal of Theoretical Biology, 2017Co-Authors: Masatsugu Yamada, Kansuporn Sriyudthsak, Masami Yokota Hirai, Masashi Iwanaga, Fumihide ShiraishiAbstract:Abstract Kinetic-order sensitivity (the ratio of relative change in a dependent variable to the relative change in a kinetic order in a power-law–type differential equation) has recently become an important indicator in Metabolic pathway analysis using mathematical models with parameter values determined from time-series data on cellular metabolite concentrations. Here, we discuss a potential problem in calculating kinetic-order sensitivities. When the steady-state metabolite concentration is less than unity, a slight increase in the kinetic order changes the metabolite concentration in the incorrect direction, yielding a kinetic-order sensitivity value with an incorrect sign. This is caused by a property of the power-law function ( y = X n ): when X is less than unity, y decreases for a larger positive n or for a smaller absolute value of negative n . We propose two solutions. The first is to directly calculate the kinetic-order sensitivities and then reverse the sign of the relevant value if a steady-state metabolite concentration less than unity is involved. The second involves calculation of the kinetic-order sensitivities after setting all metabolite concentrations to values greater than unity (e.g., by changing the units from mM to μM). The latter method changes the absolute values of the kinetic-order sensitivities according to the magnitude of a multiplication factor, because kinetic-order sensitivities do not have unique values. Nevertheless, since the normalized absolute values exhibit an almost identical distribution, it should not be difficult to identify which kinetic order has greater effect, although kinetic order rankings may change slightly under different calculation conditions.
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mathematical modeling and dynamic simulation of Metabolic Reaction systems using metabolome time series data
Frontiers in Molecular Biosciences, 2016Co-Authors: Kansuporn Sriyudthsak, Fumihide Shiraishi, Masami Yokota HiraiAbstract:The high-throughput acquisition of metabolome data is greatly anticipated for the complete understanding of cellular metabolism in living organisms. A variety of analytical technologies have been developed to acquire large-scale Metabolic profiles under different biological or environmental conditions. Time series data are useful for predicting the most likely Metabolic pathways because they provide important information regarding the accumulation of metabolites, which implies causal relationships in the Metabolic Reaction network. Considerable effort has been undertaken to utilize these data for constructing a mathematical model merging system properties and quantitatively characterizing a whole Metabolic system in toto. However, there are technical difficulties between benchmarking the provision and utilization of data. Although hundreds of metabolites can be measured, which provide information on the Metabolic Reaction system, simultaneous measurement of thousands of metabolites is still challenging. In addition, it is nontrivial to logically predict the dynamic behaviors of unmeasurable metabolite concentrations without sufficient information on the Metabolic Reaction network. Yet, consolidating the advantages of advancements in both metabolomics and mathematical modeling remain to be accomplished. This review outlines the conceptual basis of and recent advances in technologies in both the research fields. It also highlights the potential for constructing a large-scale mathematical model by estimating model parameters from time series metabolome data in order to comprehensively understand metabolism at the systems level.
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using dynamic sensitivities to characterize Metabolic Reaction systems
Bellman Prize in Mathematical Biosciences, 2015Co-Authors: Kansuporn Sriyudthsak, Rudiyanto Gunawan, Fumihide ShiraishiAbstract:Metabolite concentrations in cells are governed by enzyme kinetics in the Metabolic Reaction system. One can analyze how these concentrations depend on system variables such as enzyme activities by computing system sensitivities, which generally vary with time. Dynamic sensitivities, i.e., time-varying sensitivities, reflect the time-dependent response of the Metabolic Reaction network to perturbations. Unfortunately, dynamic sensitivity profiles are not commonly used in the analysis of Metabolic Reaction systems. In the present study, we demonstrate the use of dynamic logarithmic gains, i.e., normalized time-varying sensitivities, to gain insights into the dynamic behavior of Metabolic networks. A biosynthetic Reaction model of aromatic amino acids proposed by other researchers is used as a case study. The model system is analyzed using the dynamic logarithmic gains in parallel with simulations of the time-transient behavior of metabolite concentrations and Metabolic fluxes. The result indicates that the influences of independent variables are most pronounced just after perturbations and the effects of perturbations on metabolite concentration at early times can be larger than those at steady state. These findings suggest that it is important to perform dynamic sensitivity analysis in addition to steady-state analysis. Furthermore, the rankings of the bottleneck ranking indicators, defined as the product of dynamic logarithmic gain and metabolite concentration, for three desired amino acids reveal that the degree of bottleneck for each enzyme changes with time. In conclusion, the dynamic logarithmic gains are not only useful for analyzing Metabolic Reaction systems but also can offer additional insights on the transient behavior of the system over steady state sensitivities, leading to a proper design of Metabolic systems.
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A U-system approach for predicting Metabolic behaviors and responses based on an alleged Metabolic Reaction network
BMC Systems Biology, 2014Co-Authors: Kansuporn Sriyudthsak, Yui Yamashita, Yuji Sawada, Yukako Chiba, Hitoshi Onouchi, Satoshi Naito, Toru Fujiwara, Eberhard O Voit, Shigehiko Kanaya, Fumihide ShiraishiAbstract:Background Progress in systems biology offers sophisticated approaches toward a comprehensive understanding of biological systems. Yet, computational analyses are held back due to difficulties in determining suitable model parameter values from experimental data which naturally are subject to biological fluctuations. The data may also be corrupted by experimental uncertainties and sometimes do not contain all information regarding variables that cannot be measured for technical reasons. Results We show here a streamlined approach for the construction of a coarse model that allows us to set up dynamic models with minimal input information. The approach uses a hybrid between a pure mass action system and a generalized mass action (GMA) system in the framework of biochemical systems theory (BST) with rate constants of 1, normal kinetic orders of 1, and -0.5 and 0.5 for inhibitory and activating effects, named Unity (U)-system. The U-system model does not necessarily fit all data well but is often sufficient for predicting Metabolic behavior of metabolites which cannot be simultaneously measured, identifying inconsistencies between experimental data and the assumed underlying pathway structure, as well as predicting system responses to a modification of gene or enzyme. The U-system approach was validated with small, generic systems and implemented to model a large-scale Metabolic Reaction network of a higher plant, Arabidopsis . The dynamic behaviors obtained by predictive simulations agreed with actually available metabolomic time-series data, identified probable errors in the experimental datasets, and estimated probable behavior of unmeasurable metabolites in a qualitative manner. The model could also predict Metabolic responses of Arabidopsis with altered network structures due to genetic modification. Conclusions The U-system approach can effectively predict Metabolic behaviors and responses based on structures of an alleged Metabolic Reaction network. Thus, it can be a useful first-line tool of data analysis, model diagnostics and aid the design of next-step experiments.
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a u system approach for predicting Metabolic behaviors and responses based on an alleged Metabolic Reaction network
BMC Systems Biology, 2014Co-Authors: Kansuporn Sriyudthsak, Yui Yamashita, Yuji Sawada, Yukako Chiba, Hitoshi Onouchi, Satoshi Naito, Toru Fujiwara, Eberhard O Voit, Shigehiko Kanaya, Fumihide ShiraishiAbstract:Background Progress in systems biology offers sophisticated approaches toward a comprehensive understanding of biological systems. Yet, computational analyses are held back due to difficulties in determining suitable model parameter values from experimental data which naturally are subject to biological fluctuations. The data may also be corrupted by experimental uncertainties and sometimes do not contain all information regarding variables that cannot be measured for technical reasons.
E D Gilles - One of the best experts on this subject based on the ideXlab platform.
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the organization of Metabolic Reaction networks iii application for diauxic growth on glucose and lactose
Metabolic Engineering, 2001Co-Authors: Andreas Kremling, Knut Jahreis, Joseph W Lengeler, Katja Bettenbrock, Britta Laube, E D GillesAbstract:A mathematical model to describe carbon catabolite repression in Escherichia coli is developed and in part validated. The model is aggregated from two functional units describing glucose and lactose transport and degradation. Both units are members of the crp modulon and are under control of a global signal transduction system which calculates the signals that turn on or off gene expression for the specific enzymes. Using isogenic mutant strains, our model is validated by a set of experiments. In these experiments, substrate composition of the preculture and of the experimental culture are varied in order to stimulate the system in different ways. With the obtained measurements (three states in the liquid phase and one intracellular component) a part of the model parameters could be estimated. Therefore all experiments could be sufficiently described with a single set of parameters.
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the organization of Metabolic Reaction networks ii signal processing in hierarchical structured functional units
Metabolic Engineering, 2001Co-Authors: Andreas Kremling, E D GillesAbstract:Abstract Based on the analysis of molecular interactions of proteins with DNA binding sites, a new approach to developing mathematical models describing gene expression is introduced. Detection of hierarchical structures in Metabolic networks can be used to decompose complex Reaction schemes. This will be achieved by assigning each regulator protein to one level in the hierarchy. Signals are then transduced from the top level to the lower level, but not vice versa. The method is shown by a simple example with two interacting proteins. A comparison of simulation results shows good agreement between a model taking all interactions into account and a model developed with the new approach. Finally, the method is applied to the crpA modulon in Escherichia coli, which controls uptake and metabolism for a number of carbohydrates. Here, RNA polymerase represents the top level, CrpA the second level, and the lactose-specific repressor LacI the lowest level, respectively. Besides the lactose operon, the method is applied to the adenylate cyclase gene and the gene for the regulator CrpA.
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the organization of Metabolic Reaction networks a signal oriented approach to cellular models
Metabolic Engineering, 2000Co-Authors: Andreas Kremling, Knut Jahreis, Joseph W Lengeler, E D GillesAbstract:Complex Metabolic networks are characterized by a great number of elements and many regulatory loops. The description of these networks with mathematical models requires the definition of functional units that group together several cellular processes. The approach presented here is based on the idea that cellular functional units may be assigned directly to mathematical modeling objects. Because the proposed modeling objects have defined inputs and outputs, they can be connected with other modeling objects until eventually the whole metabolism is covered. This modular approach guarantees a high transparency for biologists as well as for engineers. Three criteria are introduced to demarcate functional units. The criteria consider the physiological pathways, the organization of the corresponding genes, and the observation that cellular systems can be structured into units showing a hierarchy of signal transduction and processing. As an example, the carbon catabolic Reactions in Escherichia coli are discussed as members of a functional unit catabolism. 2000 Academic Press
Masami Yokota Hirai - One of the best experts on this subject based on the ideXlab platform.
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investigation of kinetic order sensitivities in Metabolic Reaction networks
Journal of Theoretical Biology, 2017Co-Authors: Masatsugu Yamada, Kansuporn Sriyudthsak, Masami Yokota Hirai, Masashi Iwanaga, Fumihide ShiraishiAbstract:Abstract Kinetic-order sensitivity (the ratio of relative change in a dependent variable to the relative change in a kinetic order in a power-law–type differential equation) has recently become an important indicator in Metabolic pathway analysis using mathematical models with parameter values determined from time-series data on cellular metabolite concentrations. Here, we discuss a potential problem in calculating kinetic-order sensitivities. When the steady-state metabolite concentration is less than unity, a slight increase in the kinetic order changes the metabolite concentration in the incorrect direction, yielding a kinetic-order sensitivity value with an incorrect sign. This is caused by a property of the power-law function ( y = X n ): when X is less than unity, y decreases for a larger positive n or for a smaller absolute value of negative n . We propose two solutions. The first is to directly calculate the kinetic-order sensitivities and then reverse the sign of the relevant value if a steady-state metabolite concentration less than unity is involved. The second involves calculation of the kinetic-order sensitivities after setting all metabolite concentrations to values greater than unity (e.g., by changing the units from mM to μM). The latter method changes the absolute values of the kinetic-order sensitivities according to the magnitude of a multiplication factor, because kinetic-order sensitivities do not have unique values. Nevertheless, since the normalized absolute values exhibit an almost identical distribution, it should not be difficult to identify which kinetic order has greater effect, although kinetic order rankings may change slightly under different calculation conditions.
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mathematical modeling and dynamic simulation of Metabolic Reaction systems using metabolome time series data
Frontiers in Molecular Biosciences, 2016Co-Authors: Kansuporn Sriyudthsak, Fumihide Shiraishi, Masami Yokota HiraiAbstract:The high-throughput acquisition of metabolome data is greatly anticipated for the complete understanding of cellular metabolism in living organisms. A variety of analytical technologies have been developed to acquire large-scale Metabolic profiles under different biological or environmental conditions. Time series data are useful for predicting the most likely Metabolic pathways because they provide important information regarding the accumulation of metabolites, which implies causal relationships in the Metabolic Reaction network. Considerable effort has been undertaken to utilize these data for constructing a mathematical model merging system properties and quantitatively characterizing a whole Metabolic system in toto. However, there are technical difficulties between benchmarking the provision and utilization of data. Although hundreds of metabolites can be measured, which provide information on the Metabolic Reaction system, simultaneous measurement of thousands of metabolites is still challenging. In addition, it is nontrivial to logically predict the dynamic behaviors of unmeasurable metabolite concentrations without sufficient information on the Metabolic Reaction network. Yet, consolidating the advantages of advancements in both metabolomics and mathematical modeling remain to be accomplished. This review outlines the conceptual basis of and recent advances in technologies in both the research fields. It also highlights the potential for constructing a large-scale mathematical model by estimating model parameters from time series metabolome data in order to comprehensively understand metabolism at the systems level.
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estimation of kinetic parameters in an s system equation model for a Metabolic Reaction system using the newton raphson method
Bellman Prize in Mathematical Biosciences, 2014Co-Authors: Michio Iwata, Kansuporn Sriyudthsak, Masami Yokota Hirai, Fumihide ShiraishiAbstract:Abstract Metabolic Reaction systems can be modeled easily in terms of S-system type equations if their Metabolic maps are available. This study therefore proposes a method for estimating parameters in decoupled S-system equations on the basis of the Newton–Raphson method and elucidates the performance of this estimation method. Parameter estimation from the time-course data of metabolite concentrations reveals that the parameters estimated are highly accurate, indicating that the estimation algorithm has been constructed correctly. The number of iterations is small and the calculation converges in a very short time (usually less than 1 s). The method is also applied to time course data with noise and found to estimate parameters efficiently. Results indicate that the present method has the potential to be extended to a method for estimating parameters in large-scale Metabolic Reaction systems.
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Estimation of kinetic parameters in an S-system equation model for a Metabolic Reaction system using the Newton–Raphson method
Bellman Prize in Mathematical Biosciences, 2013Co-Authors: Michio Iwata, Kansuporn Sriyudthsak, Masami Yokota Hirai, Fumihide ShiraishiAbstract:Abstract Metabolic Reaction systems can be modeled easily in terms of S-system type equations if their Metabolic maps are available. This study therefore proposes a method for estimating parameters in decoupled S-system equations on the basis of the Newton–Raphson method and elucidates the performance of this estimation method. Parameter estimation from the time-course data of metabolite concentrations reveals that the parameters estimated are highly accurate, indicating that the estimation algorithm has been constructed correctly. The number of iterations is small and the calculation converges in a very short time (usually less than 1 s). The method is also applied to time course data with noise and found to estimate parameters efficiently. Results indicate that the present method has the potential to be extended to a method for estimating parameters in large-scale Metabolic Reaction systems.
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identification of a Metabolic Reaction network from time series data of metabolite concentrations
PLOS ONE, 2013Co-Authors: Kansuporn Sriyudthsak, Fumihide Shiraishi, Masami Yokota HiraiAbstract:Recent development of high-throughput analytical techniques has made it possible to qualitatively identify a number of metabolites simultaneously. Correlation and multivariate analyses such as principal component analysis have been widely used to analyse those data and evaluate correlations among the Metabolic profiles. However, these analyses cannot simultaneously carry out identification of Metabolic Reaction networks and prediction of dynamic behaviour of metabolites in the networks. The present study, therefore, proposes a new approach consisting of a combination of statistical technique and mathematical modelling approach to identify and predict a probable Metabolic Reaction network from time-series data of metabolite concentrations and simultaneously construct its mathematical model. Firstly, regression functions are fitted to experimental data by the locally estimated scatter plot smoothing method. Secondly, the fitted result is analysed by the bivariate Granger causality test to determine which metabolites cause the change in other metabolite concentrations and remove less related metabolites. Thirdly, S-system equations are formed by using the remaining metabolites within the framework of biochemical systems theory. Finally, parameters including rate constants and kinetic orders are estimated by the Levenberg–Marquardt algorithm. The estimation is iterated by setting insignificant kinetic orders at zero, i.e., removing insignificant metabolites. Consequently, a Reaction network structure is identified and its mathematical model is obtained. Our approach is validated using a generic inhibition and activation model and its practical application is tested using a simplified model of the glycolysis of Lactococcus lactis MG1363, for which actual time-series data of metabolite concentrations are available. The results indicate the usefulness of our approach and suggest a probable pathway for the production of lactate and acetate. The results also indicate that the approach pinpoints a probable strong inhibition of lactate on the glycolysis pathway.
Andreas Kremling - One of the best experts on this subject based on the ideXlab platform.
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the organization of Metabolic Reaction networks iii application for diauxic growth on glucose and lactose
Metabolic Engineering, 2001Co-Authors: Andreas Kremling, Knut Jahreis, Joseph W Lengeler, Katja Bettenbrock, Britta Laube, E D GillesAbstract:A mathematical model to describe carbon catabolite repression in Escherichia coli is developed and in part validated. The model is aggregated from two functional units describing glucose and lactose transport and degradation. Both units are members of the crp modulon and are under control of a global signal transduction system which calculates the signals that turn on or off gene expression for the specific enzymes. Using isogenic mutant strains, our model is validated by a set of experiments. In these experiments, substrate composition of the preculture and of the experimental culture are varied in order to stimulate the system in different ways. With the obtained measurements (three states in the liquid phase and one intracellular component) a part of the model parameters could be estimated. Therefore all experiments could be sufficiently described with a single set of parameters.
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the organization of Metabolic Reaction networks ii signal processing in hierarchical structured functional units
Metabolic Engineering, 2001Co-Authors: Andreas Kremling, E D GillesAbstract:Abstract Based on the analysis of molecular interactions of proteins with DNA binding sites, a new approach to developing mathematical models describing gene expression is introduced. Detection of hierarchical structures in Metabolic networks can be used to decompose complex Reaction schemes. This will be achieved by assigning each regulator protein to one level in the hierarchy. Signals are then transduced from the top level to the lower level, but not vice versa. The method is shown by a simple example with two interacting proteins. A comparison of simulation results shows good agreement between a model taking all interactions into account and a model developed with the new approach. Finally, the method is applied to the crpA modulon in Escherichia coli, which controls uptake and metabolism for a number of carbohydrates. Here, RNA polymerase represents the top level, CrpA the second level, and the lactose-specific repressor LacI the lowest level, respectively. Besides the lactose operon, the method is applied to the adenylate cyclase gene and the gene for the regulator CrpA.
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the organization of Metabolic Reaction networks a signal oriented approach to cellular models
Metabolic Engineering, 2000Co-Authors: Andreas Kremling, Knut Jahreis, Joseph W Lengeler, E D GillesAbstract:Complex Metabolic networks are characterized by a great number of elements and many regulatory loops. The description of these networks with mathematical models requires the definition of functional units that group together several cellular processes. The approach presented here is based on the idea that cellular functional units may be assigned directly to mathematical modeling objects. Because the proposed modeling objects have defined inputs and outputs, they can be connected with other modeling objects until eventually the whole metabolism is covered. This modular approach guarantees a high transparency for biologists as well as for engineers. Three criteria are introduced to demarcate functional units. The criteria consider the physiological pathways, the organization of the corresponding genes, and the observation that cellular systems can be structured into units showing a hierarchy of signal transduction and processing. As an example, the carbon catabolic Reactions in Escherichia coli are discussed as members of a functional unit catabolism. 2000 Academic Press