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Integration of enzyme activities into metabolic flux distributions by elementary mode analysis

Integration of enzyme activities into metabolic flux distributions by elementary mode analysis. Kyushu Institute of Technology Hiroyuki Kurata, Quanyu Zhao, Ryuichi Okuda, Kazuyuki Shimizu. Background. A computational model for large-scale biochemical networks

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Integration of enzyme activities into metabolic flux distributions by elementary mode analysis

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  1. Integration of enzyme activities into metabolic flux distributions by elementary mode analysis Kyushu Institute of Technology Hiroyuki Kurata, Quanyu Zhao, Ryuichi Okuda, Kazuyuki Shimizu

  2. Background A computational model for large-scale biochemical networks is required for the integration of postgenomic data and molecular biology data and should be readily updated with new experimental data. Network pathway-based models are promising models e.g., flux balance analysis, elementary mode analysis, extreme pathways.

  3. Network pathway analysis facilitates understanding or designing metabolic systems and enables prediction of metabolic flux distributions. Network-based flux analysis requires considering not only pathway architectures but also the proteome or transcriptome to predict flux distributions, because recombinant microbes significantly change the distribution of gene expressions. The current problem is how to integrate such heterogeneous data to build a network-based model.

  4. A problem of Flux Balance Analysis S: Stoichiometric matrix v: Flux distribution vector F(v): Objective function Q: How do you integrate transcriptome or proteome data into flux balance analysis? A: Few answers. A new method for integrating these data into metabolic flux analysis is required.

  5. Elementary Mode Analysis (EMA) The elementary mode is the minimal set of enzymes that can operate at steady-state, with all the irreversible reactions operating properly EM1 EM1 EM2 A B EM2

  6. Example of EM decomposition EM EM1 EM2 EM3 Stoichiometric Matrix EM4 EM5

  7. EM EM matrix flux 1 2 3 4 5 EMC A flux distribution is decomposed onto EMs. In general, EMCs are not determined uniquely.

  8. Enzyme Control Flux (ECF) To link enzyme activity data to flux distributions of metabolic networks, we have proposed Enzyme Control Flux (ECF), a novel model that integrates enzyme activity into elementary mode analysis (EMA). ECF presents the power-law formula describing how changes in enzyme activities between wild-type and a mutant are related to changes in the elementary mode coefficients (EMCs).

  9. ECF Strategy Enzyme activity data for wild type and mutants A flux distribution of wild type ECF Prediction of a flux distribution for a mutant

  10. For wild type P: n x m EM matrix (n: reaction number, m: number of EMs ECF correlation: mt: mutant For a mutant

  11. a1 a2 an ECF model based on the power law formula :the i-th EMC for a mutant :the i-th EMC for wild type :relative enzyme activity of a mutant to wild type :coefficient for normalized substrate uptake :power coefficient (adjustment parameter) i-th EM

  12. Validation of ECF To validate the feasibility of ECF, we integrated enzyme activity data into the EMCs of Escherichia coli and Bacillus subtilis wild-type. The ECF model effectively uses an enzyme activity profile to estimate the flux distribution of the mutants and the increase in the number of incorporated enzyme activities decreases the model error of ECF.

  13. pykF knockout mutant 1. The EM matrix is obtained by FluxAnayzer (Klamt et al). 2. The EMCs for wild type are calculated by using a flux distribution of wild type. 3. Relative enzyme activities of a mutant to wild type are integrated into the EMCs for wild type by the ECF model to obtain the EMCs for a mutant 4. A flux distribution of a mutant is predicted.

  14. Metabolic model for a pyk knockout mutant 74 EMs

  15. EMCs for wild type are calculated. The ECF model predicts the flux distribution of a mutant Flux spectra for a mutant is predicted by ECF The mean values are used.

  16. Effect of the number of the integrated enzymes on model error (ECF) max mean + s.d. mean mean - s.d. min Model Error spectrum An increase in the number of integrated enzymes enhances model accuracy. Model Error = |experimental flux value - predicted flux value|

  17. b=0.5 b=1 b=2 b=4 Best A unique adjustment parameter of b is determined. A b of 1 is a best choice for accurate prediction or correlation.

  18. Successful application of ECF to other mutants

  19. Conclusion The ECF model is a non-mechanistic and static model to link an enzyme activity profile to a metabolic flux distribution by introducing the power-law formula into EMA, suggesting that the change in an enzyme profile rather reflects the change in the flux distribution. The ECF model is highly applicable to the central metabolism in knockout mutants of E. coli and B. subtilis.

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