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Genetic modification of flux (GMF) for flux prediction of mutants

Genetic modification of flux (GMF) for flux prediction of mutants. Kyushu Institute of Technology Quanyu Zhao, Hiroyuki Kurata. Topics. Background of computational modeling of biological systems Elementary mode analysis based Enzyme Control Flux (ECF) Genetic Modification of Flux (GMF).

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Genetic modification of flux (GMF) for flux prediction of mutants

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  1. Genetic modification of flux (GMF) for flux prediction of mutants Kyushu Institute of Technology Quanyu Zhao, Hiroyuki Kurata

  2. Topics • Background of computational modeling of biological systems • Elementary mode analysis based Enzyme Control Flux (ECF) Genetic Modification of Flux (GMF)

  3. Our objectives Quantitative modeling of metabolic networks is necessary for computer-aided rational design.

  4. Computer model of metabolic systems Omics data Molecular Biology data Integration of heterogenous data Metabolic Networks Quantitative Model BASE Genomics Transcriptomics Proteomics Metabolomics Fluxomics Physiomics

  5. Quantitative Models Differential equations Dynamic model，Many unknown parameters Linear Algebraic equations Constraint based flux analysis at the steady state

  6. FLUX BALANCE ANALYSIS: ＦＢＡ Prediction of a flux distribution at the steady state Objective function Constraint S Stoichiometric matrix v flux distribution

  7. For gene deletion mutants, steady state flux is predicted using Boolean Logic Reactions for knockout gene = 0 Other reactions =1

  8. Current problem: In gene deletion mutants, many gene expressions are varied, not digital. How to integrate transcriptome or proteome into metabolic flux analysis. Proposal: Elementary mode analysis is employed for such integration.

  9. Elementary Modes (EMAs) Minimum sets of enzyme cascades consisting of irreversible reactions at the steady state EM1 EM2 EM1 A B EM2

  10. Elementary Modes (Ems) Flux distribution Coefficients EM １ ２ Elementary mode matrix ３ Stoichiometric Matrix ４ ５

  11. EM Flux Flux＝ EM Matrix・ EMC 1 2 3 4 5 EMC is not uniquely determined. Objective function is required.

  12. Objective functions Growth maximization: Linear programming Convenient function: Quadratic programming Maximum Entropy Principle (MEP)

  13. Maximum Entropy Principle (MEP) Shannon information entropy Constraint Quanyu Zhao, Hiroyuki Kurata,Maximum entropy decomposition of flux distribution at steady state to elementary modes. J Biosci Bioeng, 107: 84-89, 2009

  14. Enzyme Control Flux (ECF) ECF integrates enzyme activity profiles into elementary modes. ECF presents the power-law formula describing how changes in an enzyme activity profile between wild-type and a mutant is related to changes in the elementary mode coefficients (EMCs). Kurata H, Zhao Q, Okuda R, Shimizu K.Integration of enzyme activities into metabolic flux distributions by elementary mode analysis.BMC Syst Biol. 2007;1:31.

  15. Enzyme Control Flux(ECF) Network model with flux of WT Enzyme activity profile Mutant / WT Power-Law formula Estimation of a flux distribution of a mutant

  16. ＥＣＦ Algorithm MEP Reference model Power Law Formula Change in enzyme activity profile Prediction of a flux distribution of a target cell

  17. Power Law Formula Optimal b＝1 EMi EMi a1 a2 a5 Enzyme activity profile

  18. pykF knockout in a metabolic network 74 EMs

  19. Effect of the number of the integrated enzymes on model error (ECF) An increase in the number of integrated enzymes enhances model accuracy. Model Error = Difference in the flux distributions between WT and a mutant

  20. Prediction accuracy of ECF

  21. Summary of ECF ECF provides quantitative correlations between enzyme activity profile and flux distribution.

  22. Genetic Modification of Flux Quanyu Zhao, Hiroyuki Kurata, Genetic modification of flux for flux prediction of mutants, Bioinformatics, 25: 1702-1708, 2009

  23. Prediction of Flux distribution for genetic mutants Metabolic networks /gene deletion Metabolic flux distribution Gene expression (enzyme activity) profile MOMA/rFBA ECF Metabolic flux distribution for genetic mutants

  24. Flow chart of GMF Metabolic networks /genetic modification Metabolic flux distribution mCEF Gene expression (enzyme activity) profile ECF Metabolic flux distribution for genetic mutants

  25. Expected advantage of GMF • Available to gene knockout, over-expressing or under-expressing mutants • MOMA/rFBA are available only for gene deletion, because they use Boolean Logic.

  26. Control Effective Flux (CEF) Transcript ratio of metabolic genes CEFs for different substrates glucose, glycerol and acetate. Transcript ratio for the growth on glycerol versus glucose Stelling J, et al, Nature, 2002, 420, 190-193

  27. mCEF is an extension of CEF • available for • Genetically modification mutants • Up-regulation • Down-regulation • Deletion

  28. GMF = mCEF+ECF S (Stoichiometric matrix) P (EMs matrix) mCEF WT mCEF Mutant ECF Experimental data

  29. mCEF predicts the transcript ratio of a mutant to wild type Ishii N, et al. Science 316 : 593-597,2007

  30. Characterization of GMF Comparison of GMF(CEF+ECF) with FBA and MOMA for E. coli gene deletion mutants

  31. Vk is the flux of gene knockout reaction k • FBA • MOMA Vk is the flux of gene knockout reaction k

  32. Prediction of the flux distribution of an E. colizwf mutant by GMF, FBA, and MOMA Zhao J, Baba T, Mori H, Shimizu K. Appl Microbiol Biotechnol. 2004;64(1):91-8.

  33. Prediction of the flux distribution of an E. colignd mutant by CEF+ECF, FBA, and MOMA Zhao J, Baba T, Mori H, Shimizu K. Appl Microbiol Biotechnol. 2004;64(1):91-8.

  34. Prediction of the flux distribution of an E. colippc mutant by CEF+ECF, FBA, and MOMA Peng LF, Arauzo-Bravo MJ, Shimizu K. FEMS Microbiol Letters, 2004, 235(1): 17-23

  35. Prediction of the flux distribution of an E. colipykF mutant by CEF+ECF, FBA, and MOMA Siddiquee KA, Arauzo-Bravo MJ, Shimizu K. Appl Microbiol Biotechol 2004, 63(4):407-417

  36. Prediction of the flux distribution of an E. colipgi mutant by CEF+ECF, FBA, and MOMA Hua Q, Yang C, Baba T, Mori H, Shimizu K. J Bacteriol 2003, 185(24):7053-7067

  37. Prediction errors of FBA, MOMA and GMF for five mutants of E. coli Model Error = Difference in the flux distributions between WT and a mutant

  38. Is GMF applicable to over-expressing or less-expressing mutants? (FBA and MOMA are not applicable to these mutants.)

  39. Up/down-regulation mutants FBP over-expressing mutant of C. glutamicum G6P dehydrogenase over-expressing mutant of C. glutamicum gnd deficient mutant of C. glutamicum G6P dehydrogenase over-expressing mutant of E. coli

  40. Summary of GMF • mCEF is combined to ECF for the accurate prediction of flux distribution of mutants. • GMF is applied to the mutants where an enzyme is over-expressed, less-expressed. It has an advantage over rFBA and MOMA.

  41. Conclusion • ECF is available for the quantitative correlation between an enzyme activity profile and its associated flux distribution • GMF is a new tool for predicting a flux distribution for genetically modified mutants.

  42. Thank you very much

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