1 / 18

Purpose of the Experiment

Purpose of the Experiment. Fluxes in central carbon metabolism of a genetically engineered, riboflavin-producing Bacillus subtilis strain were investigated in glucose-limited chemostat cultures at low (0.11 h -1 ) and high (0.44 h -1 ) dilution rates. Introduction.

graham
Télécharger la présentation

Purpose of the Experiment

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Purpose of the Experiment • Fluxes in central carbon metabolism of a genetically engineered, riboflavin-producing Bacillus subtilis strain were investigated in glucose-limited chemostat cultures at low (0.11 h -1 ) and high (0.44 h -1 ) dilution rates.

  2. Introduction A metabolic network could be defined as a set of enzymatic reactions that biochemically process metabolites within the cell and transport processes that convert extra-cellular metabolites to intracellular metabolites and vice versa.

  3. E. Coli Metabolic Network The E.Coli metabolic network.

  4. Ultimate Goal Development of complete models simulating every aspect of cellular metabolism Absence of kinetic information on some of the reactions and the dynamics and regulation of metabolic reactions is the major hindrance. Many researchers have tried various approaches towards the ultimate goal with varying degrees of success. Some of them are :

  5. Simulation Models • Biochemical systems theory (Savageau, 1969a, 1969b, 1970), • Metabolic control analysis (Kacser and Burns, 1973), • Temporal decomposition (Palsson et al., 1987). • Pathway analysis (Clark, 1988), • Flux balance analysis (Varma and Palsson, 1994b), • Cybernetic modeling (Ramakrishna et al., 1996;),

  6. Flux Balance Analysis Flux balance analysis has gained a greater degree of acceptance with researchers. • It describes the metabolic physiology in a quantitative manner • It is based on the fundamental law of mass conservation. • It is based on the fundamental physicochemical constraints on metabolic networks. • It only requires information about the stoichiometry of metabolic pathways and on metabolic demands.

  7. Flux Balance Analysis (contd.)

  8. Metabolic Flux Analysis Metabolic flux analysis(MFA) is similar to flux balance analysis (FBA) but instead of the optimization techniques used in FBA experimentally measured/estimated fluxes are used to reduce the underdetermined nature of a metabolic system.

  9. Modeling through MFA • A model was developed through metabolic flux analysis to comprehensively describe the central metabolism of Bacillus subtilis • The model was based on previously developed exhaustive biochemical reactions models developed by Dauner and Sauer

  10. Problems with MFA • A typical problem of metabolic flux analysis using such metabolite balances are underdetermined equation systems. • These are caused by alternative pathways and redundant reactions in central metabolism, an inherent feature of biological systems.

  11. MFA Process • Metabolic net fluxes are determined from an initial, randomly chosen set of parameters, and the corresponding isotopomer balances are calculated. • From this isotopomer distribution, synthetic NMR signals are simulated and compared to the experimentally determined 13C-13C scalar coupling fine structures in cellular amino acids.

  12. Flow chart of flux estimation procedure

  13. System Trying to Model

  14. Model Estimation • Net and exchange fluxes were estimated on the basis of the physiological data from Dauner and Sauer

  15. Initial Basis Fluxes

  16. Determined Exchange Fluxes

  17. References • Metabolic Flux Analysis with a Comprehensive Isotopomer Model in Bacillus subtillis. Michael Dauner, James E. Bailey, Uwe Sauer.Institute of Biotechnology, ETH Zurich, CH 8093 Zurich, Switzerland.

  18. References • Robustness Analysis of the Escherichia coli Metabolic Network. Jeremy S. Edwards and Bernhard O. Palsson. 927 Biotechnol. Prog. 2000, 16, 927-939 • Combining Pathway Analysis with Flux Balance Analysis for the Comprehensive Study of Metabolic Systems. Christopher H. Schilling, Jeremy S. Edwards, David Letscher, Bernhard Ø. Palsson

More Related