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Lecture #10

Lecture #10. Metabolic Pathways. Outline. Glycolysis; a central metabolic pathway Fundamental structure (m x n = 20 x 21) Co-factor coupling (NAD, ATP, P i ) The stoichiometric matrix Its null spaces Setting up a simulation model Steady state Interpreting the results from simulation

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Lecture #10

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  1. Lecture #10 Metabolic Pathways

  2. Outline • Glycolysis; a central metabolic pathway • Fundamental structure (m x n = 20 x 21) • Co-factor coupling (NAD, ATP, Pi) • The stoichiometric matrix • Its null spaces • Setting up a simulation model • Steady state • Interpreting the results from simulation • Concentrations, fluxes, pools, ratios

  3. GLYCOLYSIS: AN OPEN SYSTEM

  4. Glycolysis as an Open System

  5. Compounds: the nodes pathway intermediates cofactors carriers

  6. Reactions: the links

  7. THE STEADY STATE

  8. The Stoichiometric Matrix 3 pathways dim(Null)=21-18=3 dim(Left Null)=20-18=2 mxn=20x21, Rank(S)=18 2 conserved moieties

  9. Glycolysis: ‘annotated’ S matrix ES=0

  10. Glycolysis: Pathways in Null(S)Selected basis based on biochemical intuition ~P synthesis redox coupling inventory of AMP

  11. The Steady State Fluxes (mM/hr):fluxes have to balance the network upper glycolysis lower glycolysis AMP exchange & demand fluxes

  12. The Steady State Concentrations (mM);determined by flux map and kinetic constants

  13. Reactions: the links pseudo elementary rate constants distance from equilibrium

  14. Model defined and ready for: DYNAMIC SIMULATION

  15. Simulation: 50% increase in kATP: dynamic responses of the concentrations • Key Concepts • Time constants • Pools • Transitions ADP ATP 50% increase at t=0 load=k[ATP]

  16. Tiled Phase Portrait: fluxes Glycolysis: 0-10 mins fluxes of interest

  17. Tiled Phase Portrait: fluxes Glycolysis: 10-infinity mins fluxes of interest

  18. Dymamic Responses of the Fluxes drain accumulation Secretion > stst Secretion <stst Secretion > stst Secretion <stst accumulation drain

  19. Tiled Phase Portrait: concentrations Glycolysis: 0-10 mins

  20. Tiled Phase Portrait: concentrations Glycolysis: 10-infinity mins

  21. Simulation: 50% increase in kATP: dynamic responses of the concentrations • Key Concepts • Time constants • Pools • Transitions ADP ATP 50% et=0 load=k[ATP] fast intermediate slow

  22. Towards systems biology STRUCTURAL PROPERTIES

  23. Glycolysis: the system with symbolic representation

  24. Structural Properties: redox trafficking in glycolysis (#): Redox value #: Flux value

  25. Structural Properties: high-energy bond trafficking in glycolysis x: Flux value (#): High-energy bond “value”

  26. Structural Properties: The Trafficking of Phosphate Groups in Glycolysis “through” “cycle”

  27. Pools:from structural properties

  28. Redox Value of Intermediates reduce glycolytic intermediates metabolites oxydized glycolytic intermediates carrier

  29. Energy Value of Intermediates

  30. Phosphate Bond Trafficking Incorporation: Recycled: Recycle ratio:

  31. Pool Map:shows their interconnections and steady state concentrations by area of square

  32. Dynamic Responses of the Pools capacity 2(ATP+ADP+AMP) occupancy 2ATP+ADP GP+ and GP- Pi ~Pi

  33. Towards physiology RATIOS

  34. Dynamic Responses of the ratios Adenosine E.C Glycolytic E.C Phosphate recycle ratio

  35. Property rations or charges and their dynamic responses

  36. Summary • First draft dynamic models can be obtained from using measured concentration values, elementary reactions, and associated mass action kinetics. • This first draft can be used as a scaffold to build more complicated models that include regulatory effects and interactions with other pathways. • Dynamic simulation can be performed for perturbation in environmental parameters and the responses examined in terms of the concentrations and the fluxes. • A metabolic map can be analyzed for its stoichiometric texture to assess the co-factor coupling • Such breakdown of the biochemistry helps define pools that are physiologically meaningful from a metabolic perspective, and are context dependent.

  37. Summary • The raw output of the simulation can be post processed with a pooling matrix that allows the pools and their ratios to be graphed to obtain a deeper interpretation of dynamic responses. • Some of the responses are built into the topological features of a network and require no regulatory action. • The identification of the reactions that move the key pools is possible by the use of the stoichiometric matrix.

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