1 / 19

Quantitative Modeling of Metabolic Networks

Quantitative Modeling of Metabolic Networks. Sai Jagan Mohan, Ph.D. Sonali Das, Ph.D. Anupama Bhat. Problem definition and approach Modules The glutathione module The bioenergetics module Complementary modeling approaches Constraint based modeling Metabolic control analysis (MCA)

Antony
Download Presentation

Quantitative Modeling of Metabolic Networks

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. Quantitative Modeling of Metabolic Networks Sai Jagan Mohan, Ph.D. Sonali Das, Ph.D. Anupama Bhat.

  2. Problem definition and approach Modules The glutathione module The bioenergetics module Complementary modeling approaches Constraint based modeling Metabolic control analysis (MCA) Summary Overview

  3. Genetic Drug / Dose Physiology / Disease Hepatotoxicity prediction is hard Metabolism Hepatotoxicity intricate and dynamic 'system-level' interactions.

  4. Our Approach A comprehensive model of homeostasis metabolism in a liver cell Toxicity Drug-induced perturbations Hepatotoxicity: Mechanisms • Cell Death of Functional Liver Cells • Impaired Bile Flow • Faulty Fat Processing

  5. The glutathione metabolism module The bioenergetics module Cytotoxicity Modules • Characteristics • Non-linear ODE’s • Two compartments • Fluxes: Enzyme Kinetics/Mass Action

  6. Enzymes => Non-linearity • VGCS = • Vmax{[ATP][Glu][Cys]/KmATPKmGlu(1+[GSH]/KiGSH)KmCys} • {1+[Glu]/KmGlu(1+[GSH]/KiGSH) + [Glu] [Cys]/KmGlu(1+[GSH]/KiGlu)KmCys+[Glu][ATP]/KmATPKmGlu(1+[GSH]/KiGSH) + [Glu][Cys][ATP]/KmATPKmGlu(1+[GSH]/KiGSH) KmCys}

  7. Vmax* [S] Venzyme= KM + [S] Perturbations Vmax[Enzyme]T

  8. The Oxidative Stress Module

  9. The Glutathione Module d [-GC] /dt = VGCS – VGS d [GSH] /dt = VGS + VGR – VGPx – Vgsh2ss –Vgsh2ca –VGST – VgshC2M –VgshM2C

  10. Simulation 1 2 3 Validation Drug: Ethacrynic Acid (EA) Experimental 2 3 1

  11. Asymptotic Analysis Simulation: Vgcs= 0 Validation Toxin: Buthionine Sulfoximine (BSO) Target: -GlutamylCysteine Synthetase (GCS) Depletes glutathione with a half –life of ~ 2 hours

  12. Thought simulation

  13. Malate-Aspartate shuttle NADH NAD Energy Utilisation PFK ATP ADP+Pi ANT NADH NAD Glycolysis OXPHOS TCA cycle ADP+Pi ATP ADP+Pi ATP NADH NAD Mitochondria MAL MAL- mito Cytosol Metabolic Network for Cellular Energetics 21 state variables 17 differential equations 4 conservation laws

  14. ADK 2ADP ATP + AMP Keq Conservation Laws Adenylate kinase (ADK) reaction is rapid (operates near equilibrium) ATP*AMP constant Keq = = ADP*ADP Total adenine pool in the cytosol = ATPe+ ADPe+AMPe= constant (Ataullakhanov & Vitvitsky Bioscience Reports. 2002 22:501-511)

  15. Constraint Based Modeling

  16. Metabolic Control Analysis (MCA)

  17. MCA for insights into control and regulation Parameter estimation Experimental validation Scaling laws for metabolic networks Future Work

  18. The Linear Approximation

  19. SS continuation analysis Parameter : VmaxGS Asymptotic Simulation VGCS = 0 Asymptotic Simulation VGS = 0 Homeostasis The Linear Approximation

More Related