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Content Biological pathways and crosstalk Simulation model development

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  1. Lecture 2: Overview of Computer Simulation of Biological Pathways and Network Crosstalk Y.Z. ChenDepartment of PharmacyNational University of SingaporeTel: 65-6616-6877; Email: phacyz@nus.edu.sg ; Web: http://bidd.nus.edu.sg • Content • Biological pathways and crosstalk • Simulation model development • Example: Development of simulation model of RhoA crosstalk to EGFR-ERK pathways • Future perspectives: more pathways, more crosstalk, network level drug effects, signaling specificity, component sensitivity, TCM mechanism

  2. Generic Signaling Pathway Signal Receptor (sensor) Transduction Cascade Targets Response Metabolic Enzyme Cytoskeletal Protein Gene Regulator Altered Metabolism Altered Gene Expression Altered Cell Shape or Motility

  3. Integrated circuit of the cell

  4. EGFR-ERK/MAPK Signaling Pathways

  5. Crosstalk of Rho and Ras

  6. The Multiple Functions of Rho Aznar & Lacal Cancer Lett 165, 1 (2001) Hall Biochem Society Transactions 33, 891 (2005)

  7. Actin Cytoskeleton Regulation Pathways KEGG database

  8. Crosstalk between RhoA and EGFR-ERK/MAPK via MEKK1 and PTEN • RhoA promotes ERK activation by its interaction with Rho kinase, an effector of RhoA, which helps to delay EGF receptor endocytosis by phosphorylating endophilin A1 and to prevent Akt inhibition of Raf by activating phosphatase PTEN that hydrolyzes Akt second messenger PIP3. • RhoA binds to MEKK1 and activate its kinase activity which subsequently phosphorylates and activates MEK1 • As activated MEK1 promotes ERK activation, it is of interest to examine to what extent RhoA can prolong ERK/MAPK activity via this MEKK1-mediated crosstalk between RhoA and EGFR-ERK signaling networks • Gallagher et al. J Biol Chem 2004: 279, 1872

  9. RhoA's crosstalk to EGFR-mediated Ras/MAPK activation via MEKK1

  10. RhoA's crosstalk to EGFR-mediated Ras/MAPK activation via PTEN

  11. Pathway Mathematical Model • Biochemical kinetics based on mass action law (Guldberg and Waage 1864) Fussenegger et al Nature Biotech 18, 768 (2000) Schoeberl et al Nature Biotech 20, 370 (2002) Sasagawa et al Nature Cell Biol 7, 365 (2005) Kiyatkin et al J Biol Chem 281, 19925 (2006)

  12. Pathway Mathematical Model • Biochemical kinetics based on mass action law (Guldberg and Waage 1864) Fussenegger et al Nature Biotech 18, 768 (2000) Schoeberl et al Nature Biotech 20, 370 (2002) Sasagawa et al Nature Cell Biol 7, 365 (2005) Kiyatkin et al J Biol Chem 281, 19925 (2006)

  13. Pathway Mathematical Model • Michaelis-Menton Kinetics (Leonor Michaelis 1875-1947; Maud Menton 1879-1960) • The rate of the reaction is equal to the negative rate of decay of the substate as well as the rate of product formation • Initial concentration of the substrate is much larger than the concentration of the enzyme • Leading to:

  14. Pathway Mathematical Model Materi & Wishart Drug Discov Today 12, 295 (2007)

  15. Pathway Mathematical Model Alderidge et al. Nature Cell Biol 8, 1195 (2006)

  16. Pathway Mathematical Model Alderidge et al. Nature Cell Biol 8, 1195 (2006)

  17. Pathway Mathematical Model Alderidge et al. Nature Cell Biol 8, 1195 (2006)

  18. Solving the Pathway EquationsRunge-Kutta method • Our task is to solve the differential equation: dx/dt = f(t, y), x(t0)= x0 • Clearly, the most obvious scheme to solve the above equation is to replace the differentials by finite differences: dt = h dx = x(t+h) - x(t) • One can then apply the Euler method or first-order Runge-Kutta formula: x(t+h) = x(t) + h f(t, x(t)) + O(h2) • The term first order refers to the fact that the equation is accurate to first order in the small step size h, thus the (local) truncation error is of order h2. The Euler method is not recommended for practical use, because it is less accurate in comparison to other methods and it is not very stable.

  19. Solving the Pathway EquationsRunge-Kutta method • The accuracy of the approximation can be improved by evaluating the function f at two points, once at the starting point, and once at the midpoint. This lead to the second-order Runge-Kutta or midpoint method: k1 = h f(t, x(t)) k2 = h f(t+h/2, x(t)+k1/2) x(t + h) = x(t) + k2 + O(h3) • The most popular Runge-Kutta formula is the fourth-order one: k1 = h f(t, x(t)) k2 = h f(t+h/2, x(t)+k1/2) k3 = h f(t+h/2, x(t)+k2/2) k4 = h f(t+h, x(t)+k3) x(t + h) = x(t) + k1/6 + k2/3 + k3/3 + k4/6 + O(h5)

  20. Solving the Pathway EquationsCash-Karp embedded Runge-Kutta algorithm

  21. Mathematical Model of EGFR-ERK/MAPK Pathway • Interaction equations and kinetic parameters

  22. Mathematical Model of EGFR-ERK/MAPK Pathway • Interaction equations and kinetic parameters

  23. Mathematical Model of EGFR-ERK/MAPK Pathway • Analysis of kinetic parameters

  24. Mathematical Model of EGFR-ERK/MAPK Pathway • Analysis of kinetic parameters

  25. Mathematical Model of EGFR-ERK/MAPK Pathway • Analysis of kinetic parameters

  26. Validation of RhoA EGFR-ERK/MAPK Crosstalk Model • Time-dependent behavior of EGF activation of ERK in PC12 cells • Our model predicted that ERK activation peaks at ~5 minutes and decays within ~50 minutes, in good agreement with observation

  27. Validation of RhoA EGFR-ERK/MAPK Crosstalk Model • EGF variation on duration of ERK activation in PC12 cells • Our model predicted that further increase of EGF levels leads to sustained ERK activation, in good agreement with observation and previous simulation results

  28. Validation of RhoA EGFR-ERK/MAPK Crosstalk Model • Time-dependent behavior of active RasGTP and their effects on ERK activation in PC12 cells • Our model predicted that RasGTP peaks at ~2.5 minutes and quickly decays to its basal levels within 20 minutes, in good agreement with observation and previous simulation results

  29. Validation of RhoA EGFR-ERK/MAPK Crosstalk Model • Time-dependent behavior of active RasGTP and their effects on ERK activation in PC12 cells • Our model predicted that Ras over-expression prolongs ERK activation by delaying its decay rate without altering the time cause for reaching the peak of activation, in good agreement with observation and previous simulation results

  30. Validation of RhoA EGFR-ERK/MAPK Crosstalk Model • Effect of scaffold protein MEKK1 on ERK activities • Our model predicted that Increased MEKK1 concentration helps to increase the level of active ERK, delay its peak time, and slightly prolong the duration of ERK activation, in good agreement with observation

  31. Validation of RhoA EGFR-ERK/MAPK Crosstalk Model • Effects of Ras over-expression on RhoA and ERK activities • Our model predicted that Ras over-expression increases the amount of active GTP-bound RhoA and prolongs the duration of its activation, leads to sustained ERK activation, in good agreement with observation and previous simulation results

  32. Effects of RhoA over-expression on ERK activation • When Ras expression is at the normal level, RhoA over-expression was found to prolong ERK activation in a dose-dependent manner

  33. Effects of RhoA over-expression on ERK activation • Effect of scaffold

  34. Effects of RhoA over-expression on ERK activation • When Ras is over-expressed, RhoA over-expression significantly reduces the number of active ERK while further prolonging its activation

  35. Future Work: Other Pathways KEGG database

  36. Pathways and Disease • Mapping normal and cancer cell signalling networks: towards single-cell proteomics Nature Rev Cancer 6, 146, 2006

  37. P Future Trend: More crosstalk e.g. crosstalk of EGFR-ERK pathway to others via RTK - PI3K – AKT pathways

  38. Future Trend: Network level drug effects e.g. drug combinations in RTK-ERK and RTK - PI3K – AKT pathways P Annals Oncology 18, 421 (2007) Drug metabolism pathway simulation published in PloS Comput Biol 3, e55 (2007)

  39. Future Trend: Knowledge learned and information gained be used for studying TCM

  40. Project Assignment • Project 1: Development of pathway simulation models • You will be given a section of a biological pathway. You are required to try to generate a more detailed and precise pathway section, derive the corresponding pathway equations and parameters, and implement the equations in an ODE solver • Project 2: Mechanism of biological crosstalks • You will be given a few papers of crosstalks between different biological entities. You are required to probe the mechanism of each of these crosstalks based on their network relationships (generated by Pathway studio), expression profiles (generated by microarray analysis), and biochemical or regulatory profiles (from literature reports)