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Computational Modeling For The G-protein Cycle

Computational Modeling For The G-protein Cycle. Yifei Bao 1 , Adriana B. Compagnoni 1 , Tommy E. White 2 , Joseph S. Glavy 2 Department of Computer Science 1 , Department of Chemistry, Chemical Biology and Biomedical Engineering 2 , Stevens Institute of Technology. Overview.

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Computational Modeling For The G-protein Cycle

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  1. Computational Modeling For The G-protein Cycle Yifei Bao1, Adriana B. Compagnoni1, Tommy E. White2, Joseph S. Glavy2Department of Computer Science1, Department of Chemistry, Chemical Biology and Biomedical Engineering2, Stevens Institute of Technology

  2. Overview • G-protein-coupled Receptor • G-protein Cycle • Ordinary Differential Equations (ODEs) Modeling • Stochastic Pi-Calculus Modeling • Petri Nets Modeling • Kappa Language Modeling • High Level Notation NJPLS2010@Stevens

  3. G-Protein Couple Receptors(GPCRs) • G-protein couple receptors (GPCRs) sense molecules outside the cell and activate inside signal transduction pathways. • An estimated 50% of the current pharmaceuticals target GPCRs. NJPLS2010@Stevens

  4. Activation cycle of G-proteins by G-protein-coupled receptors 2 1 3 5 4 NJPLS2010@Stevens

  5. Reactions and Rates NJPLS2010@Stevens

  6. The law of mass action • The law of mass action is a mathematical model prescribes the evolution of a chemical system in terms of changes of concentrations of the chemical species over time. • In its simplest form, it says that a reaction X+Yk Z has a rate k[X][Y]: the rate is proportional to the concentration of one species times the concentration of the other species by the base rate k. NJPLS2010@Stevens

  7. Ordinary Differential Equations (ODEs) NJPLS2010@Stevens

  8. Process Algebras • Formal languages originally designed to model complex reactive computer systems. • Because of the similarities between reactive computer systems and biological systems, process algebra have recently been used to model biological systems. NJPLS2010@Stevens

  9. Process Algebras • Typically two halves of a communication: Sending and Receiving. • !ch(msg) : to send message msg on channel ch. • ?ch(msg) : to receive a message msg on channel ch. NJPLS2010@Stevens

  10. Process Algebras • A and B bind • A and B dissociate A = new msg !ch (msg); Ab(msg) B = ?ch(msg); Bb (msg) Ab(msg) = !msg(); A Bb(msg) = ?msg(); B NJPLS2010@Stevens

  11. SPiM The stochastic pi-calculus is a process algebra where stochastic rates are imposed on processes, allowing a more accurate description of biological processes. SPiM (stochastic pi-calculus machine) is an implementation of the stochastic pi-calculus that can be used to run in-silico simulations that display the change over time in the populations of the different species of the system being modeled. NJPLS2010@Stevens

  12. Step 1 to Step 2 2 1 !bindb Gd G ?bindb Gbg NJPLS2010@Stevens

  13. Process modeling for G-protein Cycle NJPLS2010@Stevens

  14. Process modeling for G-protein Cycle NJPLS2010@Stevens 14

  15. Process modeling for G-protein Cycle NJPLS2010@Stevens 15

  16. Process modeling for G-protein Cycle NJPLS2010@Stevens 16

  17. Process modeling for G-protein Cycle NJPLS2010@Stevens 17

  18. Process modeling for G-protein Cycle NJPLS2010@Stevens 18

  19. SPiM directive plot RL(); abrD(); aT() NJPLS2010@Stevens

  20. Petri Nets Modeling • The basic Petri Net is a directed bipartite graph with two kinds of nodes which are either places or transitions and directed arcs which connect nodes. In modeling biological processes, place nodes represent molecular species and transition nodes represent reactions. We use Cell Illustrator to develop our model of the G-protein cycle (www.cellillustrator.com). NJPLS2010@Stevens

  21. Petri Nets Modeling NJPLS2010@Stevens

  22. Petri Nets Modeling NJPLS2010@Stevens

  23. Petri Nets Modeling NJPLS2010@Stevens

  24. Petri Nets Modeling NJPLS2010@Stevens

  25. Petri Nets Modeling NJPLS2010@Stevens

  26. Petri Nets Modeling NJPLS2010@Stevens

  27. Kappa Language Modeling • Kappa is a formal language for defining agents (typically meant to represent proteins) as sets of sites that constitute abstract resources for interaction. It is used to express rules of interactions between proteins characterized by discrete modification and binding states. The Kappa language modeling platform is Cellucidate. NJPLS2010@Stevens

  28. Kappa Language Modeling R + L -> RL R(r), L(l)->R(r!1), L(l!1) @ 3.32e-09 • R and L are agent names, r is the binding site of R, l is the binding site of L, and 3.32e-09 is the reaction rate. In R(r!1) and L(l!1), 1 is the index of the link that binds R and L at their binding sites. NJPLS2010@Stevens

  29. Kappa Language Modeling NJPLS2010@Stevens

  30. Result (ODEs and Pi-calculus) NJPLS2010@Stevens

  31. Result (Petri Nets and Kappa) NJPLS2010@Stevens

  32. High Level Notations (motivation) • Both ODEs and Petri Nets correspond closely to chemical reactions, and for the average biologist, they are relatively easy to understand. • Cellucidate provides a friendly user interface for Kappa that abstracts away from its syntax. • SPiM still needs encoding in the stochastic Pi-calculus. NJPLS2010@Stevens

  33. High Level Notations (motivation) • In order to hide Pi-calculus communication primitives and enable modeling using only terminology directly obtained from biological processes, we develop a high level notation that can be systematically translated into SPiM programs. NJPLS2010@Stevens

  34. G-protein Cycle 2 bind bind 1 3 activate and dissociate hydrolyze 5 4 dissociate NJPLS2010@Stevens 34

  35. High Level Notations NJPLS2010@Stevens

  36. Step 1 to Step 2 2 1 bind(Gd, Gbg, G, 1.0) NJPLS2010@Stevens 36

  37. High Level Notations • bind(Gd, Gbg, G, 1.0); • bind(R, L, RL, 3.32e-6); • activateAnddissociate(G, RL, Ga, Gbg, 1.0e-5); • dissociate(RL, R, L, 0.01); • hydrolyze(Ga, Gd, 0.11); • degrade(R, 4e-4); • degrade(RL, 4e-3) NJPLS2010@Stevens

  38. High Level Notations NJPLS2010@Stevens

  39. Conclusions • The models we build using stochastic modeling approaches can represent the G-protein cycle quite convincingly, which shows that stochastic modeling approaches could be efficient instruments to assist in biomedical research. • In fact, because of the randomness of dynamic biological systems, stochastic modeling approaches can make the description of biological process much simpler and more accurate. NJPLS2010@Stevens

  40. Conclusion • The high level notation that we designed is a domain specific notation that we developed for the G-protein cycle. However extending it to capture a larger collection of biological interactions is the subject of current research. NJPLS2010@Stevens

  41. Special Thanks to: Prof. Adriana Compagnoni, Prof. Joseph Glavy, Tommy White, Vishakha Sharma, Amanda DiGuilio NJPLS2010@Stevens

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