1. Where Computer Science meets Biology Systems Biology Professor Adriana Compagnoni Computer Science Department With: Joe Glavy, Svetlana Sukhishvili, Tommy White Amanda DiGuilio (CCBBME) Yifei Bao, Vishakha Sharma, Justin Sousa, and Peter Zafonte (CS)

2. Overview The Virtual Lab Modeling HER2 Modeling Bio-films Experimenting with 5 different modeling techniques Modeling the G-protein Cycle Incorporating Space

3. Process model for HER2 Signaling Pathways

4. HER2

5. HER2 – Signaling Pathways  HER signaling is initiated when two receptor molecules join together in a process called dimerization, which occurs in response to the presence of a growth factor molecule (ligand).  Dimerization of different members of ErbB family, lead to activation of different kinase mediated intracellular signaling cascades, like  PI3 K/AKT pathway  MAPK pathway  JAK/STAT pathway

6. Process Algebra Model

7. Overview of different species

8. Differential HER2 concentrations a. 100 HER2 are initializeda. 30 HER2 are initialized

9. Inhibition of Raf activation(activation rate is changed from 27.0 to 1.0E-4 ) Pathways Crosstalk

10. Computational Modeling of Bio-films for Drug Delivery

11. Hydrogen-bonded multilayer destruction Changing pH 3.2 μm fast release of capsule cargo

12. Computational Modeling For The G-protein Cycle 12 Using 5 different modeling techniques: Differential equations Stochastic Pi-calculus Stochastic Petri-Nets Kappa Bio-Pepa

13. G-Protein Couple Receptors(GPCRs) 13 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.

14. Activation cycle of G-proteins by G- protein-coupled receptors 14 1 2 3 4 5

15. Chemical Reactions and Rates 15

16. The law of mass action 16 The law of mass action is a mathematical model that 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 ([X]) times the concentration of the other species ([Y]) by the base rate k.

17. Ordinary Differential Equations (ODEs) 17

18. Process Algebras An alternative ot ODEs  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. 18

19. 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. 19

20. Process Algebras  A and B bind  A and B dissociate 20 A = new msg !ch (msg); Ab(msg) B = ?ch(msg); Bb (msg) Ab(msg) = !msg(); A Bb(msg) = ?msg(); B

21. Stochastic Pi-Calculus  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 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.

22. Step 1 to Step 2 22 1 2 Gd Gbg G !bindb ?bindb Gd represents alpha Gbg represents the beta-gamma complex

23. Process modeling for G-protein Cycle 23

24. Process modeling for G-protein Cycle 24

25. Process modeling for G-protein Cycle 25

26. Process modeling for G-protein Cycle 26

27. Process modeling for G-protein Cycle 27

28. Process modeling for G-protein Cycle 28

29. SPiM directive plot RL(); abrD(); aT()

30. 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). 30

31. NJPLS2010@Stevens Petri Nets Modeling

32. NJPLS2010@Stevens Petri Nets Modeling

33. NJPLS2010@Stevens Petri Nets Modeling

34. NJPLS2010@Stevens Petri Nets Modeling

35. NJPLS2010@Stevens Petri Nets Modeling

36. NJPLS2010@Stevens Petri Nets Modeling

37. 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.

38. Kappa Language Modeling 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. R + L -> RL R(r), L(l)->R(r!1), L(l!1) @ 3.32e-09

39. Kappa Language Modeling

40. Bio-PEPA Modeling  Bio-PEPA is a process algebra for the modeling and the analysis of biochemical networks. It is a modification of PEPA to deal with some features of biological models, such as stoichiometry and the use of generic kinetic laws.

41. Bio-PEPA Modeling

43. Result (ODEs and Pi-calculus)

44. Result (Petri Nets and Kappa)

45. Result (Bio-PEPA)

46. 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.

47. High Level Notation (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.

48. G-protein Cycle 48 1 2 3 4 5 bind activate and dissociate dissociate hydrolyze

49. Step 1 to Step 2 49 1 2 bind(Gd, Gbg, G, 1.0)

50. High Level Notation  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)

51. High Level Notation

53. We are currently modifying the Ocaml implementation of SPiM to Add a notion of Space (Affine Geometry + Process Algebra) Incorporating Space

54. 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.

55. Conclusion  The high level notation that we designed is a domain specific notation that we developed for the G-protein cycle.

56. Thank you! Questions?

57. 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 57