1. Modeling T-Cell Activation Using Visual Formalism by Evren SAHIN 11/26/2002

2. Outline Introduction What is this Biological System? Statecharts Model Results and Conclusion

3. Introduction From analysis (finding building blocks) To synthesis (integration of parts) requires language of math ;since The definition of reactive systems suits biological systems at different levels… Specially cell biology, immunology

4. Introduction Visual formalism provides a clarity powerful statecharts purpose: “Statechart visual formalism on modeling and analysis of a biological system”

5. What is this Biological System? Basically, immune system so called T-Cell Activation (coming soon) Why modeling Cell  combination of inner states Signal transductions change cell’s state

6. Cell Cycle G0: "resting" stage the cell is not actively dividing G1: Gap 1 & Gap 2 protein and RNA synthesis growth and preparation of the chromosomes for replication S: Synthesis the period of DNA synthesis M: Mitosis D: Division nucleus and then the rest of the cell divides

7. Cell Cycle An Interactive Animation from www.cellsalive.com

8. T-Cell Activation T cell: A type of white blood cell that is of crucial importance to the immune system - protecting you from viral infections; helping other cells fight bacterial and fungal infections; producing antibodies; fighting cancers; and coordinating the activities of other cells in the immune system. Naive T-Cells live many years without dividing Trying to sense changes in body Sensing through interaction with antigen-presenting cells GETTING READY TO THE BATTLE….

9. T-Cell Activation “Naive T-Cells that recognize their specific antigen on the surface of a professional antigen presenting cell cease to migrate, and are activated to proliferate and differentiate into armed effector cells. The Signaling Process: 1. Initial encounter + (presence of co-stimulatory signal)   T-Cell to G1  Synthesis of IL-2  a protein causing infection-fighting cells multiply and mature  Synthesis of IL-2 receptor 2. Binding of IL-2 to the receptor  Triggers progression through rest of the cycle Proliferation

10. Raising questions: Where is cell? What other entities does it encounter? Which kind of signals does it receive? What kind of outputs does it produce? An adequate modeling language How to differentiate between outside must solve all these and must be and inside signals? clear as much as possible! How to focus on different levels of this process? How to describe dependent and independent states of T-cell?

11. STATECHARTS: A Visual Formalism For Complex Systems. Very much fits to our needs. extends conventional diagrams with essentially hierarchy concurrency Communication compact & expressive viewing the description at different levels event-driven (continuously having to react external and internal stimuli ) Clear & Realistic Formal and rigorous

12. STATECHARTS: A Visual Formalism For Complex Systems. States and events for describing dynamic behavior State-transition diagrams : formal mechanism However; too naive for a complex system unmanageable, exponential grow up unrealistic etc…

13. STATECHARTS: A Visual Formalism For Complex Systems. To be useful; modular, hierarchical, well-structured solving exponential blow-up by relaxing on “showing every state explicitly” cluster states into super-state / refining super-state into states orthogonality need for general transitions. Remember the things to model T-Cell Activation

14. Clustering capturing depth and hierarchy arrows can originate and terminate at any level economizes arrows arrows labeled with events; plus optional conditions D = A XOR C

15. Refinement clustering was bottom-up reverse (top-down) refines abstractions zoom-in zoom out

16. Entrances suppose A is default to enter A,B,C group possible representations:  (iii)  D is default among D,B and A is default among A,C For Figure 1 For Figure 2For Figure 2(alternative)

17. Conditional Entrances entering a group of states through a condition

18. Orthogonality clustering was XOR orthogonality: AND Decomposition capturing the property: being in a state, the system must in all of its AND components dashed lines Y is orthogonal product of A and D entering Y  entering combination of (B,F)

19. Orthogonality- cont. event a(alpha) triggers B  C and F  G simultaneously (B,F)  (C,G) synchronization event u(mu) triggers only F  E independence

20. Orthogonality- cont. AND-free equivalent has 6 states (2*3) with two components each having 1000 states??? exponential blow-up orthogonality is the way to avoid it

21. STATECHARTS There are many more features of STATECHARTS But, I guess it is better not to get stuck with these details and turn back to our actual goal: creating a clear, visual model of T-Cell Activation.

22. Dynamics of T-Cell Activation Here is a basic model of the activation.

23. Dynamics of T-Cell Activation 3 orthogonal components of behavior of T-Cell Immunological State (Active or not) Phase in the cell cycle Anatomical location

24. Dynamics of T-Cell Activation Immunological State Active and non-Active no cell cycle  no proliferation has occurred  T-Cell still Naïve otherwise  active division process  newly born cell is active progeny of an activated parent

25. Dynamics of T-Cell Activation non-Active 1) Naive  no meet by an antigen 2) Standby  met antigen 3) Anergic  could not get co- simulation 4) Memory  resting after clearing antigen 5) IL2 Production  produce IL-2 and its binder. Binding leads full activation Active Proliferation (in cell cycle) Differentiation (here)

26. Dynamics of T-Cell Activation Cell Cycle Control Naive  G0 Initial encounter with specific antigen in presence of co-stimulatory signal From GO to G1 by triggering EnterCellCycle() Binding leads to S stage (also as stated earlier leads active state) If no death signal while in S  rest of cycle depending on timeout

27. Results To check whether the formal representation of the model fulfills the requirements from immunological data, model executed on Rhapsody tool. It can translate the model to executable code and then animate the statecharts. (not in paper) came up with an unexpected behavior could not reach steady Memory state when from Active to Memory, right back to Active IL-2 molecules still in the system only after an extensive search in literature found that Active to Memory down-regulates IL-2 Receptor

28. Conclusion/Comment simulations allow us compare model dynamics with actual experimental data so; modeling with powerful techniques such as statecharts can help in finding open questions in biology that can not be addresses in standard laboratory conditions alone.

29. Thanks for your attention. Any Question?