1. Efficient Modeling of Excitable Cells Using Hybrid Automata Radu Grosu SUNY at Stony Brook Joint work with Pei Ye, Emilia Entcheva and Scott A. Smolka

2. Talk Outline 1.Biological Background 2.Motivation 3.Computational Background 4.Hybrid Automata 5.HA Models of Excitable Cells 6.Simulation Results 7.Conclusions & Future Work

3. Main Goal Computational Efficiency: –Making large-scale simulation practical Formal Analysis (in the future): –Reachability –Safety –Liveness

4. Background Excitable cells –Neurons –Cardiac myocytes –Skeletal muscle cells Different concentrations of ions inside and outside of cells form: –Trans-membrane potential –Ion currents cross the cell membrane through channels

5. Squid Giant Axon (Animation from Marine Bilogical Laboratory, MA) 1. Squid at rest. 2. Mantle opens. Water enters the mantle cavity. 3. A signal from the brain is sent to the stellate ganglion which is connected to nerve cells (axons) distributed through the mantle. 4. Nerve impulses travel the length of these axons. 5. The muscles contract synchronously, rapidly closing the mantle. 6. Water is forced out through the siphon, producing a jet action.

6. Cardiac Myocytes (WorldWide Anaesthetist & Univ. of British Columbia) Cardiac Myocytes Gap Junctions Action Potential Propagation

7. 2D Simulations of Atrial Fibrillation (Kneller et al., McGill) Single Spiral WaveFast Spiral Wave Spiral Wave BreakupAtrial Fibrillation

8. Motivation (Hofstra University, NY) 1 million deaths annually caused by cardiovascular disease in US alone, or more than 40% of all deaths. Almost 25% of these are victims of ventricular fibrillation (VF). During VF, normal electrical activity of heart is masked by higher frequency activation waves, leading to small and out-of-phase localized contractions.

9. Mathematical Models Hodgkin-Huxley (HH) model –Membrane potential for squid giant axon –Developed in 1952 –Framework for the following models Luo-Rudy (LRd) model –Model for cardiac cells of guinea pig –Developed in 1991 Neo-Natal Rat (NNR) model –Being developed in Stony Brook University by Emilia Entcheva et al.

10. Who? Alan Lloyd Hodgkin *1914 +1998 Andrew Fielding Huxley *1917 Nobel Preis for Physiology or Medicine in 1963 "for their discoveries concerning the ionic mechanisms involved in excitation and inhibition in the peripheral and central portions of the nerve cell membrane"

11. - Membrane acts like a capacitor - Discharge creates an AP - Channels control the potential Ion channels Ion pumps Active Membrane (BiologyMad.com)

12. Active Membrane In an Active Membrane, some Conductances vary with respect to time and the membrane potential NaKL Inside Outside Na + K+K+

13. Action Potential (HyperPhysics, Georgia State University)

14. Action Potential Propagation (BiologyMad.com)

16. myelin stealth Ranvier nodes (ion channels only) axon nerve cell Action Potential Propagation (BiologyMad.com)

17. Currents in an Active Membrane V Inside Outside I st I Na g Na gKgK gLgL C ILIL ICIC IKIK V Na VLVL VKVK

18. The Potasium Channel (Pictures from B. Babadi, Univ. of Teheran) A subunit can be either “open” or “closed”. Channel is open iff all 4 subunits are open. Has four similar slow subunits.

19. Kinetics of Potasium Subunits (Pictures from B. Babadi, Univ. of Teheran)

20. The Sodium Channel (Pictures from B. Babadi, Univ. of Teheran) Has three similar fast subunits and a single slow subunit. mm mh

21. The Full Hodgkin-Huxley Model

22. Hodgkin-Huxley Model in Action (Applet of A. Fodor, Stanford)

23. Hybrid Automata (HA) (Alur, Henzinger, Sifakis and others) Combine both –Continuous behavior (Differential Equations) –Discrete transitions Advantages –Simplicity –Rich descriptive ability

24. Hybrid Automata (HA) HA consists of: Variables; Control graph having modes, switches; Predicates init, inv, flow for each mode; Jump conditions and Events for each switch. Simple Thermostat example: On x = 5 - 0.1x [x ≤ 22] Off x = - 0.1x [x ≥ 18] [x>21] [x<19] [x=20]

25. Stimulated General HA Template

26. Assumptions for the Flows 1.Each mode corresponds to an open/closed configuration of the gates. Gate dependence on V is factored into the modes. 2.Sodium and potasium gates (conductances) are mutually independent of each other. 3.Gate (conductance) behavior within a mode is given by a linear differential equation A step function approximation is too crude.

27. Assumptions for the Flows Problem: Equation (3) is nonlinear. Solution: Assume the inward ( I Na ) and outward ( I K + I L ) currents are linear!

28. Is this justified? (Applet of A. Fodor, Stanford)

29. Assumptions for the Flows Hence: Now take:Then:

30. HA for HH Model

31. Simulation of HH Model

32. Restitution Property (Frequency Response)

33. New Features for HA Models 1.Capture dependence on the Ca 2+ ion: –Add new flow variable v z 2.Capture restitution nonlinearity: –Add new state variable v n remembering voltage value when stimulus occurs. –Adjust AP slope with cycle constant f: –Adjust AP height & duration with constants g, h:

34. HA for NNR Model

35. Simulation for LRd Model

36. Simulation for NNR Model Single cell, single AP 3 APs on a 2*2 cell array

37. Large-scale Spatial Simulation for NNR Model Re-entry on a 400*400 cell array

38. Performance Comparison Run on a Pentium® 4 CPU 3.00GHz, 1G Memory machine

39. Conclusion Cell excitation used to be modeled by ODE systems –Hodgkin-Huxley –Luo-Rudy –Neo-Natal Rat Hybrid automata approach combines –Differential equations –Discrete mode switches Simulation by using Hybrid automata –Accurate –Efficient –Easily extended to other complex biological systems

40. Future Work Use optimization techniques to automatically derive HA model parameters. Develop simpler spatial model to further improve efficiency (FDM vs. FEM). Formal analysis: ventricular fibrillation as a reachability property. Long-term work: improved pacemaker/defibrillator technology, communicate with prosthesis robots.

41. Transmission of a nerve impulse

42. channel Ions and Channels of Excitable Cells Na + Cell K+K+ K+K+ K+K+ K+K+ Ca 2+ Na + Ca 2+ K+K+ K+K+ K+K+ Na + K+K+ K+K+ K+K+ Ca 2+ Na +

43. The Giant Axon of Squid

44. Action Potential (AP) Caused by ion fluxes - inward (Na+, Ca2+) and outward (K+) 5 stages –Resting –Upstroke –Early Repolarization –Plateau –Final Repolarization

45. Restitution Property Excitable cells respond differently to stimuli with different frequency. Each cycle is characterized by : –Action Potential Duration (APD) –Diastolic Interval (DI) Longer DI, longer APD

46. Hodgkin-Huxley Model C: Cell capacitance V: Trans-membrane voltage g na, g k, g L : Maximum channel conductance E na, E k, E L : Reversal potential m, n, h: Ion channel gate variables I st : Stimulation current

47. Two Ways of Abstraction Rational method: derive the flow functions from the differential equations in the original model Empirical method: use curve-fitting techniques to get the flow functions with the form chosen (here we use the form ).

48. General HA Template 4 control modes: –Resting and Final repolarization (FR) –Stimulated –Upstroke –Early repolarization (ER) and Plateau Threshold voltage monitoring mode switches –V o, V T and V R Event V S represents the presence of stimulus

49. HA for LRd Model

50. New Features of HA for LRd and NNR Model Add v z to capture dependence on the Ca 2+ ion Use v n to remember the current voltage when the next stimulus occurs. – determines the time cell stays in mode ER and plateau –Thus, APD will change with DI For NNR model, define and thus the threshold voltages are also influenced by DI.