“ Dynamical” Vs. “Genetic” Disease: What Do Complex Rhythms Reveal About Pathophysiology?

1. “Dynamical” Vs. “Genetic” Disease: What Do Complex Rhythms Reveal About Pathophysiology? Leon Glass Isadore Rosenfeld Chair in Cardiology McGill University, Montreal Quebec, Canada

2. Genetic Disease (40,000 hits) • Garrod (1908) – Inborn errors of metabolism show Mendelian inheritance • Pauling et al. (1949) – Sickle cell anemia: a molecular disease • Ingram (1956) – Chemical difference between normal human and sickle cell anemia hemoglobin

3. Genetic Disease: Implications for Research • Identify abnormalities • Map abnormalities • Determine genome (human genome project) • Develop therapies

4. Genetic Disease: Implications for therapy • Prevention • Environmental modifications (restrict toxic agents, replace deficient products or organs, remove toxic substances or organs) • Gene therapy (increase or decrease expression of genes)

5. Genetic Disease: Limitations of Concept • Disease arises from interactions between genetics and environment • Complex polygenic diseases are common and still not well understood • Gene therapy is of limited utility so far • People still get sick and must be treated • Disorders are often dynamic (even genetic ones)

6. Dynamical Disease (166 hits) • Reimann (1963) – Periodic diseases • Mackey and Glass (1977) – Dynamical diseases associated with qualitative changes in dynamics in physiological systems

7. Reimann (1963)

8. Mackey and Glass (1977) rate of change = production – destruction

9. Dynamical Disease: Implications for Research • Collect data from complex rhythms over long times (http://www.physionet.org) • Develop mathematical models and study effects of parameter changes • Develop biological models that display complex rhythms • Develop therapies

10. Complex rhythms are ubiquitous in physiological systems

11. Dynamical Disease: Implications for Research • Collect data from complex rhythms over long times (http://www.physionet.org) • Develop mathematical models and study effects of parameter changes • Develop biological models that display complex rhythms • Develop therapies

12. Pure Parasystole

13. Rules of Pure Parasystole Count the number of sinus beats between ectopic beats. In this sequence: (1) there are 3 integers; (2) one is odd; (3) the sum of the two smaller is one less than the largest. Glass, Goldberger, Belair (1986)

14. Modulated Parasystole Sinus beats reset the ectopic focus (Jalife and Moe, 1976) Courtemanche, Glass, Rosengarten, Goldberger (1989)

15. Modulated Parasystole with Noise Schulte-Frohlinde et al. (2001)

16. Parasystole: Conclusions • Interesting mathematics and physics (number theory, stochastic nonlinear difference equations) explain arrhythmia • Limited significance for medicine to date • Potential significance – classification of complex arrhythmia

17. Cardiac arrhythmias suddenly start and stop

18. Mechanisms of Tachycardia 1. Reentry in a ring 2. Reentry in two dimensions

19. G. R. Mines (1913)

21. Dynamical Disease: Implications for Research • Collect data from complex rhythms over long times (http://www.physionet.org) • Develop mathematical models and study effects of parameter changes • Develop biological models that display complex rhythms • Develop therapies

22. Macroscope for Studying Dynamics in Tissue Culture

23. Pacemakers and Reentry in Tissue Culture Calcium Target(Calcium Green) Calcium Spiral(Calcium Green) Voltage Spiral (di-4-ANEPPS)

24. Bursting Rhythms in Tissue Culture Bub, Glass, Publicover, Shrier, PNAS (1998)

25. Anatomy of a burst

26. Cellular Automata Model of a Burst

27. Dynamics in a Ring of Cardiac Cells Pacemaker Nagai, Gonzalez, Shrier, Glass, PRL (2000)

28. Reentry

29. Cardiac ballet

30. FitzHugh-Nagumo Model of Propagation

31. Heptanol slows propagation and leads to spiral breakup (may be similar to transition from ventricular tachycardia to ventricular fibrillation) Heptanol: Bub, Shrier, Glass, PRL (2002)

32. Simulation of heptanol addition

33. Dynamical Disease: Implications for Therapy • Analyze complex rhythms for diagnosis and prognosis • Develop novel methods for control based on dynamics of physiological system • Add noise to improve perception or to perturb dynamics • Adjust parameters (e.g. by giving drugs) to normal range

34. T-wave alternans Rosenbaum et al. (1994)

35. T-wave Alternans Predicts Arrhythmia

36. Point D2 Dimension Skinner, Pratt, Vybiral (1993)

37. Can you detect atrial fibrillation based on the RR intervals? Normal Atrial fibrillation Atrium S.A. node A.V. node Ventricle R T P R http://www.aboutatrialfibrillation.com

38. National Resource for Complex Physiologic Signals A. Goldberger, Director http://www.physionet.org

39. Identification of Atrial Fibrillation Tateno and Glass (2001)

40. Histogram of ΔRR Intervals during AF

41. Kolmogorov-Smirnov Test

42. KS Test Can Be Used to Identify AF

43. Applications of Dynamics for Diagnosis and Prognosis • Many potential applications – cardiac arrhythmias, epilepsy, tremor, blood diseases • Need for independent tests of algorithms by those with no stake in utility • Data sets of rare time series will be indispensable

44. Dynamical Disease: Implications for Therapy • Analyze complex rhythms for diagnosis and prognosis • Develop novel methods for control based on dynamics of physiological system • Add noise to improve perception or to perturb dynamics • Adjust parameters (e.g. by giving drugs) to normal range

45. Control of Cardiac Chaos I n Garfinkel, Spano, Ditto, Weiss (1992)

46. Identify the Unstable Fixed Point

47. Stimulate to Control Rhythm

48. Controlling Cardiac Alternans Hall, Christini, et al. (1997)

49. Target Unstable Fixed Point

50. Stimulate to Control Alternans