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Physiological Variability

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  1. John Doyle Experiments Ben Recht Theory/computation Physiological Variability Control and dynamical systems Caltech

  2. My interests Multiscale Physics Core theory challenges Network Centric, Pervasive, Embedded, Ubiquitous Systems Biology

  3. Collaborators and contributors(partial list) Biology:Csete,Yi, El-Samad, Khammash, Tanaka, Arkin, Savageau, Simon, AfCS, Kurata, Smolke, Gross, Kitano, Hucka, Sauro, Finney,Bolouri, Gillespie, Petzold, F Doyle, Stelling, Caporale,… Theory:Parrilo, Carlson, Murray,Vinnicombe, Paganini, Mitra Papachristodoulou, Prajna, Goncalves, Fazel, Liu,Lall, D’Andrea, Jadbabaie,Dahleh, Martins, Recht,many more current and former students, … Web/Internet: Li, Alderson, Chen, Low, Willinger,Kelly, Zhu,Yu, Wang, Chandy, … Turbulence: Bamieh, Bobba, McKeown,Gharib,Marsden, … Physics:Sandberg,Mabuchi, Doherty, Barahona, Reynolds, Disturbance ecology: Moritz, Carlson,… Finance:Martinez, Primbs, Yamada, Giannelli,… Current Caltech Former Caltech Longterm Visitor Other

  4. Thanks to • Boeing • NSF • ARO/ICB • AFOSR • NIH/NIGMS • DARPA • Lee Center for Advanced Networking (Caltech) • Hiroaki Kitano (ERATO) • Braun family

  5. Overview • Heart rate variability • From recent talks at meetings of • Intensive care doctors (SCAI) • Anesthesiologists (ASA) • http://www.cds.caltech.edu/~doyle/ASA/ • High variability in core metabolism and other examples (time permitting)

  6. Polar HR monitor Stride sensor Bike Treadmill • Familiar modeling tools (ARMA) • But new math to solve for models

  7. Healthy mean and variance HR [bpm] HR [bpm] 70 70 65 “Resting” HR 60 60 What about this? 55 50 50 45 40 40 35 30 Time 0:00:00 0:02:00 0:04:00 0:06:00

  8. Healthy response Healthy mean and variance HR [bpm] HR [bpm] 70 70 65 60 60 Sit 55 Sit Stand 50 50 45 40 40 35 30 Time 0:00:00 0:02:00 0:04:00 0:06:00

  9. R-R Intervals [ms] 1600 Sit Stand 1200 Sit 800 400 1 2 3 48 bpm 0:00:00 0:02:00 0:04:00 0:06:00 Time

  10. R-R Intervals [ms] R-R Intervals [ms] 1600 1600 1400 1400 1200 1200 1000 1000 800 800 ???? 600 600 400 400 200 200 0:00:00 0:02:00 0:04:00 0:06:00 Time

  11. R-R Intervals [ms] R-R Intervals [ms] 1600 1600 1400 1400 1200 1200 1000 1000 800 800 600 600 400 1 140 bpm 200 Time 0:00:00 0:02:00 0:04:00 0:06:00 0:08:00 0:10:00 Mean RR down (HR up) Variance down

  12. R-R Intervals [ms] 1600 Stand Sit 1200 Sit 800 Run 400 1 2 140 bpm 200 0:00:00 0:10:00 0:20:00 0:30:00 Diagnosis?

  13. HR [bpm] 160 Run 120 Large variability 80 Sit 40 0:00:00 0:10:00 0:20:00 0:30:00 Large variability?

  14. Variability: Alternative viewpoints • Ignore variability in physiology • Misinterpret variability as “emergent, chaos, criticality, scale-free” etc., etc. • Properly interpret variability as the • normal healthy function • of robust, but very complex, • feedback and anticipatory control systems

  15. Summary • Force large changes in heart rate on short timescales • Healthy subjects • Exercise load • Use control theory to interpret data • Confirm what is “well-known” to doctors, coaches, and athletes (everyone with clue) • Mimic • healthy vs ill • with fit vs unfit/fatigued

  16. Alternative viewpoints • Ignore variability in physiology • Misinterpret variability as “emergent, chaos, criticality, scale-free” etc., etc. • Properly interpret variability as the • normal healthy function • of robust, but very complex, • feedback and anticipatory control systems

  17. Intensive Care Challenges (with increasing difficulty) • Reintroduce natural variability (open loop) • Reintroduce natural feedback control • Augment feedback control in regimes outside of evolutionary pressures (e.g. ICUs)

  18. Most cited paper on HR variability 1999

  19. Nonlinear Dynamics, Fractals, and Chaos Theory: Implications for Neuroautonomic Heart Rate Control in Health and Disease Ary L. Goldberger http://www.physionet.org/tutorials/ndc/ Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation101(23):e215-e220; 2000 (June 13)

  20. …de-complexification of systems with disease appears to be a common feature of many pathologies, as well as of aging. When physiologic systems become less complex, their information content is degraded. As a result, they are less adaptable and less able to cope with the exigencies of a constantly changing environment. To generate information, a system must be capable of behaving in an unpredictable fashion. Findings from nonlinear dynamics have also challenged conventional mechanisms of physiological control based on classical homeostasis, which presumes that healthy systems seek to attain a constant steady state. In contrast, nonlinear systems with fractal dynamics, such as the neuroautonomic mechanisms regulating heart rate variability, behave as if they were driven far from equilibrium under basal conditions. This kind of complex variability, rather than a regular homeostatic steady state, appears to define the free-running function of many biological systems.

  21. Warren Weaver, 1948 • Warned against confusing • disorganized complexity versus • organized complexity • Disorganized complexity dominates physical sciences (statistical physics) • Organized complexity is relevant to biology and technology • Confusion/disconnect has gotten much worse since 1948

  22. Disorganized complexity • Chaos, fractals, stat mech, criticality, scale-free,… • Fun topics with broad interest, applicability • Organized complexity • All of the above topics plus • Control, dynamical systems, info, comp theory • Homeostasis/Rheostasis/Allostasis/Uberstasis • Tradeoffs, robust/fragile, evolvability, optimization • Protocols, architecture, scalability, constraints, …

  23. Disorganized complexity • Chaos, fractals, stat mech, criticality, scale-free,… • Fun topics with broad interest, applicability • But bio and tech networks, medicine, are bewildering when viewed with only these tools • Systematic misinterpretation of high variability • Yet successful in “high impact” journals and funding • Popular applications • Self-organized critical forest fires, Internet traffic • Scale-free Internet, metabolism, protein-protein nets • Chaos, fractals, and HR variability ???

  24. Heart &Lung HR highly variable Disorganized complexity, chaos, and HR variability? • “Resting” HR? • “free running” = unpredictable =chaotic?

  25. Organized complexity, control, and HR variability? Liver, brain, heart, kidney, digestion, muscle, other… Internal Loads • “Resting” HR???? • “free running” = unpredictable =chaotic? • or responsive to internal, fluctuating loads? • Hard to measure internal loads • Presumably fluctuating but uncertain Heart &Lung HR highly variable

  26. Internal Loads Heart &Lung HR highly variable Skeletal Muscle external Error=0 - Control

  27. Different views on variability • Disorganized complexity • Simple systems can be unpredictable • (True but irrelevant, as physiology is not simple.) • Organized complexity • Extremely robust and adaptive systems • Often require high internal complexity and organization • That results in appropriately large, adaptive, variable responses

  28. Healthy response Healthy mean and variance HR [bpm] HR [bpm] 70 70 65 60 60 Sit 55 Sit Stand 50 50 45 40 40 35 30 Time 0:00:00 0:02:00 0:04:00 0:06:00

  29. HR [bpm] 160 Run 120 Large variability 80 Sit 40 0:00:00 0:10:00 0:20:00 0:30:00 Large variability?

  30. Pace [min/km] 3:20 4:36 7:30 1 2 Time 0:00:00 0:10:00 0:20:00 0:30:00

  31. external -  10-20 watts “treadmill- stasis”? Internal Loads Heart &Lung HR highly variable Skeletal Muscle Error=0  100-400 watts stasis

  32. external -  10-20 watts “treadmill- stasis”? Internal Loads Heart &Lung HR highly variable Skeletal Muscle Error=0  100-400 watts stasis

  33. Disorganized complexity • Chaos, fractals, stat mech, criticality, scale-free,… Heart &Lung HR highly variable

  34. Disorganized complexity • Chaos, fractals, stat mech, criticality, scale-free,… • Organized complexity • All of the above topics plus • Control, dynamical systems, info, comp theory • Homeostasis/Rheostasis/Allostasis/Uberstasis • Tradeoffs, robust/fragile, evolvability, optimization • Protocols, architecture, scalability, constraints,… • We think it makes sense to use all of the above

  35. Internal Loads Heart &Lung HR highly variable Skeletal Muscle external Error=0 - Organized complexity, circa 1972

  36. Internal Loads Heart &Lung HR highly variable Skeletal Muscle external Error=0 - Control

  37. external - Internal Loads Heart &Lung HR highly variable Skeletal Muscle Error=0 Bike and treadmill

  38. Bike and treadmill HR [bpm] 160 Run ramp Steady bike Run ramp Steady bike Run ramp Steady bike Run ramp 160 140 140 120 120 100 100 80 80 60 1 22 0:00:00 0:20:00 0:40:00 1:00:00 1:20:00 1:40:00 Time

  39. Bike and treadmill 160 Steady bike Run ramp Steady bike Run ramp 140 120 100 80 60 0:40:00 1:40:00

  40. Steady bike Run ramp Steady bike Run ramp 160 140 120 100 80 60 45 min

  41. Steady bike Steady bike 160 140 120 100 80 60

  42. Steady bike Steady bike 160 140 120 100 80 60

  43. Steady bike @250w 160 140 120 Extremely repeatable 100 80 60 40 Rest HR 20 0 20 40 60 80 100

  44. Steady bike @250w 160 140 Mean HR 120 100 80 Error HR + constant 60 40 20 0 20 40 60 80 100 x5 secs

  45. HR [bpm] HR [bpm] 150 150 11/16/2007 140 140 11/22/2007 130 130 120 120 110 110 100 8 104 bpm 90 Time 0:45:00 0:50:00 0:55:00 1:00:00 1:05:00

  46. Run ramp Run ramp 160 140 120 100 80 60

  47. Run ramp Run ramp 160 140 120 100 80 60

  48. ~kcal/hr ~watts Run ramp 1089 1023 944 883 810 754 687 637 272 256 236 221 203 189 172 159 0 20 40 60 80 100

  49. 160 140 120 100 80 60 40 20 0 20 40 60 80 100 x5 secs

  50. 160 140 120 100 80 60 40 20 0 20 40 60 80 100