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CRISMA Laboratory UPMC Critical Care www.ccm.pitt.edu Exemplary Care Cut t ing-edge Research World-class Education Multi-scale modeling of Influenza a virus and its containment Towards System Biology Workshop Grenoble, May 2011


  1. CRISMA Laboratory UPMC Critical Care www.ccm.pitt.edu  Exemplary Care  Cut t ing-edge Research  World-class Education 

  2. Multi-scale modeling of Influenza a virus and its containment Towards System Biology Workshop Grenoble, May 2011 Gilles Clermont, MD CRISMA Center Center for Inflammation and Regenerative Modeling Critical Care Medicine, Mathematics, Industrial Engineering University of Pittsburgh  Exemplary Care  Cut t ing-edge Research  World-class Education 

  3. Acknowledgments Colleagues Trainees Baris Hancioglu Ian Price David Dreisigmeyer Sarah Lukens Jay Depasse NIH(GM)/NSF(DMS)  Exemplary Care  Cut t ing-edge Research  World-class Education 

  4. Epidemic Influenza In a “normal” season Up to 30 million infections in the US 100,000 attributable hospitalizations 35,000 attributable deaths 3B (direct) -15B (total) in societal costs Most deaths are due to secondary infections (pneumonia) Target population (old, sick and the very young)  Exemplary Care  Cut t ing-edge Research  World-class Education 

  5. Pandemic 2009 H1N1 In the US 61M cases, 12,700 deaths Severe illness The Australia-New Zealand experience (NEJM) 722 cases (29 cases/M) 14.3% mortality The Canadian experience (JAMA) 215 cases 14.3% mortality Our own experience Pittsburgh/UPMC 24 admissions to adult ICU starting in august 2009 75% mortality Target population Young adult, pregnant women  Exemplary Care  Cut t ing-edge Research  World-class Education 

  6.  Exemplary Care  Cut t ing-edge Research  World-class Education 

  7. Influenza A virus  Exemplary Care  Cut t ing-edge Research  World-class Education 

  8. Pandemic influenza is an inflammatory disease 1918 H5N1 Tx/91 H5N1 Perrone et al., PLoS Pathogens 2008  Exemplary Care  Cut t ing-edge Research  World-class Education 

  9. Influenza modeling in Pittsburgh Allegheny county HD Epidemiology Population Social Sciences Data Center (Burke) PSC JHU Genetics (Ghedin-Virology) Host (Clermont/Rosenfeld) Vaccines-In vivo (Ross - CVR) Biodefense (Morel)  Exemplary Care  Cut t ing-edge Research  World-class Education 

  10. Multiscale modeling IVA Global pool Containment Human IVA Population level strategies Human IVA Host level  Exemplary Care  Cut t ing-edge Research  World-class Education 

  11. Multiscale modeling – MIDAS  Exemplary Care  Cut t ing-edge Research  World-class Education 

  12. Multiscale modeling – Population  Exemplary Care  Cut t ing-edge Research  World-class Education 

  13. Population-level models (SIR) Susceptibles P(Infection|Environment) Infected P(Duration) ?? P(Death) Recovered P(Susceptible)?=0 Resistant P(immune) • Human activity (travel) • Geography/weather  Exemplary Care  Cut t ing-edge Research  World-class Education 

  14. Population-level models Agent-based model (FRED) Sophisticated cellular automata Stochastic - Distribution driven Age, household size, school sizes… Transmission, duration of disease… Computational requirements 32 nodes 75GB (3e8 agents) 5 hours Ferguson et al. Nature 2005  Exemplary Care  Cut t ing-edge Research  World-class Education 

  15. Multiscale modeling – Viral evolution  Exemplary Care  Cut t ing-edge Research  World-class Education 

  16. Multiscale modeling - Host Host Lung Tissues Cells Vaccines Virus Therapy Pathways Anti-virals •M2 inhibitors •NA inhibitors  Exemplary Care  Cut t ing-edge Research  World-class Education 

  17. The simplest viral model – ever ! (v1.0) dH = − β HV Λ =0 dt Healthy dI cell = β − δ D=0 HV I dt β dV = − pI cV dt Infected Virus cell δ - where active cell killing p is implicit in this death rate c - virus death or shedding Killer cell  Exemplary Care  Cut t ing-edge Research  World-class Education 

  18. IVA-host model 2.0 Hancioglu et al, J Theor Biol 2007  Exemplary Care  Cut t ing-edge Research  World-class Education 

  19. Host-level model 3.0 Price et al. JCC 2008  Exemplary Care  Cut t ing-edge Research  World-class Education 

  20. Looking at individuals Baccam et al. Virology 2007  Exemplary Care  Cut t ing-edge Research  World-class Education 

  21. The estimation problem dH = − β HV Λ =0 dt Healthy dI cell = β − δ D=0 HV I dt β dV = − pI cV dt Infected Virus cell δ - where active cell killing p is implicit in this death rate c - virus death or shedding Killer cell  Exemplary Care  Cut t ing-edge Research  World-class Education 

  22. Population ensemble models E ( M n ) E ( M n ) ≡ Metamodel or Ensemble Many many more models than individuals  Exemplary Care  Cut t ing-edge Research  World-class Education 

  23. Creating the ensemble Error ( ) − β Γ +Γ ∝ L L L ( | ) M D e i D 2 H H Probability (density) p A p B p E p C p D  Exemplary Care  Cut t ing-edge Research  World-class Education  Parameter axis

  24. Calculating the Ensemble Parameter space Local minima MH-type algorithm Parallel tempering/ Replica exchange as mixing method Earl, Deem 2006  Exemplary Care  Cut t ing-edge Research  World-class Education 

  25. Chain mixing β 5 β 4 β 3 β 2 β 1 tests  Exemplary Care  Cut t ing-edge Research  World-class Education 

  26. The population ensemble  Exemplary Care  Cut t ing-edge Research  World-class Education 

  27. Looking at a population Baccam et al. Virology 2007  Exemplary Care  Cut t ing-edge Research  World-class Education 

  28. Probabilistic ensembles – for subpopulations E ( M ) Sparsity -> pooling “similar” patients may be good enough The clinician does this  Exemplary Care  Cut t ing-edge Research  World-class Education 

  29. A population ensemble Patient 1 Patient 4  Exemplary Care  Cut t ing-edge Research  World-class Education 

  30. Host-level models  Exemplary Care  Cut t ing-edge Research  World-class Education 

  31. V3.0 ensemble  Exemplary Care  Cut t ing-edge Research  World-class Education 

  32. Probabilistic ensembles – for subpopulations E ( M ) Sparsity -> pooling “similar” patients may be good enough The clinician does this  Exemplary Care  Cut t ing-edge Research  World-class Education 

  33. Multiobjective estimation α β = = + 2 2 E E E E E E a b a b  Exemplary Care  Cut t ing-edge Research  World-class Education 

  34. Linking scales of description Threshold of Vmax % Infected cells B 12 A infectivity (log particles/cc) Viral load Threshold of upper 8 Vtdur 50 respiratory symptoms Dmax 10 Ddur Vtmin 0 7 14 7 14 Time (days) C IFN-alpha level 500 Threshold of systemic symptoms Fdur 100 Fmin 7 14 Time (days)  Exemplary Care  Cut t ing-edge Research  World-class Education 

  35. Linking scales of description Transmission = infectivity x upper respiratory symptoms Behavior (stay home or not)= systemic symptoms Threshold of Vtdur 12 A infectivity (log particles/cc) Viral load 8 Vtdur Vtmin 7 14 Vtmin  Exemplary Care  Cut t ing-edge Research  World-class Education 

  36. Linking scales of description Integrated run (explicit hybrid) = not practical Look-up tables = fast but memory hungry Response surfaces = forward simulations, polynomial fit, algebraic computation R Inputs = (Inputs) R f { } ∈ µ µ µ σ σ σ , , , , , R Transmissibility Behavior Resistance Transmissibility Behavior Resistance { } ∈ Inputs Viral load, burst size, viral adhesion, existing antibodies, ..  Exemplary Care  Cut t ing-edge Research  World-class Education 

  37. Predicted impact of therapy Virulent pathogen  Exemplary Care  Cut t ing-edge Research  World-class Education  Si ifi i i i i

  38. A multiscale lung model Inflammation occurs in the tissue barrier between air and blood. Tissue swelling impairs gas diffusion. Extreme inflammation of a respiratory unit (~25 alveoli) can completely stop gas exchange (shunt). The global impact of inflammation depends on the combined contribution of respiratory units (RU) with diverse anatomical and physiologic properties. Reynolds et al, J Theor Biol 2009  Exemplary Care  Cut t ing-edge Research  World-class Education 

  39. Knowledge and successful translation Empirical Kepler/Newton GR - Einstein QG - ?? Epicycles Ellipses Precessing ellipses Black hole physics - to XVII century First interaction Discrepancy between Discrepancy between between a physical predictions and gravity and other law and empiric empiric observation forces of nature observation Depth of knowledge  Exemplary Care  Cut t ing-edge Research  World-class Education 

  40. UPMC Critical Care www.ccm.pitt.edu  Exemplary Care  Cut t ing-edge Research  World-class Education 

  41.  Exemplary Care  Cut t ing-edge Research  World-class Education 

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