1. Calibrating models of human physiology using scientific cloud Tomáš Kulhánek Institute of Pathological Physiology, First Faculty of Medicine, Charles Univerzity in Prague, Czech Republic EGI Champion

2. Who we are Institute of Pathological Physiology  Interdisciplinary team (~10 people)- physicians, mathematicians, computer scientists, biomedical engineers, painters/graphical designers, …  mathematical modeling of human physiology, Software system for simulation application, Graphical design, Educational portal www.physiome.cz/atlaswww.physiome.cz/atlas

3. Modelica Modelica - is an open standard, object-oriented, declarative, multi-domain modeling language for component-oriented modeling of complex systems. Industry - automotive companies, such as Audi, BMW, Daimler, Ford, Toyota, VW use Modelica to design energy efficient vehicles and/or improved air conditioning systems. Power plant providers, such as ABB, EDF, Siemens use Modelica, as well as many other companies. Research - projects within Europe spend 75 Mil. € in the years 2007-2015 to further improve Modelica and Modelica related technology. This is performed within the ITEA2 projects EUROSYSLIB, MODELISAR, OPENPROD, and MODRIO Tools – commercial (3DS Dymola, Wolfram System Modeler, MAPLE, Simulation X), free- opensource (OpenModelica)

4. Modelica in Physiology Combination of hydraulic, biochemical, thermofluid, osmotic domain HumMod - Kofránek, Jiří, Mateják,Marek, Privitzer, Pavol: HumMod - large scale physiological model in Modelica. 8th. International Modelica konference 2011, Dresden, GermanyHumMod - large scale physiological model in Modelica Physiolibrary – free library for modeling physiology, www.physiolibrary.org, 1st price Modelica Free Library Award, 10th International Modelica Conference, March 12, 2014, Lund, Sweden www.physiolibrary.org

5. What is our motivation Tell me, I‘ll forget. Show me and I may remember. Involve me and I‘ll understand.

6. Models Model of aircraft Model of human physiology circulation respiration Blood gases Acidbase, ionic, volume and osmotic balance Digestion Energy metabolism Haematopoiesis … and other susbsystems Neural and hormonal control Mathematical models of physiological subsystems integrated into one unit Influence of drugs Models of medical devices

7. Motivation for modeling Experiment, measurement of experimental data Verification, Validation, compare model simulation with real data Model, simplified mathematical description of reality

8. Hydraulic resistor Elastic baloon Hydraulic valve Example – model of cardiovascular system Heart Pulmonary circulation Systemic circulation

9. Example – model of cardiovascular system

11. RRAIN – resistance of vena cava=0.003 mmHg*s/ml EITHV – elastance of vena cava = 0.0182 mmHg/ml Example – model of cardiovascular system

13. Ventricula Elastance EMAX = 4 mmHg/ml Example – model of cardiovascular system

14. Ventricula Elastance EMAX = 4 mmHg/ml RRAIN – resistance of vena cava=0.003 mmHg*s/ml EITHV – elastance of vena cava = 0.0182 mmHg/ml … Parameters of the model

15. Example – model of cardiovascular system What are the values of the parameters, which fits to concrete human? Ventricula Elastance EMAX = ? RRAIN – resistance of vena cava=? EITHV – elastance of vena cava = ? …

16. Example – model of cardiovascular system Ventricula Elastance EMAX = ? RRAIN – resistance of vena cava=? EITHV – elastance of vena cava = ? … What are the values of the parameters, which fits to concrete human? - Hard(invasive) to measure directly -Measure other model variable -compute parameter, fit simulation with measured data „parameter identification“ „Model calibration“

17. Example – model of cardiovascular system Measure variable from real patient Estimate parameters and simulate and compare with the measured data (curve fitting) Repeat until simulation gives similar result as the measured data

18. What we have done System for parameter identification -Modelica model => FMI Executable (commercial Dymola tool) -Web portal and web services =>.NET, REST API (ServiceStack,SignalR) -Non-linear mathematical models => Global optimization methods => Genetic algorithm -Each iteration produces independent tasks => we tried Desktop grid (BOINC), local cluster, EGI cloud (CESNET NGI) -Kulhánek T., Identification of model parameters in cloud deployed simulation service, IEEE EMBS 2013 – Osaka, Japan, EGI TF 2013 - Madrid

19. What we have done Model of saturation O2 in Hemoglobin -Data Imai (1972), Roughton(1967), … -Theory: -Find K1 … K4 to fit the data K1=K2 = 0.1301628020369 K3 = 0.744154273954473 K4 = 32.4804162428542 200 000 simulations in local cluster in 20 minutes

20. What we have done Complex model of human physiology (HumMod) -Origin www.hummod.orgwww.hummod.org -Modelica implementation Single simulation – 10-60 s Calibrating model for test parameter took 7 days on local cluster vs. 18 hours on cloud:

21. What we have done Model of hemodynamics of cardiovascular system Meurs et al. (2006-2011), Burkhoff et al.(1997-2013) 7), Fernandez de Canete et al. (2013), … -Find factors of vena cava elastance, resistence, ventricular elastance,… to fit pacient data (pressure on different body positions) heart.heart.leftHeart.ventricularElastance.factor0.276715208845232 pulmonaryCirculation.EPA.factor2.42977968242538 pulmonaryCirculation.RPP.factor0.326200900501185 pulmonaryCirculation.RLAIN.factor7.03415309534463 heart.heart.leftHeart.RxAOutflow.factor0.0579461745835701 heart.heart.leftHeart.RxVOutflow.factor0.115592156812037 heart.heart.rightHeart.ventricularElastance.factor0.00138825200390164 heart.heart.rightHeart.RxVOutflow.factor0.0339999571366457 systemicCirculation.systemicVeins.RETHV.factor21.5099238722829 systemicCirculation.systemicVeins.EITHV.factor0.183594380817957 systemicCirculation.systemicVeins.VITHVU.factor0.00115139326808882 systemicCirculation.systemicVeins.RRAIN.factor29.4603401420539 systemicCirculation.systemicPeripheralVessels.RTA.factor2.58765141868037 systemicCirculation.systemicPeripheralVessels.RSP.factor0.744428239928714 systemicCirculation.systemicArteries.EETHA.factor0.318031878463708 systemicCirculation.systemicArteries.RETHA.factor0.0792556174368193 systemicCirculation.systemicArteries.EITHA.factor0.416618612811922 5 hours in local cluster 30 minutes in EGI cloud (30 CPU)

22. Summary Calibrating models of human physiology Motivation Education – train students using simulators Research – construct, validate models System Web portal,.NET web services, REST API, … local cluster, EGI cloud (CESNET NGI) Models Simple models – suitable to calibrate locally Middle models – ? Complex models – suitable in HPC cloud

23. What next? Calibration of model of farmacodynamics & farmacokinetics – use case in healthcare Model calibration – iteration steps can‘t be parallelized Parameter sweep – iterations can be parallelized, Monte Carlo simulation integrate model simulation to grid middleware, or desktop grid middleware Repository of valid values of physiological models - repository of validated models - results of model calibration - validated values from journal articles

24. Motto Tell me, I‘ll forget. Show me and I may remember. Involve me and I‘ll understand.

25. Thank you for your attention Acknowledgment: EGI, CESNET NGI Supported by: FR CESNET 431/2011 tomas.kulhanek@lf1.cuni.cz www.physiovalues.org