Calibrating models of human physiology using scientific cloud

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

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/atlas

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 ITEA 2 projects EUROSYSLIB, MODELISAR, OPENPROD, and MODRIO Tools – commercial (3 DS Dymola, Wolfram System Modeler, MAPLE, Simulation X), freeopensource (Open. Modelica)

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

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

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

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

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

Example – model of cardiovascular system

Example – model of cardiovascular system

Example – model of cardiovascular system RRAIN – resistance of vena cava=0. 003 mm. Hg*s/ml EITHV – elastance of vena cava = 0. 0182 mm. Hg/ml

Example – model of cardiovascular system

Example – model of cardiovascular system Ventricula Elastance EMAX = 4 mm. Hg/ml

Example – model of cardiovascular system Ventricula Elastance EMAX = 4 mm. Hg/ml RRAIN – resistance of vena cava=0. 003 mm. Hg*s/ml EITHV – elastance of vena cava = 0. 0182 mm. Hg/ml … Parameters of the model

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?

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“

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

What we have done System for parameter identification - Modelica model => FMI Executable (commercial Dymola tool) - Web portal and web services =>. NET, REST API (Service. Stack, Signal. R) - 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

What we have done Model of saturation O 2 in Hemoglobin - Data Imai (1972), Roughton(1967), … - Theory: - Find K 1 … K 4 to fit the data K 1=K 2 = 0. 1301628020369 K 3 = 0. 744154273954473 K 4 = 32. 4804162428542 200 000 simulations in local cluster in 20 minutes

What we have done Complex model of human physiology (Hum. Mod) - Origin www. 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:

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, 5 hours in local cluster elastance, … to fit resistence, ventricular 30 pacient minutesdata in EGI cloud (30 (pressure on CPU) different body positions) heart. left. Heart. ventricular. Elastance. factor 0. 276715208845232 pulmonary. Circulation. EPA. factor 2. 42977968242538 pulmonary. Circulation. RPP. factor 0. 326200900501185 pulmonary. Circulation. RLAIN. factor 7. 03415309534463 heart. left. Heart. Rx. AOutflow. factor 0. 0579461745835701 heart. left. Heart. Rx. VOutflow. factor 0. 115592156812037 heart. right. Heart. ventricular. Elastance. factor 0. 00138825200390164 heart. right. Heart. Rx. VOutflow. factor 0. 0339999571366457 systemic. Circulation. systemic. Veins. RETHV. factor 21. 5099238722829 systemic. Circulation. systemic. Veins. EITHV. factor 0. 183594380817957 systemic. Circulation. systemic. Veins. VITHVU. factor 0. 00115139326808882 systemic. Circulation. systemic. Veins. RRAIN. factor 29. 4603401420539 systemic. Circulation. systemic. Peripheral. Vessels. RTA. factor 2. 58765141868037 systemic. Circulation. systemic. Peripheral. Vessels. RSP. factor 0. 744428239928714 systemic. Circulation. systemic. Arteries. EETHA. factor 0. 318031878463708 systemic. Circulation. systemic. Arteries. RETHA. factor 0. 0792556174368193 systemic. Circulation. systemic. Arteries. EITHA. factor 0. 416618612811922

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

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

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

Thank you for your attention Acknowledgment: EGI, CESNET NGI Supported by: FR CESNET 431/2011 tomas. kulhanek@lf 1. cuni. cz www. physiovalues. org