A Multi-formalism solver (M2SL) and XML model … · XML model markup (DAEML): Application to the...

A Multi-formalism solver (M2SL) and XML model markup (DAEML):

Application to the Guyton models

S. Randall ThomasIR4M UMR8081 CNRS - Univ. Paris Sud 11

Orsay & Villejuif, FRANCEand

A. I. HernándezLTSI - INSERM U642

Université de Rennes 1. Rennes, France

OpenModelica WorkshopFebruary 6, 2012

Linköping1

1Monday, February 6, 12

Overview

• The Guyton CVS (CardioVascular System) model: foundation for a collaborative “core model” for the VPH (Virtual Physiological Human)• Quick description of its underpinnings• Our modularization, extensions, and • Global sensitivity analysis and Virtual Population

• A brief introduction to M2SL: Multiformalism Multilevels Simulation Library • Objectives• Structure• User interface


SAPHIR & BIMBO collaboratorsIR4M UMR8081

(CNRS Orsay & Villejuif)Randy Thomas

Rob Moss Thibault Grosse

Stana AgnesPierre MazièreJérôme Bazin

Boubacar BenzianSylvain Demey

MC ENSIIE(Orsay)

Brigitte GrauAnne-Laure LigozatAnne-Lise Minard

IBISC (Univ. Evry)Fariza Tahi

Farida ZehraouiNadia Abchiche

Tarek Melliti

LTSI INSERM U.642 Rennes

Alfredo HernandezVirginie LeRolle

David OjedaGuy Carrault

Mireille Gareau

INSERM U927Poitiers

Patrick HannaertFrançois Guillaud

Collaborators fromBIMBO project

(Lyon)François Gueyffier

Ivanny MarchandAlexandra Laugerotte

Thierry Dumont

TIMC/PRETA UMR 5525 CNRS GrenoblePierre Baconnier

Julie Fontecave-JallonPascale Calabrese

Enas Abdulhay

Univ. Paris VIJean-Pierre Françoise

NASARon White


SAPHIR & BIMBO collaboratorsIR4M UMR8081

(CNRS Orsay & Villejuif)Randy Thomas

Rob Moss Thibault Grosse

Stana AgnesPierre MazièreJérôme Bazin

Boubacar BenzianSylvain Demey

MC ENSIIE(Orsay)

Brigitte GrauAnne-Laure LigozatAnne-Lise Minard

IBISC (Univ. Evry)Fariza Tahi

Farida ZehraouiNadia Abchiche

Tarek Melliti

LTSI INSERM U.642 Rennes

Alfredo HernandezVirginie LeRolle

David OjedaGuy Carrault

Mireille Gareau

INSERM U927Poitiers

Patrick HannaertFrançois Guillaud

Collaborators fromBIMBO project

(Lyon)François Gueyffier

Ivanny MarchandAlexandra Laugerotte

Thierry Dumont

TIMC/PRETA UMR 5525 CNRS GrenoblePierre Baconnier

Julie Fontecave-JallonPascale Calabrese

Enas Abdulhay

Univ. Paris VIJean-Pierre Françoise

NASARon White

3

Collaborations (HumMod, G92) with

the group of Jiri Kofranek,

Charles University, Prague


SAPHIR: Towards a modular “core model” environment



Parameter'DB:'QKDB/

QSDB'

Interac2ve''

Model'repository:''

Virtual'Kidney/'KidneyGrid'

Portal'

Detailed(replacement(modules(

Robust((mul34formalism(solver:(M2SL(

DAEML'

(&'CellML)'

XSLT'

converters'

"Guyton"'

ontology'

Ontology'tools'

AutoKInforma2on'extrac2on'

from'PDF'files'

OMIE:'Ontology'Mapping'within'an'Interac2ve'

and'Extensible'environment'


Core-model environment: Guyton CVS models

kidney muscles

circulatory

dynamics

capillary

membrane

dynamics

thirst

ADH

control

angiotensin

control

aldosterone

control

electrolytes

& cell

water

tissue fluids,

pressures,

gel red cells,

viscosity

autonomic

control

pulmonary

dynamics

local blood

flow

control

oxygen

delivery

heart rate…

heart

hypertrophy

Guyton, Coleman, Granger (1972) Ann. Rev. Physiol.



kidney muscles

circulatory

dynamics

capillary

membrane

dynamics

thirst

ADH

control

angiotensin

control

aldosterone

control

electrolytes

& cell

water

tissue fluids,

pressures,

gel red cells,

viscosity

autonomic

control

pulmonary

dynamics

local blood

flow

control

oxygen

delivery

heart rate…

heart

hypertrophy


+ Multi-organ interactions and regulatory loops



kidney muscles

circulatory

dynamics

capillary

membrane

dynamics

thirst

ADH

control

angiotensin

control

aldosterone

control

electrolytes

& cell

water

tissue fluids,

pressures,

gel red cells,

viscosity

autonomic

control

pulmonary

dynamics

local blood

flow

control

oxygen

delivery

heart rate…

heart

hypertrophy


+ Multi-organ interactions and regulatory loops- Non pulsatile model, focused on long-term response



kidney muscles

circulatory

dynamics

capillary

membrane

dynamics

thirst

ADH

control

angiotensin

control

aldosterone

control

electrolytes

& cell

water

tissue fluids,

pressures,

gel red cells,

viscosity

autonomic

control

pulmonary

dynamics

local blood

flow

control

oxygen

delivery

heart rate…

heart

hypertrophy



-> Integrate a pulsatile model into the Guyton model



kidney muscles

circulatory

dynamics

capillary

membrane

dynamics

thirst

ADH

control

angiotensin

control

aldosterone

control

electrolytes

& cell

water

tissue fluids,

pressures,

gel red cells,

viscosity

autonomic

control

pulmonary

dynamics

local blood

flow

control

oxygen

delivery

heart rate…

heart

hypertrophy



-> Integrate a pulsatile model into the Guyton model-> Integrate RAAS model into the Guyton model


Blood pressure regulation: multi-organ integrationmany systems are involved, at many scales

from Guyton, A. C. (1980). Circulatory Physiology III. Arterial Pressure and Hypertension. Philadelphia, W.B. Saunders.

regulatory systems act over different time scales


Blood pressure regulation: multi-organ integrationmany systems are involved, at many scales

from Guyton, A. C. (1980). Circulatory Physiology III. Arterial Pressure and Hypertension. Philadelphia, W.B. Saunders.

regulatory systems act over different time scales

and over different pressure ranges


Guyton (G92): Comprehensive Sensitivity analysis...

•I/O maps of the 25 modules (all SAPHIR teams)• For each module: plots of all output variables as function of each input, over

a relevant physiological range of values

• Comprehensive sensitivity analysis (IBISC team)•Sensitivity of 297 system variables to each of 96 selected parameters at 5

min., 1h, 1day, and 4 weeks (steady-state) are calculated• This is done for normal steady state and also (twice) for >1000 x 96

randomized "individuals" (Morris. 1991. "Factorial Sampling Plans for Preliminary Computational Experiments." Technometrics, 33(2): 161-174)

• We have thus:• the mean ± SD of the effect of each parameter on each variable, • estimates of the interactions among the parameter effects (covariance

analysis provides details), and• a virtual population of env. 500 000 randomized individuals, and


G92 global sensitivity analysis"heatplots" of means of elementary effects

variables

para

met

ers

para

met

ers

variables

Mean values of the normalized effect, (% change of vj wrt its steady state value), of a small change of each parameter (one-at-a-time, 10% of allowed range) on all variables. Effects are shown at four times after the parameter change, as marked. The graph is truncated at ± 1%.

Clearly, the patterns change with time after the parameter perturbations.


In addition to the sensitivity analysis, per se:A Large Population of "Virtual (Guyton) Individuals"

Randomized parameters --> analogous to "genotype"This results in a variety of virtual "phenotypes"

Not surprisingly, a large proportion of the virtual population is "hypertensive"

The differences between parameter values of the normotensive vs. hypertensive subpopulations may be

interesting…


Multi-plots to visualize sensitivity results


Virtual "Guyton-population": Parameters most implicated in high BP in the virtual population

192 000 "virtual individuals" with randomized parameter values: 109,266 were Hypertensive (MAP above 106 mmHg)

Parameters whose means increased or decreased by at least 5% in the hypertensive subpopulation compared to normotensive subpopulation

Increased by >5% in hypertensives: AARK! basic afferent arteriolar resistanceANCSN! sensitivity controller of AngII effectCPR!critical plasma protein concentration for protein destructionKORGN! gain of positive feedback Korner conceptLPPR! rate of liver protein production).

Decreased by >5% in hypertensives: AUTOK! rate of development of very rapid autoregulationAUV!blood volume shifted from unstressed to stressedCPF!pulmonary capillary filtration coefficientEARK! basic efferent arteriolar resistanceGFLC! glomerular filtration coefficient)

from: Hernandez, A. I., V. Le Rolle, D. Ojeda, P. Baconnier, J. Fontecave-Jallon, F. Guillaud, T. Grosse, R. G. Moss, P. Hannaert and S. R. Thomas (2011). "Integration of detailed modules in a core model of body fluid homeostasis and blood pressure regulation." Prog Biophys Mol Biol 107(1): 169-182


Target scenario: Hypertension—Defects of Distal Tubule NaCl reabsorption.How to model the gene-to-organism relationship?



We must account for:



We must account for:•the change of function at the molecular/ cell membrane level



We must account for:•the change of function at the molecular/ cell membrane level•therapeutic target




•the impact on blood volume, via NaCl reabsorption




•the impact on blood volume, via NaCl reabsorption•the resulting effect on blood pressure




•the impact on blood volume, via NaCl reabsorption•the resulting effect on blood pressure•hormonal and nervous system feedbacks




•the impact on blood volume, via NaCl reabsorption•the resulting effect on blood pressure•hormonal and nervous system feedbacks•(including possible effects on expression of the target gene!)









BUT keep execution time manageable!


M2SL: Multiformalism Multilevels Simulation Library® A. Hernandez(Rennes)

ODE/PDE

FEM

Multi-Agent Models

Cellular Automata

M2SL

LTSI%


Core-model environment: Guyton CVS modelsHow should we implement this model? kidney muscles

circulatory dynamics

capillary membrane dynamics

thirst

ADH control

angiotensin control

aldosterone control

electrolytes & cell water

tissue fluids,

pressures, gel

red cells,

viscosity

autonomic control

pulmonary dynamics

local blood flow

control

oxygen delivery

heart rate…

heart Hyper- trophy

Kidney

Guyton72

Circulatory dynamics

Autonomic control

•••

Circulatory dynamics (d)

Heart Circulation

Electrical Activity

Mechanical Activity

Hydraulic Activity

Systemic circulation

Pulmonary circulation

Handling of different

time-scales


Modeling & simulation method/tool: M2SL

www.ltsi.univ-rennes1.fr/m2sl

• M2SL: Multi-formalism modeling library, based on a co-simulation approach • Object-oriented (C++)• Hierarchical structures for Models and simulators

Coupled Model

Coupled Model

Atomic Model

Atomic Model

Atomic Model

Model hierarchy

Continuous formalism Discrete formalism

Coordinator

Coordinator

Simulator Simulator

Simulator

Root Coordinator

Simulator hierarchy

Temporal synchronization: •  Different synchronization strategies

Inter-module coupling •  Interface-based methods •  S-H, Interpolation, …

A. Defontaine, A. I. Hernández, Acta Biotheoretica, vol. 52, pp. 273-90, 2004A. I. Hernández, et al Progress in Biophysics and Molecular Biology, vol. 107, pp. 169-182, 2011.


http://www.ltsi.univ-rennes1.fr/m2sl

http://www.ltsi.univ-rennes1.fr/m2sl


Mul$%formalism-modeling-by--co%simula$on!

• !Hierarchical!structure!!• !Object1oriented!!• !Distributed!approach!

Input/outputcoupling

Temporal Synchronization

Coupled model Coordinator

Coupled model

Atomic model Atomic model

Atomic model Coordinator

Simulator Simulator

Simulator

Con$nuous'Formalism' Doscrete'Formalism'

Model'hierarchy' Simulator'hierarchy'

time

O2 I1

M2 M1

I1 O2

Synchronisation & simulation at fixed timestep

Synchronisation at fixed timestep & adaptive simulation

Synchronisation & simulation both adaptive



Mul$%formalism-modeling-by--co%simula$on!

• !Hierarchical!structure!!• !Object1oriented!!• !Distributed!approach!

Input/outputcoupling

Temporal Synchronization

Coupled model Coordinator

Coupled model

Atomic model Atomic model

Atomic model Coordinator

Simulator Simulator

Simulator

Con$nuous'Formalism' Doscrete'Formalism'

Model'hierarchy' Simulator'hierarchy'

time

O2 I1

M2 M1

I1 O2

Synchronisation & simulation at fixed timestep

Synchronisation at fixed timestep & adaptive simulation

Synchronisation & simulation both adaptive


Simulation example of the G72 model with M2SL

A. Hernández, et al. PTRS-A v.367,pp 4923-4940, 2009.

Benchmark – Adaptive with fixed couplingSimulation of 2 min. of intense exercise

DT initialized to 1e-4, coupling = 2.5e-4, max abs. Err = 5e-13Execution time 3.2 secs : ~ 3 times faster than a standard fixed-step

Model Outputs match reference benchmark ∆T of each sub-model

0 5 100.2

0.4

0.6

0.8

1

1.2vud

0 5 1026

28

30

32

34

36

38

40pvo

0 5 100

2

4

6

8

10pmo

0 5 1090

100

110

120

130

140

150pa

0 5 100

1

2

3

4

5aup

0 5 105

10

15

20

25qlo

0 5 100

5

10

15

20bfm

0 5 100

1000

2000

3000

4000mmo

0 5 100

0.2

0.4

0.6

0.8

1

1.2

Hemodynamics

time (min)

δt a

,1

0 5 100

0.2

0.4

0.6

0.8

1

1.2

AldoControl

time (min)

δt a

,2

0 5 100

0.2

0.4

0.6

0.8

1

1.2

AngioControl

time (min)

δt a

,3

0 5 100

0.2

0.4

0.6

0.8

1

1.2

MuscleBloodFlow

time (min)

δt a

,4

0 5 100

0.2

0.4

0.6

0.8

1

1.2

LocalBFControl

time (min)

δt a

,5

0 5 100

0.2

0.4

0.6

0.8

1

1.2

AntiDHormone

time (min)

δt a

,6

0 5 100

0.2

0.4

0.6

0.8

1

1.2

HeartViciousCycle

time (min)

δt a

,7

0 5 100

0.2

0.4

0.6

0.8

1

1.2

CapillaryMembrane

time (min)

δt a

,8

0 5 100

0.2

0.4

0.6

0.8

1

1.2

GelProtein

time (min)

δt a

,9

0 5 100

0.2

0.4

0.6

0.8

1

1.2

HeartHypertrophy

time (min)

δt a

,10

0 5 100

0.2

0.4

0.6

0.8

1

1.2

KidneySaltOut

time (min)

δt a

,11

0 5 100

0.2

0.4

0.6

0.8

1

1.2

PlasmaTissue

time (min)

δt a

,12

0 5 100

0.2

0.4

0.6

0.8

1

1.2

PulmonaryDynamics

time (min)

δt a

,13

0 5 100

0.2

0.4

0.6

0.8

1

1.2

Electrolytes

time (min)

δt a

,14

0 5 100

0.2

0.4

0.6

0.8

1

1.2

AutonomicControl

time (min)

δt a

,15


M2SL www.ltsi.univ-rennes1.fr/m2sl

M2SL Models and simulators

Parallel (MPI) Evolutionary

algorithm Library

Simulated annealing

Kalman and Markov Library

Batch Processor

Matlab mex-interface

Parameter identification Sensitivity analysis

Analysis of intracerebral EEGs: Wendling F., Hernández A. Journal of Clinical Neurophysiology, 2005

Analysis of the ANS: Le Rolle V., Hernández A. Modelling and Simulation in Engineering 2008

Analysis of cardiac strain signals: Le Rolle V., Hernández A. Art. Int. Medicine. 2008

Integration of renal function into a CVS model R. Thomas, et al. PTRS-A, vol. 366, pp. 3175–3197 2008.

Multiresolution integration of pulsatile heart into a CVS model A. Hernández, et al. PTRS-A, vol. 367, pp. 4923-4940, 2009Analysis of long-term pacemaker data V. Le Rolle. IEEE TBME, vol. 58, pp. 2982-2986, 2011.


M2SL www.ltsi.univ-rennes1.fr/m2sl

Generation of an XML (DAEML) file

XSLT

M2SL (C++) BM

Makefile

JNI calls

M2SL: Native library

HTML

Matlab


Thank you

21












Robust''mul*+formalism'solver:'M2SL'

DAEML&(&&CellML)&

XSLT&converters&






DAEML&(&&CellML)&

XSLT&converters&

Parameter DB: QKDB/QSDB






DAEML&(&&CellML)&

XSLT&converters&


Ontology(tools(






DAEML&(&&CellML)&

XSLT&converters&


Ontology(tools(

"Guyton"(ontology(






DAEML&(&&CellML)&

XSLT&converters&


Ontology(tools(

"Guyton"(ontology(

Auto%Informa,on-extrac,on-from-PDF-files-






DAEML&(&&CellML)&

XSLT&converters&


Ontology(tools(

"Guyton"(ontology(

Auto%Informa,on-extrac,on-from-PDF-files-

Interac(ve**Model*repository:**

Virtual*Kidney/*KidneyGrid*Portal*


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