SIAM Conference on the Life Sciences San Diego, 7-10 Aug 2012

Computational Physiology & the VPH/Physiome Project

Peter Hunter

Auckland University, NZ, and Oxford University, UK

Tissue

Osteon Nephron Acinus Liver lobule Lymph node Cardiac sheets

Organ

Heart Lungs Diaphragm Colon Eye Knee Liver

Environment

Organ system

Organism

Cell

Protein Gene Atom

Network

x 1million 20 generations

The challenge: organs to proteins

• Biophysically based models at every level – as much as possible (there’s always a black box!)

• Adoption of model and data standards – SBML, CellML, FieldML for models

• Automated assembly of multi-scale models – molecule to organ(ism)

• Automated model reduction – otherwise too expensive

• New instrumentation – new instruments → new expts → new knowledge

A multi-scale bioengineering approach needs:

History of the VPH/Physiome Project ..

1999 Systems Biology Markup Language

2006 STEP: Strategy for European Physiome 2008 VPH Network of Excellence

2011 VPH Institute

2010 Drug Disease Model Resources (DDMoRe)

1997 IUPS Physiome Committee

1998 CellML, FieldML

2003 IMAG (NIH, NSF, FDA, NASA, DOE, DOD, ..)

Cardiovascular system Respiratory system Musculo-skeletal system Digestive system Skin (integument) Urinary system Lymphoid system Female reproductive system Special sense organs Central nervous system Endocrine system Male reproductive system

Organ system Physiome Projects

Verification, Benchmarks

Experimental measurements

Model standards: SBML, CellML, FieldML

Model repositories: Biomodels, PMR2

Data standards: DICOM, BioSignalML, ..

Minimum information standards: MIAME, MICEE, ..

Data repositories: PhysioNet, CAPdb, CVRG, ..

Standards for models, data & software

Validation, Limitations

(Journal review)

APIs, webservices

Software: OpenCOR, OpenCMISS, Continuity, Chaste, ..

Simulation standards: SED-ML Functional curation

APIs, webservices

Curation Annotation

Reference description

Metadata: Ontologies

Need modules!

Metadata: Ontologies GO, FMA, ..

CellML – standards, databases and tools

(www.cellml.org)

Cuellar AA, Lloyd CM, Nielsen PF, Halstead MDB, Bullivant DP, Nickerson DP, Hunter PJ. An overview of CellML 1.1, a biological model description language.SIMULATION: Transactions of the Society for Modeling and Simulation, 79(12):740-747, 2003

Cell cycle (25 models)

Calcium dynamics (63 models) Cell migration (2 models)

Circadian rhythms (9 models) Endocrine system (29)

PKPD models (7 models)

Myofilament mechanics (15) Metabolism (35 models)

Electrophysiology (117 models) Excitation-contaction (15 models)

Gene regulation DNA repair (3) Synthetic biology (5 models)

Material constitutive

laws

Metabolism

1. Glycolysis 2. Gluconeogenesis 3. Pentose phosphate pathway 4. Electron transport chain 5. Tricarboxylic acid (TCA) cycle 6. Lipid metabolism 7. Oxidation of odd-chain fatty acids 8. Fatty acid synthesis 9. Cholesterol biosynthesis 10.Phospholipid synthesis 11.Glycolipid synthesis 12.Triacylglycerol synthesis 13.Nitrogen metabolism 14.Pyrimidine nucleotide synthesis 15.Purine nucleotide synthesis 16.Purine nucleotide degradation 17.Conversion of IMP into AMP & GMP 18.Pyrimidine nucleotide degradation 19.Pyrimidine nucleotide degradation 20.Amino acid catabolism 21.Non-essential amino acid synthesis

1. cAMP signalling 2. Calcium signalling - via cADP-ribose signalling NAADP signalling Voltage operated channels (VOCs) Receptor operated channels (ROCs) IP3-Ca2+ signalling (via PLC-PIP2) DAG-PKC signalling (via PLC-PIP2) PI4-5P2 signalling Inositol polyphosphate signalling PI3-Kinase signalling 3. NO-cGMP signalling 4. Redox signalling 5. MAP-Kinase signalling 6. NF-kB signalling 7. Phospholipase D (PLD) signalling 8. Sphingomyelin signalling 9. JAK-STAT signalling 10. Smad signalling 11. Wnt signalling 12. Hedgehog signalling 13. Notch signalling 14. ER stress signalling 15. AMP signalling

Signaling

http://www.cellml.org/tools → OpenCOR

Data: Initial conditions

Boundary conditions

Population Atlas

(Cardiac, MSK, ..)

Codes: Simulation

Visualisation

Specification of models & solution

techniques

Models from

repository

SED-ML

BioSignalML, DICOM

Comparison with reference

data

Workflow for reference description

Benchmarks

Quality control?

MICEE etc

CellML, SBML & FieldML

PhysioNet

Protein degradation

Cell cycle

Protein trafficking

Calcium transport

Intracell. signalling

Gene transcription

Gene regulation

Protein regulation

Protein synthesis

Cellular metabolism

Electro-physiology

Cell process Molecular Biology

Genes miRNA mRNA

… Proteins

Sequence Structure

PTMs Binding motifs

… Lipids

… Carbo-

hydrates …

CellML & SBML libraries

Model components annotated with

ontologies

Gene networks

Metabolic modules

Ion channels

Signalling modules

Cell receptors

…

Physiome FieldML library

Stru

ctu

re &

ph

ysic

s

Infrastructure for linking Molecular Biology to Physiome

Cell function

Motility

Meiosis

Mitosis

Apoptosis

Contraction

Signalling

Transport

Growth

Adhesion

ECM protein synthesis

Sensing

Cell type Ti

ssu

e m

oti

fs

Illustrate ideas with 4 organs:

1. Circulatory system: Heart 2. Respiratory system: Lungs 3. Musculo-skeletal system 4. Digestive system: Stomach

cell-cell connections

proteins genomic

sequence

amino acid

sequence

torso

Heart physiome: Multi-physics and multi-scale

3D cell

tissue

heart

cellular processes

nm

m

=109nm

Hunter PJ, Pullan AJ, Smaill, BH. Modeling total heart function. Annual Review of Biomedical Engineering, 5:147-177, 2003 LeGrice IJ, Hunter PJ, Smaill BH. Am.J.Physiol. 272:H2466-H2476, 1997

Myocardial activation Ventricular wall mechanics Ventricular blood flow Heart valve mechanics Coronary blood flow Neural control

Torso model

Composite lumped parameter

cell model

Hodgkin-Huxley type ion channel model

Markov ion channel model

3D protein model

Coarse grained MD model

Quantum mechanics model

Molecular dynamics model

Continuum tissue model

Organ model

Discrete tissue structure model

Calcium transport models Myofilament mechanics Signal pathway models Metabolic pathway models Gene regulation models

3D cell model

MRI (100m)

MicroCT (5m)

Confocal light microscopy

(0.5m)

Electron tomography

(5nm)

X-ray diffraction

(5A)

Imaging Modelling

Fluid flow

Reaction-diffusion

Electro-magnetic

Finite elasticity

Partial differential equations (PDEs)

Bayesian network description

Molecular dynamics/coarse graining

Poisson-Boltzmann …

Differential algebraic equations

10-9

10-6

10-3

1m

Tissue

Organ system

Organ

Organism

Cell

Protein Gene Atom

Network

Scale Multi-scale

Cardiac structure & kinematics from histology & MRI

Coronal

slice

Short axis

slice

Young AA. Model Tags: Direct 3D tracking of heart wall motion from tagged magnetic resonance images. Medical Image Analysis 3:361-372, 1999

LeGrice IJ, Young AA, Hunter PJ, Smaill BH. J. Mol Cell Cardiol 32(3) A1, 2000

Tissue level structure

Radiological data

Structural data

Molecular data

Model provides framework for aligning data

Mathematical model

Kim, Cannell & Hunter. Changes in calcium current among different transmural regions contributes to action potential heterogeneity in rat heart. PBMB 103(1):28-34, 2010

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I Ca d

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pA

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)

-20pA/pF

-60mV 40mV

LV epi

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RV

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Septum

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LV endo

Physiological data

Element fields: undeformed coords X,Y,Z (or l,m,q) deformed coords x,y,z fibre & sheet orientations electrical potential concentration fields for Ca2+, O2 , etc

Element with Hermite basis

Large deformation elasticity theory Finite element method

x1

x2

x3 u , u , u , etc x1 x2

material coordinates

Equations: Integral formulation - Galerkin method - Gaussian quad

Costa KD, Hunter PJ, Wayne JS, Waldman LK, Guccione JM & McCulloch AD. ASME J. Biomech. Eng. 118:464-472, 1996 Nash MP and Hunter PJ. J. Elasticity. 61(1-3):113-141, 2001

Governing equations for solid mechanics

Kinematics: finite strain theory, incompressible tissue Equilibrium eqtns: conservation of mass & momentum Constitutive eqtns: based on fibrous-sheet structure nonlinearly elastic, viscoelastic, porous etc Boundary conditions: displacement or force & contact mechanics Other factors: residual stress, growth & remodelling

Costa KD, Hunter PJ, Wayne JS, Waldman LK, Guccione JM & McCulloch AD. ASME J. Biomech. Eng. 118:464-472, 1996 Nash and Hunter. J. Elasticity. 61(1-3):113-141, 2001

Galerkin finite element method

Tissue level function: passive properties

Hunter PJ, Smaill BH, Nielsen PMF. Biophysical J, 49(2):90a, 1986 Malcolm DTK, Nielsen PMF, Hunter PJ, Charette G. BMMB, 1(3):197-210, 2002 Schmid, H., Nash, M.P., Young, A.A., Röhrle, O., Hunter, P.J. J Biomech Eng, 129(2):279-283, 2007

Axial

tension

Axial strain

sheet axis

fibre axis

sheet normal

fibre

sheet

normal

Epi

Transmural confocal image of rat myocardium

Endo

4 mm

Thermopile arrays

Trabecula

RV inner wall

1 mm

Hunter PJ, McCulloch AD & ter Keurs HEDJ. Prog Biophys Molec Biol 69:289-331, 1998 Niederer, S.A., Hunter, P.J., Smith, N.P. Biophysical Journal, 90(5):1697–1722, 2006

- electrophysiology - myofilament mechanics - metabolism - signalling

Model:

Tissue level function: active properties

radial circumferential

base

apex

Putting it all together

Nickerson, D.P., Smith, N.P., Hunter, P.J. New developments in a strongly coupled cardiac electromechanical model. Europace, 7(2):S118-S127, 2005. Smith NP, Hunter PJ, Paterson DJ. The cardiac physiome: at the heart of coupling models to measurement. Experimental Physiology, 94(5):469-471, 2009. Keldermann RH, Nash MP, Panfilov AV. Modelling cardiac mechano-electrical feedback using reaction-diffusion-mechanics systems. Physica D 238(11-12):1000-1007, 2009.

Fluid flow

Reaction-diffusion

Electro-magnetic

Finite elasticity

Continuum equations

Microstructural model

Constitutive law

Multiscale tissue models

Fluid flow

Reaction-diffusion

Electro-magnetic

Finite elasticity

Continuum equations

Microstructural model

Constitutive law

Mixture theory (collagen, etc)

Growth laws

Cellular signalling

Schmid H, Watton PN, Maurer MM, Wimmer J, Winkler P, Wang YK, Rohrle O, Itskov M. Biomechanics and Modeling in Mechanobiology, 9:295-315, 2010

Modelling growth

Musculo-skeletal system

Load generic models into the anatomical component under study:

Web-accessible database of generic models (+ tissue structure):

Generic models of the joints

Shim VB, Hunter PJ, Pivonka P, Fernandez JW. A multiscale framework based on the physiome markup languages for exploring the initiation of osteoarthritis at the bone-cartilage interface. IEEE Trans Biomed Eng. 58(12):3532-6, 2011

Automatic model generation Need fully automated process for segmentation and mesh fitting

1

1. Load DICOMs of a CT scan

2

2. Identify the femurs in the scan

3

3. Segment the femur surface

4

4. Fit a high-quality mesh to the femur

E.g. for the femur in a CT scan:

Model generation results

• Fully automated (batch process @ 6 min/femur) • >85% success rate on 230 normal CT scans • Continue to improve as apply to database of 20k scans

A typical successful case A typical failure case

Analysis

• Analysis of CT image profiles sampled normal to the surface model

• Spatially varying thickness over the surface is incorporated into the model, enabling correspondent comparisons between individuals or groups

• Thickness information aids the construction of accurate 3-D models of internal structure

• Extract dominant modes for population subgroups

• Automatic estimation of cortical bone thickness

• Sub-pixel accuracy down to ~0.5mm

A multi-scale model of the lung

Respiratory system

Tawhai MH, Clark AR, Donovan GM, Burrowes KS. Computational modeling of airway & pulmonary vascular structure & function: development of a `Lung Physiome'. Critical Reviews in BME, 2011.

Digestive system: stomach

Faville et al. BiophysJ. 96, 4834-4852, 2009. Biophysically based mathematical modeling of interstitial cells of Cajal slow wave activity generated from a discrete unitary potential basis.

We currently have:

• Model & data encoding standards • Public repositories of curated, annotated

& validated models • Reference descriptions of models • Multi-scale, multi-physics algorithms • Framework for stochastic modeling • Carefully managed, open source codes • Benchmark problems • Population atlases

Summary

But we need:

• Better model reduction algorithms • Better ways of handling multi-scale

(as opposed to multiple-scale) • 3D cell models • A comprehensive modelling

framework for metabolic pathways • A comprehensive modelling

framework for signalling pathways • Better links to bioinformatic databases • Better links to medical informatics

Acknowledgements: The CellML/FieldML team

Poul Nielsen

Andrew Miller

Randall Britten

Richard Christie

Alan Garny

David Nickerson

Tommy Yu

Jonna Terkildsen

Mike Cooling

Catherine LLoyd

Musculo-skeletal team Thor Besier Justin Fernandez Alice Hung Duane Malcolm Kumar Mithraratne Katja Oberhofer Vickie Shim Tim Wu Yu Zhang

Cardiac team Bruce Smaill Ian LeGrice Denis Loiselle Martyn Nash Greg Sands Andrew Taberner Alistair Young Jichao Zhao Andrew McCulloch Nic Smith

Lung team Merryn Tawhai Kelly Burrowes Alys Clark

GI team Andrew Pullan (1962-2012) Leo Cheng Peng Du Greg O’Grady

NZ Health Research Council NZ Ministry of Science & Innovation NZ Maurice Wilkins Centre CoRE UK Wellcome Trust (Heart Physiome) FP7 (euHEART & NoE) NIH (Cardiac Atlas Project - CAP)

Acknowledgements: Funding

www.vph-noe.eu