Information Flow at the Systems Level:Organization and Modeling of Experimental Data

Across Multiple Scales of Biological Analysis

Raimond L. Winslow

Center for Cardiovascular Bioinformatics & ModelingJohns Hopkins University Whiting School of Engineering and

School of Medicine(www.ccbm.jhu.edu)

Outline

Objective

– develop new methods for risk stratification and treatment of

Sudden Cardiac Death (SCD)

Data Collection from the Molecular to Organ level

Data Organization

Integrative Modeling

– A tool for understanding the relationships between molecular events (e.g., changes in gene/protein expression, post-translational modifications of proteins) and function at the cellular and whole-heart levels

Heart Failure is the Leading Cause of SCD

MR Imaging of Canine Heart Pre- and Post- Failure

Chamber DilationWall Thinning

Mechanical pump failure leading to reduced

cardiac output

Diverse origins

Common end-stage phenotype

The primary U.S. hospital discharge

diagnosis Incidence ~ 400,000/year, prevalence of ~ 4.5

million

15% mortality at 1 Yr, 80% mortality at 6 Yr

leading cause of Sudden Cardiac Death in

the US

Heart Failure

Data Collection

Gene/ProteinExpression

Cell MembraneTransporter

Function

CellElectro-

Physiology

CardiacImaging

VentricularConduction

Experiments (Human, Canine, Rabbit)

Microarrays

2D PAGE

Mass Spec (MALDI-TOF,

TOF-TOF, SELDI)

HeterologousExpression

Systems

Whole Cell &Patch-Clamp

Recording

Ca2+, Na+ & V

NADH, FADH,

Vmito, Ca2+

mito

MR DiffusionTensor

Imaging

Spin-Tagging

ElectrodeArrays

Modeling & Data Analysis

Goal: To understand the molecular basis of sudden cardiac death in human heart failure

PatientData

Data Organization

Database Federation Software(IBM Information Integrator)

MAGE-DB2 Protein-DB2 CLINICALIMAGING

SQL

Web Services Integration(IBM MinelinkTM)

ModelsData Analysis &

Visualization

SOAPSOAP

SOAPHTML

(Not Completed)

(HIPPA)

IBM WebsphereTM

Integrative Modeling:Relating Molecular Mechanisms of Excitation-Contraction

Coupling to Cellular and Whole-Heart Function

Ca2+

L-Type Ca2+

Channel

Ca2+ ReleaseChannels (RyR)

10 nm

From Katz (1992) Physiology of the Heart

Ca2+

~ 10 nm

Bers (2002) Nature 415: 198-205

Trigger Ca2+Release Ca2+

{Vm

}The “Calcium Release Unit” (CaRUs)

~ 10 L-Type Channels and 50 RyR~5,000 such units in the myocyte~ independendent

Ca2+-I >> Voltage-I

Common Pool Models of the Myocyte

Existing Myocyte Models

Iserca2a

Existing myocyte models lump all 5,000 CaRUs into single compartment

– => “common pool” modelsDescribed as systems of ODEsReconstruct properties of the AP

-100

-80

-60

-40

-20

0

20

40

0 0.1 0.2 0.3 0.4 0.5

Experiment

-100

-80

-60

-40

-20

0

20

40

0 100 200 300 400 5000 0.1 0.2 0.3 0.4 0.5

Model

Common Pool ModelsReconstruct the AP

EC Coupling and Common Pool Models

Model PredictionUnstable APs (Alternans)

Linz & Meyer (1998) J. Physiol.513(pt 2): 425-442

When Ca2+-I >> Voltage-I

Unstable APs

Ca2+

LCCs

RyR

Ca2+

Mechanism

{

The Common Ca2+ Pool

Experiment

Model

Tot

al C

a2+ R

elea

se

Membrane Potential

Lack of Graded Release

Integrating from Channels to the Cell:The Local-Control Myocyte Model

Ca2+ Flux from NSR

(Jtr)

Ca2+ Flux to Cytosol

(Jxfer)RyRs(Jrel)

JSR

LCC

(ICaL)ClCh

(Ito2)

Jxfer,i,4

Jxfer,i,2

Jxfer,i,3

Jiss,i,1,4 Jiss,i,2,

3

Jiss,i,3,

4

Jiss,i,1,

2

Jxfer,i,1

Ca2+ Release Unit

1 ICaL : 5 RyR per Functional Unit

4 functional units coupled via Ca2+ diffusion per Calcium Release Unit (CaRU)

~ 12,500 independent CaRU’s per myocyte (=> ~ 50,000 LCCs per cell)

Numerically integrate the ODEs defining the myocyte model over steps t, while simulating stochastic dynamics of the CaRUs within each t

Greenstein, J. L. and Winslow, R. L. (2002) Biophys. J. 83: 2918-2945

Stochastic Simulation Algorithm

Improved pseudo-random number generator (MT19937) with longer period and improved performance

Dynamic allocation algorithm for controlling number of CaRUs

Parallel implementation, ~ linear scaling

~1 minute per 1 Sec of activity

Model can relate channel level events (e.g., phosphorylation) to whole-cell behavior

RyR

Op

en F

ract

ion

12,500 CaRUs

Local Control Myocyte Model Exhibits Graded Release and Stable APs

40

4

Experiment

Wier et al (1994) J. Physiol.474(3): 463-471

Model Action Potentials

KCND3 (Ito1) ~ 66% ATP2A2 ~ (62%)

KCNJ12 (IK1) ~ 32% NCX1 ~ (75%)

Genes EncodingK+ Currents

Genes EncodingEC Coupling Proteins

Experiment

Model

Normal

Normal

Failing

Failing

Normal and Failing APsAltered Gene Expression in End-StageCanine and Human Heart Failure

{ {

Kaab et al (1996). Circ. Res. 78(2): 262Yung et al (2003). Genomics. in press online

Little Effect on AP and Ca2+ Transient

Major Effect on AP and Ca2+ Transient

Greenstein & Winslow (2002). Biophys. J. 83(6): 2918

Altered Expression of EC Coupling Proteins and the Cellular Phenotype of Heart Failure

Relating Effects of PKA-Mediated Phosphorylation of EC-Coupling Proteins to Cellular Function

EC-coupling proteins are believed to be hyper-phosphorylated in the failing heart

Targets and actions of PKA-mediated phosphorylation ( 1M ISO)– L-Type Ca2+ Channels (LCCs)

∙ Increase LCC availability (~ 2 – 2.5x)

∙ Mode-1, 2 re-distribution (~ 15% Mode-2, ~85% Mode-1)

▪ Increased mean channel open time in Mode-2 (~.5 to 5.0 mSec)

– Serca2a Pump (ATP2A2)∙ Serca2 up-regulated by ~ 3x (Simmerman & Jones Physiol. Rev. 78: 921)

− IKr

∙ Increased through reduced inactivation (Heath & Terrar J. Physiol. 522: 391)

– IKs

• Increased ~ 2x (Kathofer et al J. Biol. Chem. 275: 26743)

Use the local-control model to understand consequences of this hyper-phosphorylation at the cellular level

Develop Model Using Data on 1-Adrenergic AgonistsEffects on APs and Ca2+ Transients

“Baseline Model”

– Serca2 and K+ current changes

– Mode-1,2 redistribution

– Increased availability

ControlIso (1 M)

Action Potentials Ca2+ Transients

Early After-Depolarizations in Response to LCC Phosphorylation

Early After-Depolarizations (EADs) are thought to trigger polymorphic ventricular tachycardia

Rate of occurrence of EADs is increased in myocytes isolated from failing hearts

No EADs in the absence of Mode 2 gating

=> rate of EAD generation increases with increased Mode-2 gating

0 0 100

7.5 2 100

15 5 100

% Mode 2 # EADs # APs

EAD Frequency

EAD Generation is Stochastic

Identical initial conditions, but different random number seeds produces different realizations of LCC and RyR state transitions

=> stochastic gating of LCCs triggers EADs

Initiation of Stochastic EADs by Increased Mode-2 Gating

Mode 2 Current

Mode 1 Current

Long Mode-2 open time increases likelihood of clustered random Mode-2 LCC openings

Spontaneous, near simultaneous openings of a sufficient number of LCCs gating in Mode 2 generates inward current

Resulting depolarization re-activates LCCs gating in Mode 1, producing an EAD

Novel hypothesis regarding generation of EADs

Fox and Hutchins (1972). Johns Hopkins Med. J. 130(5): 289-299

Integrating from Cell to Ventricular Function:DTMR Imaging of Ventricular Anatomic Structure

DTMRI 3x3 diffusion tensor Mi(x)Hypothesis – The principle eigenvector of Mi(x) is aligned with fiber direction at point x

Diffusion Tensor MR Imaging (DTMRI)

x

DTMRI vs HISTO Fiber Angles DTMRI Fiber AnglesIn Cross Section

Holmes, A. et al (2000). Magn. Res. Med., 44:157

Scollan et al (2000). Ann. Biomed. Eng., 28(8): 934-944.

fixed Myocardium3-D FSE DTMRI256 x 256 x 100 imaging volume350 m in-plane, 800 m out-of-plane resolutionFiber orientation estimates at ~ 3 * 106 voxels

Imaging Procedure

Finite Element Models of Cardiac Ventricular Anatomy

Epicardial Fibers – FEM Model Endocardial Fibers – FEM Model

User selects number of volume elements/nodesMatlab GUI for visual control of the fitting processAll imaging datasets, FE models, and FEM software are available at www.ccmb.jhu.edu

Modeling Electrical Conduction in the Cardiac VentriclesEADs Can Trigger Ventricular Arrhythmias

Reaction-Diffusion Equation

Winslow et al (2000). Ann. Rev. Biomed. Eng., 2: 119-155

HxtxvxMtxItxvICt

txviappion

m

,),()(1

1),()),((

1),(

{From Ionic Models From DTMRI{

EADs Trigger Reentry and Polymorpic VT

“Closing the Loop” on Whole-Heart Experimentsand Models

256 Epicardial Electrode Array

Measure Electrode Positions

MR Image and ModelVentricular Anatomy

“Closing the Loop” on Whole-Heart Experiments and Models (cont.)

Electrically mapped and DTMR imaged 4 normal and 3 failing canine hearts

– 256-electrode sock array, ~ 5mm electrode spacing

Complete anatomical and electrical reconstruction performed on one normal canine heart

ModelExperiment

Winslow et al. (2002). Novartis Foundation Symposium 247: In Silico Simulation of Biological Processes, pgs. 129-150, John Wiley & Sons, Ltd. 2002.

Summary

Use of a “hierarchy of models”, each developed to address problems at different levels of biological organization, is important

Individual stochastically gating channels

Cell models

Tissue/whole heart models

The detailed spatial arrangement of ion channels in the cardiac myocyte has a profound effect on cell and whole heart function

Stochastic effects at low molecule copy number– ~10 – 100 free Ca2+ ions in the diadic space at the peak of the Ca2+ transient

– Continuum models may not be valid

– Dynamics of Ca2+ ions become important

Importance of the interplay between modeling and experiment– Whole heart models have been used exclusively in the “predictive” mode

– Methods now exist for coupling whole-heart experiments and models

Acknowledgements

Supported by the NIH (HL60133, HL70894, HL61711, HL72488, P50 HL52307, NO1-HV-28180, ), the Falk Medical Trust, the Whitaker Foundation, the D. W Reynolds Foundation and IBM Corporation