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 & Modeling
Johns 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, posttranslational modifications of proteins) and function at the
cellular and whole-heart levels
Heart Failure is the Leading Cause of SCD
Heart Failure
MR Imaging of Canine Heart
Pre- and Post- Failure
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
Chamber Dilation
Wall Thinning
Data Collection
Goal: To understand the molecular basis of sudden cardiac
death in human heart failure
Experiments (Human, Canine, Rabbit)
Gene/Protein
Expression
Microarrays
2D PAGE
Mass Spec
(MALDI-TOF,
TOF-TOF,
SELDI)
Cell Membrane
Cell
Transporter
ElectroFunction
Physiology
Heterologous
Expression
Systems
Whole Cell &
Patch-Clamp
Recording
Cardiac
Imaging
Ventricular
Conduction
Ca2+, Na+ MR Diffusion Electrode
Tensor
Arrays
&V
Imaging
NADH,
FADH, Spin-Tagging
Vmito,
Ca2+mito
Modeling & Data Analysis
Patient
Data
Data Organization
HTML
SOAP
IBM WebsphereTM
(Not Completed)
Web Services Integration
(IBM MinelinkTM)
Data Analysis &
Visualization
SQL
SOAP
SOAP
Models
Database Federation Software
(IBM Information Integrator)
(HIPPA)
MAGE-DB2
Protein-DB2
IMAGING
CLINICAL
Integrative Modeling:
Relating Molecular Mechanisms of Excitation-Contraction
Coupling to Cellular and Whole-Heart Function
~ 10 nm
From Katz (1992) Physiology of the Heart
Bers (2002) Nature 415: 198-205
Ca2+ Release
Channels (RyR)
{
Vm
10 nm
Ca2+
L-Type Ca2+
Channel
Ca2+
2+
Trigger Ca
Ca2+
Release
}
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” models
Described as systems of ODEs
Reconstruct properties of the AP
Common Pool Models
Reconstruct the AP
40
Experiment
20
Model
40
20
0
0
-20
-20
-40
-40
-60
-60
-80
-80
-100
-100
0
0.1
0.2
0.3
0.4
0.5
00
0.1
100
0.2
200
0.3
300
0.4
400
0.5
500
EC Coupling and Common Pool Models
When Ca2+-I >> Voltage-I
Experiment
Model
Membrane Potential
Linz & Meyer (1998) J. Physiol.
513(pt 2): 425-442
Mechanism
Unstable APs
Ca2+
LCCs
Model Prediction
Unstable APs (Alternans)
Ca2+
{
Total Ca2+ Release
Lack of Graded Release
The Common Ca2+ Pool
RyR
Integrating from Channels to the Cell:
The Local-Control Myocyte Model
Greenstein, J. L. and Winslow, R. L. (2002) Biophys. J. 83: 2918-2945
Ca2+ Release Unit
Ca2+ Flux
from NSR
(Jtr)
Jxfer,i,1
Jxfer,i,2
Jiss,i,1
Ca2+ Flux to
Cytosol
JSR
RyRs
(Jrel)
(Jxfer)
LCC
ClCh
Jxfer,i,4
Jiss,i,1,
J,2iss,i,2,
4
3
Jiss,i,3
Jxfer,i,3
,4
(ICaL) (Ito2)
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 Dt, while
simulating stochastic dynamics of the CaRUs within each Dt
RyR Open Fraction
Stochastic Simulation Algorithm
12,500 CaRUs
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
Local Control Myocyte Model Exhibits Graded Release
and Stable APs
Experiment
4
40
Wier et al (1994) J. Physiol.
474(3): 463-471
Model
Action Potentials
Altered Expression of EC Coupling Proteins and the
Cellular Phenotype of Heart Failure
Altered Gene Expression in End-Stage
Canine and Human Heart Failure
Normal and Failing APs
Kaab et al (1996). Circ. Res. 78(2): 262
Yung et al (2003). Genomics. in press online
Experiment
Genes Encoding
K+ Currents
Genes Encoding
EC Coupling Proteins
Failing
Normal
~ 66%
{
KCNJ12 (IK1) ~ 32%
Little Effect on
AP and Ca2+ Transient
ATP2A2 ~ (62%)
NCX1
~ (75%)
{
KCND3 (Ito1)
Major Effect on
AP and Ca2+ Transient
Greenstein & Winslow (2002). Biophys. J. 83(6): 2918
Model
Failing
Normal
Relating Effects of PKA-Mediated Phosphorylation of ECCoupling Proteins to Cellular Function
EC-coupling proteins are believed to be hyper-phosphorylated in the
failing heart
Targets and actions of PKA-mediated phosphorylation ( 1mM 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)
∙ Serca2a 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 hyperphosphorylation at the cellular level
Develop Model Using Data on b1-Adrenergic Agonists
Effects on APs and Ca2+ Transients
Action Potentials
Ca2+ Transients
Control
Iso (1 mM)
“Baseline Model”
– Serca2a and K+ current
changes
– Mode-1,2 redistribution
– Increased availability
Early After-Depolarizations in Response to LCC
Phosphorylation
EAD Frequency
% Mode 2 # EADs
# APs
0
0
100
7.5
2
100
15
5
100
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
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
Integrating from Cell to Ventricular Function:
DTMR Imaging of Ventricular Anatomic Structure
Diffusion Tensor MR Imaging (DTMRI)
x
DTMRI  3x3 diffusion tensor Mi(x)
Hypothesis – The principle
eigenvector of Mi(x) is aligned with
fiber direction at point x
Fox and Hutchins (1972). Johns
Hopkins Med. J. 130(5): 289-299
DTMRI vs HISTO Fiber Angles
Holmes, A. et al (2000). Magn. Res.
Med., 44:157
DTMRI Fiber Angles
In Cross Section
Scollan et al (2000). Ann. Biomed.
Eng., 28(8): 934-944.
Imaging Procedure
fixed Myocardium
3-D FSE DTMRI
256 x 256 x 100 imaging
volume
350 mm in-plane, 800 mm
out-of-plane resolution
Fiber orientation
estimates at ~ 3 * 106
voxels
Finite Element Models of Cardiac Ventricular Anatomy
User selects number of volume elements/nodes
Matlab GUI for visual control of the fitting process
All imaging datasets, FE models, and FEM software are available at
www.ccmb.jhu.edu
Epicardial Fibers – FEM Model
Endocardial Fibers – FEM Model
Modeling Electrical Conduction in the Cardiac Ventricles
EADs Can Trigger Ventricular Arrhythmias
Reaction-Diffusion Equation

v( x, t ) 1 
1  

 I ion (v( x, t ))  I app ( x, t )  
  M i ( x)v( x, t ) , x  H

t
Cm 
b   1

From Ionic Models
From DTMRI
EADs Trigger Reentry and Polymorpic VT
Winslow et al (2000). Ann. Rev. Biomed. Eng., 2: 119-155
“Closing the Loop” on Whole-Heart Experiments
and Models
256 Epicardial Electrode Array
Measure Electrode Positions
MR Image and Model
Ventricular 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
Experiment
Model
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