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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