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

1
Information Flow at the Systems
LevelOrganization and Modeling of Experimental
Data Across Multiple Scales of Biological
AnalysisRaimond L. WinslowCenter for
Cardiovascular Bioinformatics ModelingJohns
Hopkins University Whiting School of Engineering
andSchool of Medicine(www.ccbm.jhu.edu)
2
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

3
Heart Failure is the Leading Cause of SCD
MR Imaging of Canine Heart Pre- and Post- Failure
Heart 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
4
Data Collection
Goal To understand the molecular basis of sudden
cardiac death in human heart failure
Experiments (Human, Canine, Rabbit)
Cell Membrane Transporter Function
Cell Electro- Physiology
Gene/Protein Expression
Cardiac Imaging
Ventricular Conduction
Patient Data
Microarrays 2D PAGE Mass Spec (MALDI-TOF,
TOF-TOF, SELDI)
MR Diffusion Tensor Imaging Spin-Tagging
Heterologous Expression Systems Whole Cell
Patch-Clamp Recording
Ca2, Na V NADH, FADH, Vmito, Ca2mito
Electrode Arrays
Modeling Data Analysis
5
Data Organization
HTML
SOAP
IBM WebsphereTM
(Not Completed)
SQL
SOAP
SOAP
Database Federation Software (IBM Information
Integrator)
Data Analysis Visualization
Models
(HIPPA)
MAGE-DB2
Protein-DB2
CLINICAL
IMAGING
6
Integrative ModelingRelating Molecular
Mechanisms of Excitation-Contraction Coupling to
Cellular and Whole-Heart Function
Ca2 Release Channels (RyR)
10 nm
Ca2
Ca2
L-Type Ca2 Channel

• Ca2-I gtgt Voltage-I

Trigger Ca2
Release Ca2
7
Common Pool Models of the Myocyte
Existing Myocyte Models

• Existing myocyte models lump all 5,000 CaRUs into
  single compartment
• gt common pool models
• Described as systems of ODEs
• Reconstruct properties of the AP

Iserca2a
8
EC Coupling and Common Pool Models
Mechanism
Unstable APs
RyR
Ca2

Ca2
LCCs
Model Prediction Unstable APs (Alternans)
The Common Ca2 Pool
9
Integrating from Channels to the CellThe
Local-Control Myocyte Model
Greenstein, J. L. and Winslow, R. L. (2002)
Biophys. J. 83 2918-2945
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 CaRUs per myocyte (gt
  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

10
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

11
Local Control Myocyte Model Exhibits Graded
Release and Stable APs
Action Potentials
12
Altered Expression of EC Coupling Proteins and
the Cellular Phenotype of Heart Failure
Altered Gene Expression in End-Stage Canine and
Human Heart Failure
Kaab et al (1996). Circ. Res. 78(2) 262 Yung et
al (2003). Genomics. in press online
Genes Encoding K Currents
Genes Encoding EC Coupling Proteins
KCND3 (Ito1) 66 ATP2A2 (62)
KCNJ12 (IK1) 32 NCX1
(75)


Little Effect on AP and Ca2 Transient
Major Effect on AP and Ca2 Transient
Greenstein Winslow (2002). Biophys. J. 83(6)
2918
13
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 ( 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 hyper-phosphorylation at the
  cellular level

14
Develop Model Using Data on b1-Adrenergic
AgonistsEffects on APs and Ca2 Transients
Ca2 Transients
Action Potentials

• Baseline Model
• Serca2a and K current changes
• Mode-1,2 redistribution
• Increased availability

15
Early After-Depolarizations in Response to LCC
Phosphorylation
EAD Frequency

• 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
• gt rate of EAD generation increases with
  increased Mode-2 gating

Mode 2
EADs
APs
0 0 100
7.5 2 100
15 5 100
16
EAD Generation is Stochastic

• Identical initial conditions, but different
  random number seeds produces different
  realizations of LCC and RyR state transitions
• gt stochastic gating of LCCs triggers EADs

17
Initiation of Stochastic EADs by Increased Mode-2
Gating

• 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

18
Integrating from Cell to Ventricular
FunctionDTMR Imaging of Ventricular Anatomic
Structure
DTMRI Fiber Angles In Cross Section
DTMRI vs HISTO Fiber Angles
Holmes, A. et al (2000). Magn. Res. Med., 44157
Scollan et al (2000). Ann. Biomed. Eng., 28(8)
934-944.
19
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

Endocardial Fibers FEM Model
Epicardial Fibers FEM Model
20
Modeling Electrical Conduction in the Cardiac
VentriclesEADs Can Trigger Ventricular
Arrhythmias
Reaction-Diffusion Equation
EADs Trigger Reentry and Polymorpic VT
Winslow et al (2000). Ann. Rev. Biomed. Eng., 2
119-155
21
Closing the Loop on Whole-Heart Experimentsand
Models
256 Epicardial Electrode Array
MR Image and Model Ventricular Anatomy
Measure Electrode Positions
22
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

Winslow et al. (2002). Novartis Foundation
Symposium 247 In Silico Simulation of Biological
Processes, pgs. 129-150, John Wiley Sons, Ltd.
2002.
23
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

24
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