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Circulation Research. 2004;95:1135-1136
doi: 10.1161/01.RES.0000151330.81518.73
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(Circulation Research. 2004;95:1135.)
© 2004 American Heart Association, Inc.

Editorials

The Ongoing Journey to Understand Heart Function Through Integrative Modeling

Raimond L. Winslow, Joseph L. Greenstein

From The Center for Cardiovascular Bioinformatics and Modeling and The Whitaker Biomedical Engineering Institute, Johns Hopkins University School of Medicine and Whiting School of Engineering, Md.

Correspondence to Raimond L. Winslow, Rm 201B Clark Hall, The Johns Hopkins University, 3400 N Charles St, Baltimore, MD 21218. E-mail rwinslow{at}bme.jhu.edu

See related article, pages 1216–1224


Key Words: hear function • modeling • cardiac electrophysiology

Cardiac electrophysiology is a field with a rich history of integrative modeling that has been coupled closely with design and interpretation of experiments. The first models of the cardiac action potential (AP)1 were developed shortly after the Hodgkin–Huxley model of the squid AP and were formulated to explain the experimental observation that, unlike neuronal APs, cardiac APs exhibit a long duration plateau phase. Over the subsequent 20 years, refinement of these models to incorporate emerging experimental data on properties of voltage-gated membrane currents, transport and exchange processes regulating intracellular ion concentrations, and mechanisms of calcium (Ca2+)-induced Ca2+-release (CICR) led to the first integrative model of the cardiac AP, the DiFrancesco–Noble model.2 This landmark model of the Purkinje fiber AP provided the electrophysiological community a mathematical framework on which to build, thus stimulating development of a broad range of integrative cardiac myocyte models. These now include models of canine, guinea pig, human and rabbit ventricular myocytes,3–8 sinoatrial node cells (for review, see Wilders et al9) and atrial myocytes.10,11

Recent research efforts have been directed at extending the range of biophysical and biochemical mechanisms included in these models to enhance their explanatory and predictive capabilities. Important areas of research include: (1) use of single-channel and whole cell current data, in combination with knowledge of channel protein structure, to develop continuous time Markov chain models of voltage-gated channels and membrane transporters12,13; (2) development and integration of mechanistic models of the CICR process12,14,15; (3) modeling of force generation16; (4) modeling of mitochondrial ATP production and its regulation by Ca2+17; and (5) the first steps toward incorporation of intracellular signaling pathways and their actions on target proteins.18 With this depth of mechanistic detail, it is fair to say that if there is a "virtual" cell to be had, it is the cardiac myocyte.

Applications of these myocyte models have been both diverse and informative. More detailed characterization of channel gating properties has enabled investigation of the arrhythmogenic potential of "channelopathies" at the cellular level.13,19 Models have also been applied to analyzing the potential benefits of targeted gene delivery for regulation of QT interval.20 Incorporation of changes in the expression levels of potassium (K+) channels and Ca2+ cycling proteins measured experimentally in end-stage heart failure within a model of the canine ventricular myocyte has accounted for observed changes of the heart failure phenotype, including AP prolongation and reduction of Ca2+ transient amplitude.4 Modeling the effects of acute ischemia (elevated extracellular K+ levels, acidosis, and anoxia) has provided insight into mechanisms of slowed and failing conduction.21

Further integration is of course required to understand how disease-induced changes in cellular processes affect function at the level of tissue and whole organ. In this regard, a second landmark study was that of Nielsen et al.22 These investigators were the first to develop experimental methods for the systematic reconstruction of cardiac ventricular fiber organization and efficient computational methods for the representation of these anatomical data through use of finite-element models.22 This important development set the stage for modeling of electrical conduction in cardiac ventricular tissue and whole-ventricles through solution of either the bidomain or monodomain reaction-diffusion equations.23 Subsequent development of diffusion tensor MRI methods to map ventricular fiber organization has both improved the spatial resolution as well as decreased the time needed to acquire data on ventricular geometry and fiber structure.24

This brings us to the elegant study of Saucerman et al, presented in this issue of Circulation Research.25 These investigators capitalize on both of the integrative modeling approaches described above to elucidate a mechanistic link between a mutation in KCNQ1, the gene that encodes the {alpha} subunit of the repolarizing current IKs and the abnormal electrocardiogram associated with long QT syndrome. The LQT1-associated mutation KCNQ1-G589D has been found to disrupt a local signaling complex composed of the A-kinase anchoring protein (AKAP) yotiao, KCNQ1, protein kinase A (PKA), and protein phosphatase 1,26 and LQT1 patients display additional QT prolongation and increased susceptibility to sudden cardiac death27 in response to exercise or stress. Because this mutation leads to a defect at the interface between the ß1-adrenergic signaling cascade and a target membrane current protein, a theoretical study of the underlying mechanisms could only be accomplished with a model that incorporates both cell signaling and electrophysiology and is then extended to the tissue level. Saucerman et al25 therefore develop and apply a rather comprehensive integrative computational model of ß1-adrenergic signaling, excitation-contraction coupling, and AP propagation to dissect the mechanisms underlying the LQT1 clinical phenotype in patients with the KCNQ1-G589D mutation. The findings indicate that the KCNQ1-G589D mutation alone does not prolong the QT interval, but that in the presence of the ß-adrenergic agonist isoproterenol, abnormalities of repolarization occur in the form of early afterdepolarizations (EADs), and QT prolongation. Prolongation is a consequence of enhanced L-type Ca2+ current in the absence of a counterbalancing enhancement of IKs. Analysis of conduction using a 3-D ventricular wedge model shows that the occurrence of early afterdepolarizations elevates transmural dispersion of repolarization and leads to additional T-wave. The authors conclude that the KCNQ1-G589D mutation causes a breakdown in the regulatory control of IKs by the b1-adrenergic signaling pathway, yielding an increase in the occurrence of repolarization abnormalities, increased dispersion of repolarization in the ventricular wedge and increased vulnerability to abnormal impulse propagation.

The integrative modeling achieved by Saucerman et al has clearly enhanced our understanding of mechanisms of arrhythmia at the cell and tissue level. However, even greater levels of integrative understanding remain to be confronted. One of the most important involves local signaling complexes within the myocyte. The fact that AKAP yotiao, KCNQ1, PKA, and phosphatase 1 coimmunoprecipitate26 suggests that signaling occurs within a small microdomain near this complex. Another clear example of such microdomain signaling is the process of cardiac CICR. Given estimates of diad dimensions, protein size and placement, and the concentration changes likely to occur in response to Ca2+ release events, signaling between the L-Type Ca2+ channel and ryanodine receptors is likely mediated by fewer than {approx}10 free Ca2+ ions. Evidence from neurons suggests that in the case of the ß2-adrenergic signaling system, the receptor, G protein, L-Type Ca2+ channel, adenylyl cyclase, AKAP, PKA and protein phosphatase 2A coimmunoprecipitate,28 a finding likely true of cardiac myocytes. Reaction-diffusion equations derived from laws of mass action are not appropriate for describing concentrations of signaling molecules at these anticipated low molecule counts. Rather, in this situation, the stochastic motion of molecules is likely to have a profound effect on the nature of local signaling29 and potentially of cell and tissue behavior as well. Understanding and modeling of these molecular-level events, as well as development of new mathematical approaches for abstracting models across multiple levels of biological analysis, remains an important challenge for the future.

Footnotes

The opinions expressed in this editorial are not necessarily those of the editors or of the American Heart Association.

References

  1. Noble D. Cardiac action and pace maker potentials based on the Hodgkin-Huxley equations. Nature. 1960; 188: 495–497.[Medline] [Order article via Infotrieve]
  2. DiFrancesco D, Noble D. A model of cardiac electrical activity incorporating ionic pumps and concentration changes. Philos Trans R Soc B Biol Sci. 1985; 307: 353–398.[Medline] [Order article via Infotrieve]
  3. Luo CH, Rudy Y. A dynamic model of the cardiac ventricular action potential. I. Simulations of ionic currents and concentration changes. Circ Res. 1994; 74: 1071–1096.[Abstract/Free Full Text]
  4. Winslow RL, Rice JJ, Jafri MS, Marban E, O’Rourke B. Mechanisms of altered excitation-contraction coupling in canine tachycardia-induced heart failure. II. Model studies. Circ Res. 1999; 84: 571–586.[Abstract/Free Full Text]
  5. Iyer V, Mazhari R, Winslow RL. A Computational model of the human left-ventricular epicardial myocyte. Biophys J. 2004; 87: 1507–1525.[Abstract/Free Full Text]
  6. ten Tusscher KH, Noble D, Noble PJ, Panfilov AV. A model for human ventricular tissue. Am J Physiol Heart Circ Physiol. 2004; 286: H1573–H1589.[Abstract/Free Full Text]
  7. Pandit SV, Giles WR, Demir SS. A mathematical model of the electrophysiological alterations in rat ventricular myocytes in type-I diabetes. Biophys J. 2003; 84: 832–841.[Abstract/Free Full Text]
  8. Puglisi JL, Bers DM. LabHEART: an interactive computer model of rabbit ventricular myocyte ion channels and Ca transport. Am J Physiol Cell Physiol. 2001; 281: C2049–C2060.[Abstract/Free Full Text]
  9. Wilders R, Jongsma HJ, van Ginneken AC. Pacemaker activity of the rabbit sinoatrial node. A comparison of mathematical models. Biophys J. 1991; 60: 1202–1216.[Abstract/Free Full Text]
  10. Nygren A, Fiset C, Firek L, Clark JW, Lindblad DS, Clark RB, Giles WR. Mathematical model of an adult human atrial cell: the Role of K+ currents in repolarization. Circ Res. 1998; 82: 63–81.[Abstract/Free Full Text]
  11. Courtemanche M, Ramirez RJ, Nattel S. Ionic mechanisms underlying human atrial action potential properties: insights from a mathematical model. Am J Physiol Heart Circ Physiol. 1998; 275: H301–H321.[Abstract/Free Full Text]
  12. Jafri S, Rice JJ, Winslow RL. Cardiac Ca2+ dynamics: the roles of ryanodine receptor adaptation and sarcoplasmic reticulum load. Biophys J. 1998; 74: 1149–1168.[Abstract/Free Full Text]
  13. Clancy C, Rudy Y. Linking a genetic defect to its cellular phenotype in a cardiac arrhythmia. Nature. 1999; 400: 566–569.[CrossRef][Medline] [Order article via Infotrieve]
  14. Greenstein JL, Winslow RL. An integrative model of the cardiac ventricular myocyte incorporating local control of Ca2+ release. Biophys J. 2002; 83: 2918–2945.[Abstract/Free Full Text]
  15. Stern M, Song L, Cheng H, Sham J, Yang H, Boheler K, Rios E. Local control models of cardiac excitation-contraction coupling. A possible role for allosteric interactions between ryanodine receptors. J Gen Physiol. 1999; 113: 469–489.[Abstract/Free Full Text]
  16. Rice JJ, Jafri MS, Winslow RL. Modeling short-term interval-force relations in cardiac muscle. Am J Physiol. 2000; 278: H913–H931.
  17. Cortassa S, Aon M, Marban E, Winslow R, O’Rourke B. An integrated model of cardiac mitochondrial energy metabolism and calcium dynamics. Biophys J. 2003; 84: 2734–2755.[Abstract/Free Full Text]
  18. Saucerman JJ, Brunton LL, Michailova AP, McCulloch AD. Modeling beta-adrenergic control of cardiac myocyte contractility in silico. J Biol Chem. 2003; 278: 47997–48003.[Abstract/Free Full Text]
  19. Dumaine R, Towbin JA, Brugada P, Vatta M, Nesterenko DV, Nesterenko VV, Brugada J, Brugada R, Antzelevitch C. Ionic mechanisms responsible for the electrocardiographic phenotype of the Brugada syndrome are temperature dependent. Circ Res. 1999; 85: 803–809.[Abstract/Free Full Text]
  20. Mazhari R, Nuss HB, Winslow RL, Marban E. Ectopic expression of KCNE3 in the heart accelerated cardiac repolarization: a novel approach to gene therapy for long QT syndrome. J Clinic Invest. 2002; 109: 1083–1090.[CrossRef][Medline] [Order article via Infotrieve]
  21. Shaw RM, Rudy Y. Electrophysiologic effects of acute myocardial ischemia. A mechanistic investigation of action potential conduction and conduction failure. Circ Res. 1997; 80: 124–138.[Abstract/Free Full Text]
  22. Nielsen PMF, LeGrice IJ, Smaill BH, Hunter PJ. Mathematical model of geometry and fibrous structure of the heart. Am J Physiol. 1991; 260: H1365–H1378.[Medline] [Order article via Infotrieve]
  23. Hooks DA, Tomlinson KA, Marsden SG, LeGrice IJ, Smaill BH, Pullan AJ, Hunter PJ. Cardiac microstructure: implications for electrical propagation and defibrillation in the heart. Circ Res. 2002; 91: 331–338.[Abstract/Free Full Text]
  24. Scollan D, Zhang J, Holmes A, Yung C, Winslow R. Reconstruction of cardiac ventricular geometry and fiber orientation using GRASS and diffusion-tensor magnetic resonance imaging. Ann Biomed Eng. 2000; 28: 934–944.[CrossRef][Medline] [Order article via Infotrieve]
  25. Saucerman JJ, Healy SN, Belik ME, Puglisi JL, McCulloch AD. Proarrhytmic consequences of a KCNQ1 AKAP-binding domain mutation: computational models of whole cells and heterogeneous tissue. Circ Res. 2004; 95: 1216–1224.[Abstract/Free Full Text]
  26. Marx SO, Kurokawa J, Reiken S, Motoike H, D’Armiento J, Marks AR, Kass RS. Requirement of a macromolecular signaling complex for beta adrenergic receptor modulation of the KCNQ1-KCNE1 potassium channel. Science. 2002; 295: 496–499.[Abstract/Free Full Text]
  27. Schwartz PJ, Priori SG, Spazzolini C, Moss AJ, Vincent GM, Napolitano C, Denjoy I, Guicheney P, Breithardt G, Keating MT, Towbin JA, Beggs AH, Brink P, Wilde AAM, Toivonen L, Zareba W, Robinson JL, Timothy KW, Corfield V, Wattanasirichaigoon D, Corbett C, Haverkamp W, Schulze-Bahr E, Lehmann MH, Schwartz K, Coumel P, Bloise R. Genotype-phenotype correlation in the long-QT syndrome: gene-specific triggers for life-threatening arrhythmias. Circulation. 2001; 103: 89–95.[Abstract/Free Full Text]
  28. Davare MA, Avdonin V, Hall DD, Peden EM, Burette A, Weinberg RJ, Horne MC, Hoshi T, Hell JW. A beta2 adrenergic receptor signaling complex assembled with the Ca2+ channel Cav1.2. Science. 2001; 293: 98–101.[Abstract/Free Full Text]
  29. Bhalla US. Signaling in small subcellular volumes. I. Stochastic and diffusion effects on individual pathways. Biophys J. 2004; 87: 733–744.[Abstract/Free Full Text]

Related Article:

Proarrhythmic Consequences of a KCNQ1 AKAP-Binding Domain Mutation: Computational Models of Whole Cells and Heterogeneous Tissue
Jeffrey J. Saucerman, Sarah N. Healy, Mary E. Belik, Jose L. Puglisi, and Andrew D. McCulloch
Circ. Res. 2004 95: 1216-1224. [Abstract] [Full Text] [PDF]



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