Key Findings

Summary

Aiming to understanding how disease phenotypes apparent at the whole-organism scale emerge from molecular, cellular, tissue, organ, and organ-system interactions, we have assembled multi-scale (lumped-parameter and distributed) models for cardiovascular systems components, using the models to construct and assess competing hypotheses [1-3] and analyze data from rodent models [4], other species [1], and humans [5-14]. Cardiac models and model components developed by this group, and of particular relevance to the studies proposed here, include new models of rodent myocyte excitation-contraction coupling [15-22], myofilament interactions [23-25], and signal transduction [26, 27], as well as new multi-scale continuum models of ventricular electrophysiology [28], mechanics [29], growth and remodeling in ventricular hypertrophy [30-32]. In parallel we have developed modules for simulating components for metabolic processes (e.g., [33-44]), gas exchange ([45-47]), and solute transport in the body [48-50] in a self-consistent computational framework. We are now realizing the utility of all of this foundational work by applying integrated models in current and proposed studies. For example, a recent study pulls some of these pieces together to determine how metabolic changes observed in heart failure are sufficient to cause systolic dysfunction and whole-body heart failure symptoms and how myosin activating drugs affect the disease phenotype [51]. We have also translated these technologies to the clinic by developing patient-specific models of cardiac electromechanics [12-14] that have shown exciting potential to improve prediction of therapeutic outcomes in patients with dyssynchronous heart failure.

References

1.Beard, D.A., et al., A computational analysis of the long-term regulation of arterial pressure. F1000Res, 2013. 2: p. 208.

2.Beard, D.A., Tautology vs. physiology in the etiology of hypertension. Physiology (Bethesda), 2013. 28(5): p. 270-1.

3.Pettersen, K.H., et al., Arterial stiffening provides sufficient explanation for primary hypertension. PLoS Comput Biol, 2014. 10(5): p. e1003634.

4.Tewari, S.G., et al., Analysis of cardiovascular dynamics in pulmonary hypertensive C57BL6/J mice. Front Physiol, 2013. 4: p. 355.

5.Batzel, J.J., L. Ellwein, and M.S. Olufsen, Modeling cardio-respiratory system response to inhaled CO2 in patients with congestive heart failure. Conf Proc IEEE Eng Med Biol Soc, 2011. 2011: p. 2418-21.

6.Aoi, M.C., et al., Impaired cerebral autoregulation is associated with brain atrophy and worse functional status in chronic ischemic stroke. PLoS One, 2012. 7(10): p. e46794.

7.Ellwein, L.M., et al., Patient-specific modeling of cardiovascular and respiratory dynamics during hypercapnia. Math Biosci, 2013. 241(1): p. 56-74.

8.Ottesen, J.T., J. Mehlsen, and M.S. Olufsen, Structural correlation method for model reduction and practical estimation of patient specific parameters illustrated on heart rate regulation. Math Biosci, 2014. 257: p. 50-9.

9.Qureshi, M.U., et al., Numerical simulation of blood flow and pressure drop in the pulmonary arterial and venous circulation. Biomech Model Mechanobiol, 2014. 13(5): p. 1137-54.

10.Williams, N.D., et al., Patient-specific modelling of head-up tilt. Math Med Biol, 2014. 31(4): p. 365-92.

11.Matzuka, B., et al., Using Kalman Filtering to Predict Time-Varying Parameters in a Model Predicting Baroreflex Regulation During Head-Up Tilt. IEEE Trans Biomed Eng, 2015. 62(8): p. 1992-2000.

12.Aguado-Sierra, J., et al., Patient-specific modeling of dyssynchronous heart failure: a case study. Prog Biophys Mol Biol, 2011. 107(1): p. 147-55.

13.Krishnamurthy, A., et al., Patient-Specific Models of Cardiac Biomechanics. J Comput Phys, 2013. 244: p. 4-21.

14.Villongco, C.T., et al., Patient-specific modeling of ventricular activation pattern using surface ECG-derived vectorcardiogram in bundle branch block. Prog Biophys Mol Biol, 2014. 115(2-3): p. 305-13.

15.Land, S., et al., Integrating multi-scale data to create a virtual physiological mouse heart. Interface Focus, 2013. 3(2): p. 20120076.

16.Land, S., et al., Beta-adrenergic stimulation maintains cardiac function in Serca2 knockout mice. Biophys J, 2013. 104(6): p. 1349-56.

17.Edwards, A.G., et al., Nonequilibrium reactivation of Na+ current drives early afterdepolarizations in mouse ventricle. Circ Arrhythm Electrophysiol, 2014. 7(6): p. 1205-13.

18.Morotti, S., et al., A novel computational model of mouse myocyte electrophysiology to assess the synergy between Na+ loading and CaMKII. J Physiol, 2014. 592(Pt 6): p. 1181-97.

19.Smith, A.F., et al., Transmural variation and anisotropy of microvascular flow conductivity in the rat myocardium. Ann Biomed Eng, 2014. 42(9): p. 1966-77.

20.Land, S., et al., Computational modeling of Takotsubo cardiomyopathy: effect of spatially varying beta-adrenergic stimulation in the rat left ventricle. Am J Physiol Heart Circ Physiol, 2014. 307(10): p. H1487-96.

21.Morotti, S., et al., Atrial-selective targeting of arrhythmogenic phase-3 early afterdepolarizations in human myocytes. J Mol Cell Cardiol, 2015.

22.Aronsen, J.M., et al., Hypokalaemia induces Ca(2+) overload and Ca(2+) waves in ventricular myocytes by reducing Na(+),K(+)-ATPase alpha2 activity. J Physiol, 2015. 593(6): p. 1509-21.

23.Sheikh, F., et al., Mouse and computational models link Mlc2v dephosphorylation to altered myosin kinetics in early cardiac disease. J Clin Invest, 2012. 122(4): p. 1209-21.

24.Tangney, J.R., et al., Novel role for vinculin in ventricular myocyte mechanics and dysfunction. Biophys J, 2013. 104(7): p. 1623-33.

25.Rao, V., et al., PKA phosphorylation of cardiac troponin I modulates activation and relaxation kinetics of ventricular myofibrils. Biophys J, 2014. 107(5): p. 1196-204.

26.Boras, B.W., et al., Using Markov state models to develop a mechanistic understanding of protein kinase A regulatory subunit RIalpha activation in response to cAMP binding. J Biol Chem, 2014. 289(43): p. 30040-51.

27.Boras, B.W., et al., Bridging scales through multiscale modeling: A case study on Protein Kinase A. Frontiers in Physiology, 2015. 6.

28.Gonzales, M.J., et al., Structural contributions to fibrillatory rotors in a patient-derived computational model of the atria. Europace, 2014. 16 Suppl 4: p. iv3-iv10.

29.Sundnes, J., et al., Improved discretisation and linearisation of active tension in strongly coupled cardiac electro-mechanics simulations. Comput Methods Biomech Biomed Engin, 2014. 17(6): p. 604-15.

30.Kerckhoffs, R.C., J. Omens, and A.D. McCulloch, A single strain-based growth law predicts concentric and eccentric cardiac growth during pressure and volume overload. Mech Res Commun, 2012. 42: p. 40-50.

31.Kerckhoffs, R.C., J.H. Omens, and A.D. McCulloch, Mechanical discoordination increases continuously after the onset of left bundle branch block despite constant electrical dyssynchrony in a computational model of cardiac electromechanics and growth. Europace, 2012. 14 Suppl 5: p. v65-v72.

32.Kerckhoffs, R.C., Computational modeling of cardiac growth in the post-natal rat with a strain-based growth law. J Biomech, 2012. 45(5): p. 865-71.

33.Pradhan, R.K., et al., Characterization of Mg2+ inhibition of mitochondrial Ca2+ uptake by a mechanistic model of mitochondrial Ca2+ uniporter. Biophys J, 2011. 101(9): p. 2071-81.

34.Qi, F., et al., Detailed kinetics and regulation of mammalian 2-oxoglutarate dehydrogenase. BMC Biochem, 2011. 12: p. 53.

35.Mescam, M., K.C. Vinnakota, and D.A. Beard, Identification of the catalytic mechanism and estimation of kinetic parameters for fumarase. J Biol Chem, 2011. 286(24): p. 21100-9.

36.Li, X., et al., A database of thermodynamic properties of the reactions of glycolysis, the tricarboxylic acid cycle, and the pentose phosphate pathway. Database (Oxford), 2011. 2011: p. bar005.

37.Beard, D.A., Simulation of cellular biochemical system kinetics. Wiley Interdiscip Rev Syst Biol Med, 2011. 3(2): p. 136-46.

38.Tewari, S.G., et al., A biophysical model of the mitochondrial ATP-Mg/P(i) carrier. Biophys J, 2012. 103(7): p. 1616-25.

39.Li, X., F. Wu, and D.A. Beard, Identification of the kinetic mechanism of succinyl-CoA synthetase. Biosci Rep, 2013. 33(1): p. 145-63.

40.Bazil, J.N., et al., Analysis of the kinetics and bistability of ubiquinol:cytochrome c oxidoreductase. Biophys J, 2013. 105(2): p. 343-55.

41.Tewari, S.G., et al., Markov chain Monte Carlo based analysis of post-translationally modified VDAC gating kinetics. Front Physiol, 2014. 5: p. 513.

42.Bazil, J.N., et al., Determining the origins of superoxide and hydrogen peroxide in the mammalian NADH:ubiquinone oxidoreductase. Free Radic Biol Med, 2014. 77: p. 121-9.

43.Dasika, S.K., K.C. Vinnakota, and D.A. Beard, Characterization of the kinetics of cardiac cytosolic malate dehydrogenase and comparative analysis of cytosolic and mitochondrial isoforms. Biophys J, 2015. 108(2): p. 420-30.

44.Dasika, S.K., K.C. Vinnakota, and D.A. Beard, Determination of the catalytic mechanism for mitochondrial malate dehydrogenase. Biophys J, 2015. 108(2): p. 408-19.

45.Dash, R.K. and J.B. Bassingthwaighte, Erratum to: Blood HbO2 and HbCO2 dissociation curves at varied O2, CO2, pH, 2,3-DPG and temperature levels. Ann Biomed Eng, 2010. 38(4): p. 1683-701.

46.Bassingthwaighte, J.B., et al., Modeling to link regional myocardial work, metabolism and blood flows. Ann Biomed Eng, 2012. 40(11): p. 2379-98.

47.Dash, R.K., B. Korman, and J.B. Bassingthwaighte, Simple accurate mathematical models of blood HbO and HbCO dissociation curves at varied physiological conditions: evaluation and comparison with other models. Eur J Appl Physiol, 2015.

48.Thompson, M.D. and D.A. Beard, Development of appropriate equations for physiologically based pharmacokinetic modeling of permeability-limited and flow-limited transport. J Pharmacokinet Pharmacodyn, 2011. 38(4): p. 405-21.

49.Thompson, M.D., D.A. Beard, and F. Wu, Use of partition coefficients in flow-limited physiologically-based pharmacokinetic modeling. J Pharmacokinet Pharmacodyn, 2012. 39(4): p. 313-27.

50.Thompson, M.D. and D.A. Beard, Physiologically based pharmacokinetic tissue compartment model selection in drug development and risk assessment. J Pharm Sci, 2012. 101(1): p. 424-35.

51.Tewari, S.G., et al., Influence of metabolic dysfunction on cardiac mechanics in decompensated hypertrophy and heart failure. (in review), 2015.