Abriel,, H., Rougier,, J.‐S., & Jalife,, J. (2015). Ion channel macromolecular complexes in cardiomyocytes: Roles in sudden cardiac death. Circulation Research, 116, 1971–1988.

Ahrens‐Nicklas,, R. C., & Christini,, D. J. (2009). Anthropomorphizing the mouse cardiac action potential via a novel dynamic clamp method. Biophysical Journal, 97, 2684–2692.

Al Abed,, A., Guo,, T., Lovell,, N. H., & Dokos,, S. (2013). Optimisation of ionic models to fit tissue action potentials: Application to 3D atrial modelling. Computational and Mathematical Methods in Medicine, 2013, 951234.

Aliev,, R. R., & Panfilov,, A. V. (1996). A simple two‐variable model of cardiac excitation. Chaos, Solitons %26 Fractals, 7, 293–301.

Andrianakis,, I., McCreesh,, N., Vernon,, I., McKinley,, T. J., Oakley,, J. E., Nsubuga,, R. N., … White,, R. G. (2017). Efficient history matching of a high dimensional individual‐based HIV transmission model. SIAM/ASA Journal on Uncertainty Quantification, 5, 694–719.

Apgar,, J. F., Witmer,, D. K., White,, F. M., & Tidor,, B. (2010). Sloppy models, parameter uncertainty, and the role of experimental design. Molecular BioSystems, 6, 1890–1900.

Arevalo,, H. J., Vadakkumpadan,, F., Guallar,, E., Jebb,, A., Malamas,, P., Wu,, K. C., & Trayanova,, N. A. (2016). Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models. Nature Communications, 7, 11437.

Ball,, F. G., & Rice,, J. A. (1992). Stochastic models for ion channels: Introduction and bibliography. Mathematical Biosciences, 112, 189–206.

Ballouz,, S., Mangala,, M. M., Perry,, M. D., Heitmann,, S., Gillis,, J. A., Hill,, A. P., & Vandenberg,, J. I. (2019). Co‐expression of calcium channels and delayed rectifier potassium channels protects the heart from proarrhythmic events. bioRxiv, 659821.

Balser,, J. R., Roden,, D. M., & Bennett,, P. B. (1990). Global parameter optimization for cardiac potassium channel gating models. Biophysical Journal, 57, 433–444.

Banga,, J. R. (2008). Optimization in computational systems biology. BMC Systems Biology, 2, 47.

Banyasz,, T., Horvath,, B., Jian,, Z., Izu,, L. T., & Chen‐Izu,, Y. (2011). Sequential dissection of multiple ionic currents in single cardiac myocytes under action potential‐clamp. Journal of Molecular and Cellular Cardiology, 50, 578–581.

Bassingthwaighte,, J. B. (2000). Strategies for the Physiome project. Annals of Biomedical Engineering, 28, 1043–1058.

Bauer,, R., Bowman,, B., & Kenyon,, J. (1987). Theory of the kinetic analysis of patch‐clamp data. Biophysical Journal, 52, 961–978.

Beattie,, K. A., Hill,, A. P., Bardenet,, R., Cui,, Y., Vandenberg,, J. I., Gavaghan,, D. J., … Mirams,, G. R. (2018). Sinusoidal voltage protocols for rapid characterisation of ion channel kinetics. The Journal of Physiology, 596, 1813–1828.

Beaumont,, J., Roberge,, F., & Lemieux,, D. (1993). Estimation of the steady‐state characteristics of the Hodgkin‐Huxley model from voltage‐clamp data. Mathematical Biosciences, 115, 145–186.

Beaumont,, J., Roberge,, F., & Leon,, L. (1993). On the interpretation of voltage‐clamp data using the Hodgkin‐Huxley model. Mathematical Biosciences, 115, 65–101.

Bebarova,, M. (2012). Advances in patch clamp technique: Towards higher quality and quantity. General Physiology and Biophysics, 31, 131–140.

Beeler,, G. W., & Reuter,, H. (1977). Reconstruction of the action potential of ventricular myocardial fibres. The Journal of Physiology, 268, 177–210.

Behar,, J., & Yaniv,, Y. (2017). Age‐related pacemaker deterioration is due to impaired intracellular and membrane mechanisms: Insights from numerical modeling. The Journal of General Physiology, 149, 935–949.

Bers,, D. M. (2002). Cardiac excitation‐contraction coupling. Nature, 415, 198–205.

Bers,, D. M. (2008). Calcium cycling and signaling in cardiac myocytes. Annual Review of Physiology, 70, 23–49.

Bett,, G., Zhou,, Q., & Rasmusson,, R. (2011). Models of HERG gating. Biophysical Journal, 101, 631–642.

Bezanilla,, F., & Armstrong,, C. M. (1977). Inactivation of the sodium channel. I. Sodium current experiments. The Journal of General Physiology, 70, 549–566.

Bishop,, M. J., & Plank,, G. (2012). The role of fine‐scale anatomical structure in the dynamics of reentry in computational models of the rabbit ventricles. The Journal of Physiology, 590, 4515–4535.

Bondarenko,, V. E. (2014). A compartmentalized mathematical model of the *β*1‐adrenergic signaling system in mouse ventricular myocytes. PLoS ONE, 9, e89113.

Bot,, C. T., Kherlopian,, A. R., Ortega,, F. A., Christini,, D. J., & Krogh‐Madsen,, T. (2012). Rapid genetic algorithm optimization of a mouse computational model: Benefits for anthropomorphization of neonatal mouse cardiomyocytes. Frontiers in Physiology, 3, 421. https://doi.org/10.3389/fphys.2012.00421.

Boyett,, M. R. (2009). “And the beat goes on” the cardiac conduction system: The wiring system of the heart. Experimental Physiology, 94, 1035–1049.

Brennan,, T., Fink,, M., & Rodriguez,, B. (2009). Multiscale modelling of drug‐induced effects on cardiac electrophysiological activity. European Journal of Pharmaceutical Sciences, 36, 62–77.

Britton,, O. J., Abi‐Gerges,, N., Page,, G., Ghetti,, A., Miller,, P. E., & Rodriguez,, B. (2017). Quantitative comparison of effects of dofetilide, sotalol, quinidine, and verapamil between human ex vivo trabeculae and in silico ventricular models incorporating inter‐individual action potential variability. Frontiers in Physiology, 8, 597. https://doi.org/10.3389/fphys.2017.00597.

Britton,, O. J., Bueno‐Orovio,, A., Ammel,, K. V., Lu,, H. R., Towart,, R., Gallacher,, D. J., & Rodriguez,, B. (2013). Experimentally calibrated population of models predicts and explains intersubject variability in cardiac cellular electrophysiology. Proceedings of the National Academy of Sciences of the United States of America, 110, E2098–E2105.

Britton,, O. J., Bueno‐Orovio,, A., Virág,, L., Varró,, A., & Rodriguez,, B. (2017). The electrogenic Na+/K+ pump is a key determinant of repolarization abnormality susceptibility in human ventricular cardiomyocytes: A population‐based simulation study. Frontiers in Physiology, 8, 278. https://doi.org/10.3389/fphys.2017.00278.

Brown,, K. S., & Sethna,, J. P. (2003). Statistical mechanical approaches to models with many poorly known parameters. Physical Review E, 68, 021904.

Brynjarsdóttir,, J., & O`Hagan,, A. (2014). Learning about physical parameters: The importance of model discrepancy. Inverse Problems, 30, 114007.

Bueno‐Orovio,, A., Cherry,, E. M., & Fenton,, F. H. (2008). Minimal model for human ventricular action potentials in tissue. Journal of Theoretical Biology, 253, 544–560.

Bueno‐Orovio,, A., Sánchez,, C., Pueyo,, E., & Rodriguez,, B. (2014). Na/K pump regulation of cardiac repolarization: Insights from a systems biology approach. Pflügers Archiv—European Journal of Physiology, 466, 183–193.

Cairns,, D. I., Fenton,, F. H., & Cherry,, E. M. (2017). Efficient parameterization of cardiac action potential models using a genetic algorithm. Chaos: An Interdisciplinary Journal of Nonlinear Science, 27, 093922.

Cannon,, R. C., & D`Alessandro,, G. (2006). The ion channel inverse problem: Neuroinformatics meets biophysics. PLoS Computational Biology, 2, e91.

Carbonell‐Pascual,, B., Godoy,, E., Ferrer,, A., Romero,, L., & Ferrero,, J. M. (2016). Comparison between Hodgkin–Huxley and Markov formulations of cardiac ion channels. Journal of Theoretical Biology, 399, 92–102.

Celentano,, J. J., & Hawkes,, A. G. (2004). Use of the covariance matrix in directly fitting kinetic parameters: Application to GABAA receptors. Biophysical Journal, 87, 276–294.

Chaloner,, K., & Verdinelli,, I. (1995). Bayesian experimental design: A review. Statistical Science, 10, 273–304.

Chandler,, N. J., Greener,, I. D., Tellez,, J. O., Inada,, S., Musa,, H., Molenaar,, P., … Dobrzynski,, H. (2009). Molecular architecture of the human sinus node. Circulation, 119, 1562–1575.

Cherry,, E. M., & Fenton,, F. H. (2007). A tale of two dogs: Analyzing two models of canine ventricular electrophysiology. American Journal of Physiology—Heart and Circulatory Physiology, 292, H43–H55.

Clancy,, C. E., & Rudy,, Y. (1999). Linking a genetic defect to its cellular phenotype in a cardiac arrhythmia. Nature, 400, 566–569.

Clancy,, C. E., & Rudy,, Y. (2001). Cellular consequences of HERG mutations in the long QT syndrome: Precursors to sudden cardiac death. Cardiovascular Research, 50, 301–313.

Clancy,, C. E., & Rudy,, Y. (2002). Na+ channel mutation that causes both brugada and long‐QT syndrome phenotypes. Circulation, 105, 1208–1213.

Clerx,, M. (2018). Personalisation of cellular electrophysiology models: Utopia? In 2018 Computing in Cardiology Conference (CinC), 45:1–4.

Clerx,, M., Beattie,, K. A., Gavaghan,, D. J., & Mirams,, G. R. (2019). Four ways to fit an ion channel model. Biophysical Journal, 117, 2420–2437.

Clerx,, M., Collins,, P., de Lange,, E., & Volders,, P. G. A. (2016). Myokit: A simple interface to cardiac cellular electrophysiology. Progress in Biophysics and Molecular Biology, 120, 100–114.

Clerx,, M., Collins,, P., & Volders,, P. G. A. (2015). Applying novel identification protocols to Markov models of INa. In 2015 Computing in Cardiology Conference (CinC), 889–892.

Clerx,, M., Robinson,, M., Lambert,, B., Lei,, C. L., Ghosh,, S., Mirams,, G. R., & Gavaghan,, D. J. (2019). Probabilistic inference on noisy time series (PINTS). Journal of Open Research Software, 7, 23.

Cobelli,, C., & Distefano,, J. J. R. (1980). Parameter and structural identifiability concepts and ambiguities: A critical review and analysis. American Journal of Physiology—Regulatory, Integrative and Comparative Physiology, 239, R7–R24.

Colatsky,, T., Fermini,, B., Gintant,, G., Pierson,, J. B., Sager,, P., Sekino,, Y., … Stockbridge,, N. (2016). The comprehensive in vitro proarrhythmia assay (CiPA) initiative—Update on progress. Journal of Pharmacological and Toxicological Methods, 81, 15–20.

Colman,, M. A., Aslanidi,, O. V., Kharche,, S., Boyett,, M. R., Garratt,, C., Hancox,, J. C., & Zhang,, H. (2013). Pro‐arrhythmogenic effects of atrial fibrillation‐induced electrical remodelling: Insights from the three‐dimensional virtual human atria. The Journal of Physiology, 591, 4249–4272.

Colman,, M. A., Saxena,, P., Kettlewell,, S., & Workman,, A. J. (2018). Description of the human atrial action potential derived from a single, congruent data source: Novel computational models for integrated experimental‐numerical study of atrial arrhythmia mechanisms. Frontiers in Physiology, 9, 1211. https://doi.org/10.3389/fphys.2018.01211.

Colquhoun,, D., Hatton,, C., & Hawkes,, A. (2003). The quality of maximum likelihood estimates of ion channel rate constants. The Journal of Physiology, 547, 699–728.

Colquhoun,, D., & Sigworth,, F. (1995). Fitting and statistical analysis of single‐channel records. In Single‐channel recording (pp. 191–263). Boston, MA: Springer.

Cooper,, J., Mirams,, G. R., & Niederer,, S. A. (2011). High‐throughput functional curation of cellular electrophysiology models. Progress in Biophysics and Molecular Biology, 107, 11–20.

Cooper,, J., Scharm,, M., & Mirams,, G. R. (2016). The cardiac electrophysiology web lab. Biophysical Journal, 110, 292–300.

Cooper,, J., Vik,, J. O., & Waltemath,, D. (2015). A call for virtual experiments: Accelerating the scientific process. Progress in Biophysics and Molecular Biology, 117, 99–106.

Corrado,, C., & Niederer,, S. A. (2016). A two‐variable model robust to pacemaker behaviour for the dynamics of the cardiac action potential. Mathematical Biosciences, 281, 46–54.

Corrado,, C., Whitaker,, J., Chubb,, H., Williams,, S., Wright,, M., Gill,, J., … Niederer,, S. A. (2017). Personalized models of human atrial electrophysiology derived from endocardial electrograms. IEEE Transactions on Biomedical Engineering, 64, 735–742.

Corrado,, C., Williams,, S., Karim,, R., Plank,, G., O`Neill,, M., & Niederer,, S. (2018). A work flow to build and validate patient specific left atrium electrophysiology models from catheter measurements. Medical Image Analysis, 47, 153–163.

Courtemanche,, M., Ramirez,, R. J., & Nattel,, S. (1998). Ionic mechanisms underlying human atrial action potential properties: Insights from a mathematical model. American Journal of Physiology ‐ Heart and Circulatory Physiology, 275, H301–H321.

Coveney,, S., & Clayton,, R. H. (2018). Fitting two human atrial cell models to experimental data using Bayesian history matching. Progress in Biophysics and Molecular Biology, 139, 43–58.

Csercsik,, D., Farkas,, I., Szederkényi,, G., Hrabovszky,, E., Liposits,, Z., & Hangos,, K. M. (2010). Hodgkin–Huxley type modelling and parameter estimation of GnRH neurons. Biosystems, 100, 198–207.

Csercsik,, D., Hangos,, K., & Szederkenyi,, G. (2012). Identifiability analysis and parameter estimation of a single Hodgkin–Huxley type voltage dependent ion channel under voltage step measurement conditions. Neurocomputing, 77, 178–188.

Daly,, A. C., Clerx,, M., Beattie,, K. A., Cooper,, J., Gavaghan,, D. J., & Mirams,, G. R. (2018). Reproducible model development in the cardiac electrophysiology web lab. Progress in Biophysics and Molecular Biology, 139, 3–14.

Davies,, M. R., Mistry,, H. B., Hussein,, L., Pollard,, C. E., Valentin,, J.‐P., Swinton,, J., & Abi‐Gerges,, N. (2012). An in silico canine cardiac midmyocardial action potential duration model as a tool for early drug safety assessment. American Journal of Physiology—Heart and Circulatory Physiology, 302, H1466–H1480.

Decker,, K. F., Heijman,, J., Silva,, J. R., Hund,, T. J., & Rudy,, Y. (2009). Properties and ionic mechanisms of action potential adaptation, restitution, and accommodation in canine epicardium. American Journal of Physiology—Heart and Circulatory Physiology, 296, H1017–H1026.

Degasperi,, A., Fey,, D., & Kholodenko,, B. N. (2017). Performance of objective functions and optimisation procedures for parameter estimation in system biology models. Npj Systems Biology and Applications, 3, 1–9.

Destexhe,, A., & Huguenard,, J. R. (2000). Nonlinear thermodynamic models of voltage‐dependent currents. Journal of Computational Neuroscience, 9, 259–270.

Devenyi,, R. A., Ortega,, F. A., Groenendaal,, W., Krogh‐Madsen,, T., Christini,, D. J., & Sobie,, E. A. (2017). Differential roles of two delayed rectifier potassium currents in regulation of ventricular action potential duration and arrhythmia susceptibility. The Journal of Physiology, 595, 2301–2317.

DiFrancesco,, D., & Noble,, D. (1985). A model of cardiac electrical activity incorporating ionic pumps and concentration changes. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 307, 353–398.

Dokos,, S., & Lovell,, N. H. (2004). Parameter estimation in cardiac ionic models. Progress in Biophysics and Molecular Biology, 85, 407–431.

Drovandi,, C. C., Cusimano,, N., Psaltis,, S., Lawson,, B. A. J., Pettitt,, A. N., Burrage,, P., & Burrage,, K. (2016). Sampling methods for exploring between‐subject variability in cardiac electrophysiology experiments. Journal of the Royal Society Interface, 13, 20160214.

Dutta,, S., Chang,, K. C., Beattie,, K. A., Sheng,, J., Tran,, P. N., Wu,, W. W., … Li,, Z. (2017). Optimization of an in silico cardiac cell model for proarrhythmia risk assessment. Frontiers in Physiology, 8, 616. https://doi.org/10.3389/fphys.2017.00616.

Edeson,, R., Ball,, F., Yeo,, G. F., Milne,, R., & Davies,, S. (1994). Model properties underlying non‐identifiability in single channel inference. Proceedings of the Royal Society of London. Series B: Biological Sciences, 255, 21–29.

Eichel,, C. A., Ríos‐Pérez,, E. B., Liu,, F., Jameson,, M. B., Jones,, D. K., Knickelbine,, J. J., & Robertson,, G. A. (2019). A microtranslatome coordinately regulates sodium and potassium currents in the human heart. eLife, 8, e52654.

Eisner,, D. A., Caldwell,, J. L., Kistamás,, K., & Trafford,, A. W. (2017). Calcium and excitation‐contraction coupling in the heart. Circulation Research, 121, 181–195.

Elkins,, R. C., Davies,, M. R., Brough,, S. J., Gavaghan,, D. J., Cui,, Y., Abi‐Gerges,, N., & Mirams,, G. R. (2013). Variability in high‐throughput ion‐channel screening data and consequences for cardiac safety assessment. Journal of Pharmacological and Toxicological Methods, 68, 112–122.

Elshrif,, M. M., & Cherry,, E. M. (2014). A quantitative comparison of the behavior of human ventricular cardiac electrophysiology models in tissue. PLoS ONE, 9, e84401.

Epstein,, M., Calderhead,, B., Girolami,, M. A., & Sivilotti,, L. G. (2016). Bayesian statistical inference in ion‐channel models with exact missed event correction. Biophysical Journal, 111, 333–348.

Fabbri,, A., Fantini,, M., Wilders,, R., & Severi,, S. (2017). Computational analysis of the human sinus node action potential: Model development and effects of mutations. The Journal of Physiology, 595, 2365–2396.

Fabbri,, A., Goversen,, B., Vos,, M. A., van Veen,, T. A. B., & de Boer,, T. P. (2019). Required GK1 to suppress automaticity of iPSC‐CMs depends strongly on IK1 model structure. Biophysical Journal, 117, 2303–2315.

Faber,, G. M., & Rudy,, Y. (2000). Action potential and contractility changes in [Na+]i overloaded cardiac myocytes: A simulation study. Biophysical Journal, 78, 2392–2404.

Fastl,, T. E., Tobon‐Gomez,, C., Crozier,, A., Whitaker,, J., Rajani,, R., McCarthy,, K. P., … Niederer,, S. A. (2018). Personalized computational modeling of left atrial geometry and transmural myofiber architecture. Medical Image Analysis, 47, 180–190.

Fenton,, F., & Karma,, A. (1998). Vortex dynamics in three‐dimensional continuous myocardium with fiber rotation: Filament instability and fibrillation. Chaos: An Interdisciplinary Journal of Nonlinear Science, 8, 20–47.

Fink,, M., Niederer,, S., Cherry,, E., Fenton,, F., Koivumaki,, J., Seemann,, G., … Smith,, N. (2011). Cardiac cell modelling: Observations from the heart of the physiome project. Progress in Biophysics and Molecular Biology, 104, 2–21.

Fink,, M., & Noble,, D. (2009). Markov models for ion channels: Versatility versus identifiability and speed. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 367, 2161–2179.

Fink,, M., Noble,, D., Virag,, L., Varro,, A., & Giles,, W. R. (2008). Contributions of HERG K+ current to repolarization of the human ventricular action potential. Progress in Biophysics and Molecular Biology, 96, 357–376.

Fink,, M., Noble,, P. J., & Noble,, D. (2011). Ca^{2+}‐induced delayed afterdepolarizations are triggered by dyadic subspace Ca^{2+} affirming that increasing SERCA reduces aftercontractions. American Journal of Physiology—Heart and Circulatory Physiology, 301, H921–H935.

Fragoso,, T. M., Bertoli,, W., & Louzada,, F. (2018). Bayesian model averaging: A systematic review and conceptual classification. International Statistical Review, 86, 1–28.

Fredkin,, D. R., Montal,, M., & Rice,, J. A. (1985). Identification of aggregated Markovian models: Application to the nicotinic acetylcholine receptor. Proceedings of the Berkeley Conference in Honor of Jerzy Neyman and Jack Kiefer, 1, 269–289.

Gadsby,, D. C. (2009). Ion channels versus ion pumps: The principal difference, in principle. Nature Reviews Molecular Cell Biology, 10, 344–352.

Garny,, A., Kohl,, P., Hunter,, P. J., Boyett,, M. R., & Noble,, D. (2003). One‐Dimensional Rabbit Sinoatrial Node Models. Journal of Cardiovascular Electrophysiology, 14, S121–S132.

Gábor,, A., & Banga,, J. R. (2015). Robust and efficient parameter estimation in dynamic models of biological systems. BMC Systems Biology, 9, 74.

Gelman,, A., Carlin,, J. B., Stern,, H. S., Dunson,, D. B., Vehtari,, A., & Rubin,, D. B. (2013). Bayesian data analysis (3rd ed.). CRC Press: Chapman and Hall.

Goldberger,, A. L., Amaral,, L. A., Glass,, L., Hausdorff,, J. M., Ivanov,, P. C., Mark,, R. G., … Stanley,, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101, e215–e220.

Golowasch,, J., Goldman,, M. S., Abbott,, L. F., & Marder,, E. (2002). Failure of averaging in the construction of a conductance‐based neuron model. Journal of Neurophysiology, 87, 1129–1131.

Gong,, J. Q. X., & Sobie,, E. A. (2018). Population‐based mechanistic modeling allows for quantitative predictions of drug responses across cell types. Npj Systems Biology and Applications, 4, 11.

Grandi,, E., Pandit,, S. V., Voigt,, N., Workman,, A. J., Dobrev,, D., Jalife,, J., & Bers,, D. M. (2011). Human atrial action potential and Ca^{2+} model: Sinus rhythm and chronic atrial fibrillation. Circulation Research, 109, 1055–1066.

Gray,, R., & Pathmanathan,, P. (2016). A parsimonious model of the rabbit action potential elucidates the minimal physiological requirements for alternans and spiral wave breakup. PLoS Computational Biology, 12, e1005087.

Gray,, R. A., & Pathmanathan,, P. (2018). Patient‐specific cardiovascular computational modeling: Diversity of personalization and challenges. Journal of Cardiovascular Translational Research, 11, 80–88.

Green,, H. D., Thomas,, G., & Terry,, J. R. (2017). Signal reconstruction of pulmonary vein recordings using a phenomenological mathematical model: Application to pulmonary vein isolation therapy. Frontiers in Physiology, 8, 496. https://doi.org/10.3389/fphys.2017.00496.

Groenendaal,, W., Ortega,, F. A., Kherlopian,, A. R., Zygmunt,, A. C., Krogh‐Madsen,, T., & Christini,, D. J. (2015). Cell‐specific cardiac electrophysiology models. PLoS Computational Biology, 11, e1004242.

Gutenkunst,, R. N., Waterfall,, J. J., Casey,, F. P., Brown,, K. S., Myers,, C. R., & Sethna,, J. P. (2007). Universally sloppy parameter sensitivities in systems biology models. PLoS Computational Biology, 3, e189–1878.

Hafner,, D., Borchard,, U., Richter,, O., & Neugebauer,, M. (1981). Parameter estimation in Hodgkin‐Huxley‐type equations for membrane action potentials in nerve and heart muscle. Journal of Theoretical Biology, 91, 321–345.

Hansen,, N. (2006). The CMA evolution strategy: A comparing review (pp. 75–102). Berlin: Springer Berlin Heidelberg.

Hass,, H., Loos,, C., Raimúndez‐Álvarez,, E., Timmer,, J., Hasenauer,, J., & Kreutz,, C. (2019). Benchmark problems for dynamic modeling of intracellular processes. Bioinformatics, 35, 3073–3082.

Hastie,, T., Tibshirani,, R., & Friedman,, J. (2003). Elements of statistical learning: Data mining, inference, and prediction (2nd ed.). New York, NY: Springer.

Hedley,, W. J., Nelson,, M. R., Bellivant,, D., & Nielsen,, P. F. (2001). A short introduction to CellML. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 359, 1073–1089.

Heijman,, J., Erfanian Abdoust,, P., Voigt,, N., Nattel,, S., & Dobrev,, D. (2016). Computational models of atrial cellular electrophysiology and calcium handling, and their role in atrial fibrillation. The Journal of Physiology, 594, 537–553. https://doi.org/10.1113/JP271404.

Heijman,, J., Volders,, P. G., Westra,, R. L., & Rudy,, Y. (2011). Local control of *β*‐adrenergic stimulation: Effects on ventricular myocyte electrophysiology and Ca(2+)‐transient. Journal of Molecular and Cellular Cardiology, 50, 863–871.

Herrera‐Valdez,, M. A., & Lega,, J. (2011). Reduced models for the pacemaker dynamics of cardiac cells. Journal of Theoretical Biology, 270, 164–176.

Hilgemann,, D. W., & Noble,, D. (1987). Excitation‐contraction coupling and extracellular calcium transients in rabbit atrium: Reconstruction of basic cellular mechanisms. Proceedings of the Royal Society of London. Series B: Biological Sciences, 230, 163–205.

Hines,, K. E., Middendorf,, T. R., & Aldrich,, R. W. (2014). Determination of parameter identifiability in nonlinear biophysical models: A Bayesian approach. The Journal of General Physiology, 143, 401–416.

Hodgkin,, A. L., & Huxley,, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117, 500–544.

Hodgson,, M. (1999). A Bayesian restoration of an ion channel signal. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61, 95–114.

Hodgson,, M. E., & Green,, P. J. (1999). Bayesian choice among Markov models of ion channels using Markov chain Monte Carlo. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 455, 3425–3448.

Holland,, J. H. (1992). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control and artificial intelligence. Cambridge, MA: MIT Press.

Horn,, R., & Lange,, K. (1983). Estimating kinetic constants from single channel data. Biophysical Journal, 43, 207–223.

Horn,, R., & Vandenberg,, C. A. (1984). Statistical properties of single sodium channels. The Journal of General Physiology, 84, 505–534.

Hosein‐Sooklal,, A., & Kargol,, A. (2002). Wavelet analysis of nonequilibrium ionic currents in human heart sodium channel (hh1a). The Journal of Membrane Biology, 188, 199–212.

Hund,, T. J., & Rudy,, Y. (2004). Rate dependence and regulation of action potential and calcium transient in a canine cardiac ventricular cell model. Circulation, 110, 3168–3174.

Hunter,, P., Robbins,, P., & Noble,, D. (2002). The IUPS human physiome project. Pflügers Archiv—European Journal of Physiology, 445, 1–9.

Hutter,, O. F., & Noble,, D. (1960). Rectifying properties of heart muscle. Nature, 188, 495–495.

Irvine,, L. A., Saleet Jafri,, M., & Winslow,, R. L. (1999). Cardiac sodium channel Markov model with temperature dependence and recovery from inactivation. Biophysical Journal, 76, 1868–1885.

Jafri,, M. S., Rice,, J. J., & Winslow,, R. L. (1998). Cardiac Ca^{2+} dynamics: The roles of ryanodine receptor adaptation and sarcoplasmic reticulum load. Biophysical Journal, 74, 1149–1168.

Jæger,, K. H., Charwat,, V., Charrez,, B., Finsberg,, H., Maleckar,, M. M., Wall,, S., … Tveito,, A. (2019). Improved computational identification of drug response using optical measurements of human stem cell derived cardiomyocytes in microphysiological systems. bioRxiv, 787390.

Jæger,, K. H., Wall,, S., & Tveito,, A. (2019). Detecting undetectables: Can conductances of action potential models be changed without appreciable change in the transmembrane potential? Chaos: An Interdisciplinary Journal of Nonlinear Science, 29, 073102.

Johnstone,, R. H., Bardenet,, R., Gavaghan,, D. J., Polonchuk,, L., Davies,, M. R., & Mirams,, G. R. (2016). Hierarchical Bayesian modelling of variability and uncertainty in synthetic action potential traces. In Computing in Cardiology Conference (CinC), 2016.

Johnstone,, R. H., Chang,, E. T. Y., Bardenet,, R., de Boer,, T. P., Gavaghan,, D. J., Pathmanathan,, P., … Mirams,, G. R. (2016). Uncertainty and variability in models of the cardiac action potential: Can we build trustworthy models? Journal of Molecular and Cellular Cardiology, 96, 49–62.

Jones,, E., Oliphant,, T., & Peterson,, P. (2001). SciPy: Open source scientific tools for Python. Retrieved from http://www.scipy.org/

Kargol,, A. (2013). Wavelet‐based protocols for ion channel electrophysiology. BMC Biophysics, 6, 3.

Kargol,, A., Smith,, B., & Millonas,, M. M. (2002). Applications of nonequilibrium response spectroscopy to the study of channel gating. Experimental design and optimization. Journal of Theoretical Biology, 218, 239–258.

Kaur,, J., Nygren,, A., & Vigmond,, E. J. (2014). Fitting membrane resistance along with action potential shape in cardiac myocytes improves convergence: Application of a multi‐objective parallel genetic algorithm. PLoS ONE, 9, e107984.

Keener,, J., & Sneyd,, J. (2009). Mathematical physiology: II: Systems physiology. New York, NY: Springer‐Verlag, Springer Science %26 Business Media.

Kennedy,, M. C., & O`Hagan,, A. (2001). Bayesian calibration of computer models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63, 425–464.

Koivumäki,, J. T., Korhonen,, T., & Tavi,, P. (2011). Impact of sarcoplasmic reticulum calcium release on calcium dynamics and action potential morphology in human atrial myocytes: A computational study. PLoS Computational Biology, 7, e1001067.

Krogh‐Madsen,, T., Jacobson,, A. F., Ortega,, F. A., & Christini,, D. J. (2017). Global optimization of ventricular myocyte model to multi‐variable objective improves predictions of drug‐induced Torsades de Pointes. Frontiers in Physiology, 8, 1059. https://doi.org/10.3389/fphys.2017.01059\.

Krogh‐Madsen,, T., Sobie,, E. A., & Christini,, D. J. (2016). Improving cardiomyocyte model fidelity and utility via dynamic electrophysiology protocols and optimization algorithms. The Journal of Physiology, 594, 2525–2536.

Lambert,, B. (2018). A student`s guide to Bayesian statistics. Newbury Park, CA: Sage.

Lancaster,, M. C., & Sobie,, E. (2016). Improved prediction of drug‐induced Torsades de Pointes through simulations of dynamics and machine learning algorithms. Clinical Pharmacology %26 Therapeutics, 100, 371–379.

Lawson,, B. A. J., Drovandi,, C. C., Cusimano,, N., Burrage,, P., Rodriguez,, B., & Burrage,, K. (2018). Unlocking data sets by calibrating populations of models to data density: A study in atrial electrophysiology. Science Advances, 4, e1701676.

Lee,, A. W. C., Nguyen,, U. C., Razeghi,, O., Gould,, J., Sidhu,, B. S., Sieniewicz,, B., … Niederer,, S. (2019). A rule‐based method for predicting the electrical activation of the heart with cardiac resynchronization therapy from non‐invasive clinical data. Medical Image Analysis, 57, 197–213.

Lee,, J., Smaill,, B., & Smith,, N. (2006). Hodgkin–Huxley type ion channel characterization: An improved method of voltage clamp experiment parameter estimation. Journal of Theoretical Biology, 242, 123–134.

Lei,, C. L., Clerx,, M., Beattie,, K. A., Melgari,, D., Hancox,, J. C., Gavaghan,, D. J., … Mirams,, G. R. (2019). Rapid characterisation of hERG channel kinetics II: Temperature dependence. Biophysical Journal, 117, 2455–2470.

Lei,, C. L., Clerx,, M., Gavaghan,, D. J., Polonchuk,, L., Mirams,, G. R., & Wang,, K. (2019). Rapid characterisation of hERG channel kinetics I: Using an automated high‐throughput system. Biophysical Journal, 117, 2438–2454.

Lei,, C. L., Clerx,, M., Whittaker,, D. G., Gavaghan,, D. J., de Boer,, T. P., & Mirams,, G. R. (2019). Accounting for variability in ion current recordings using a mathematical model of artefacts in voltage‐clamp experiments. bioRxiv. Retrieved from https://www.biorxiv.org/content/early/2019/12/22/2019.12.20.884353.

Lei,, C. L., Ghosh,, S., Whittaker,, D. G., Aboelkassem,, Y., Beattie,, K. A., Cantwell,, C. D., … Wilkinson,, R. D. (2020). Considering discrepancy when calibrating a mechanistic electrophysiology model. arXiv. Retrieved from https://arxiv.org/abs/2001.04230.

Lei,, C. L., Wang,, K., Clerx,, M., Johnstone,, R. H., Hortigon‐Vinagre,, M. P., Zamora,, V., … Polonchuk,, L. (2017). Tailoring mathematical models to stem‐cell derived cardiomyocyte lines can improve predictions of drug‐induced changes to their electrophysiology. Frontiers in Physiology, 8, 986. https://doi.org/10.3389/fphys.2017.00986.

Lenk,, C., Weber,, F. M., Bauer,, M., Einax,, M., Maass,, P., & Seeman,, G. (2015). Initiation of atrial fibrillation by interaction of pacemakers with geometrical constraints. Journal of Theoretical Biology, 366, 13–23.

Li,, Z., Dutta,, S., Sheng,, J., Tran,, P. N., Wu,, W., Chang,, K., … Colatsky,, T. (2017). Improving the in silico assessment of Proarrhythmia risk by combining hERG (human ether‐à‐go‐go‐related gene) channel‐drug binding kinetics and multichannel pharmacology. Circulation. Arrhythmia and Electrophysiology, 10, e004628.

Li,, Z., Dutta,, S., Sheng,, J., Tran,, P. N., Wu,, W., & Colatsky,, T. (2016). A temperature‐dependent in silico model of the human ether‐à‐go‐go‐related (hERG) gene channel. Journal of Pharmacological and Toxicological Methods, 81, 233–239.

Li,, Z., Ridder,, B. J., Han,, X., Wu,, W. W., Sheng,, J., Tran,, P. N., … Strauss,, D. G. (2019). Assessment of an in silico mechanistic model for Proarrhythmia risk prediction under the CiPA initiative. Clinical Pharmacology %26 Therapeutics, 105, 466–475.

Liepe,, J., Filippi,, S., Komorowski,, M., & Stumpf,, M. P. (2013). Maximizing the information content of experiments in systems biology. PLoS Computational Biology, 9, e1002888.

Linz,, K. W., & Meyer,, R. (1998). Control of L‐type calcium current during the action potential of Guinea‐pig ventricular myocytes. The Journal of Physiology, 513, 425–442.

Loewe,, A., Wilhelms,, M., Fischer,, F., Scholz,, E. P., Dössel,, O., & Seemann,, G. (2014). Arrhythmic potency of human ether‐à‐go‐go‐related gene mutations L532P and N588K in a computational model of human atrial myocytes. Europace, 16, 435–443.

Lombardo,, D. M., Fenton,, F. H., Narayan,, S. M., & Rappel,, W.‐J. (2016). Comparison of detailed and simplified models of human atrial myocytes to recapitulate patient specific properties. PLoS Computational Biology, 12, e1005060.

Lombardo,, D. M., & Rappel,, W.‐J. (2017). Systematic reduction of a detailed atrial myocyte model. Chaos: An Interdisciplinary Journal of Nonlinear Science, 27, 093914. https://doi.org/10.1063/1.4999611.

Luo,, C., & Rudy,, Y. (1994). A dynamic model of the cardiac ventricular action potential. I. Simulations of ionic currents and concentration changes. Circulation Research, 74, 1071–1096.

Lyddon,, S., Walker, S., & Holmes, C. C. (2018). Nonparametric learning from Bayesian models with randomized objective functions. ArXiv, abs/1806.11544.

MacLeod,, R., Stinstra,, J., Lew,, S., Whitaker,, R., Swenson,, D., Cole,, M., … Johnson,, C. (2009). Subject‐specific, multiscale simulation of electrophysiology: A software pipeline for image‐based models and application examples. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 367, 2293–2310.

Mahajan,, A., Shiferaw,, Y., Sato,, D., Baher,, A., Olcese,, R., Xie,, L.‐H., … Weiss,, J. N. (2008). A rabbit ventricular action potential model replicating cardiac dynamics at rapid heart rates. Biophysical Journal, 94, 392–410.

Maleckar,, M. M., Greenstein,, J. L., Giles,, W. R., & Trayanova,, N. A. (2009). K+ current changes account for the rate dependence of the action potential in the human atrial myocyte. American Journal of Physiology—Heart and Circulatory Physiology, 297, H1398–H1410.

Mann,, S. A., Imtiaz,, M., Winbo,, A., Rydberg,, A., Perry,, M. D., Couderc,, J.‐P., … Vandenberg,, J. I. (2016). Convergence of models of human ventricular myocyte electrophysiology after global optimization to recapitulate clinical long QT phenotypes. Journal of Molecular and Cellular Cardiology, 100, 25–34.

Marder,, E., & Goaillard,, J.‐M. (2006). Variability, compensation and homeostasis in neuron and network function. Nature Reviews Neuroscience, 7, 563–574.

Mathias,, R. T., Cohen,, I. S., & Oliva,, C. (1990). Limitations of the whole cell patch clamp technique in the control of intracellular concentrations. Biophysical Journal, 58, 759–770.

Mazhari,, R., Greenstein,, J. L., Winslow,, R. L., Marbán,, E., & Nuss,, H. B. (2001). Molecular interactions between two long‐QT syndrome gene products, HERG and KCNE2, rationalized by in vitro and in silico analysis. Circulation Research, 89, 33–38.

McAllister,, R. E., Noble,, D., & Tsien,, R. W. (1975). Reconstruction of the electrical activity of cardiac Purkinje fibres. The Journal of Physiology, 251, 1–59.

Menon,, V., Spruston,, N., & Kath,, W. L. (2009). A state‐mutating genetic algorithm to design ion‐channel models. Proceedings of the National Academy of Sciences, 106, 16829–16834.

Milescu,, L. S., Akk,, G., & Sachs,, F. (2005). Maximum likelihood estimation of ion channel kinetics from macroscopic currents. Biophysical Journal, 88, 2494–2515.

Milescu,, L. S., Yamanishi,, T., Ptak,, K., Mogri,, M. Z., & Smith,, J. C. (2008). Real‐time kinetic modeling of voltage‐gated ion channels using dynamic clamp. Biophysical Journal, 95, 66–87.

Millonas,, M. M., & Hanck,, D. A. (1998a). Nonequilibrium response spectroscopy and the molecular kinetics of proteins. Physical Review Letters, 80, 401–404.

Millonas,, M. M., & Hanck,, D. A. (1998b). Nonequilibrium response spectroscopy of voltage‐sensitive ion channel gating. Biophysical Journal, 74, 210–229.

Mirams,, G. R., Arthurs,, C. J., Bernabeu,, M. O., Bordas,, R., Cooper,, J., Corrias,, A., … Gavaghan,, D. J. (2013). Chaste: An open source C++ library for computational physiology and biology. PLoS Computational Biology, 9, e1002970.

Mirams,, G. R., Cui,, Y., Sher,, A., Fink,, M., Cooper,, J., Heath,, B. M., … Noble,, D. (2011). Simulation of multiple ion channel block provides improved early prediction of compounds` clinical torsadogenic risk. Cardiovascular Research, 91, 53–61.

Mirams,, G. R., Davies,, M. R., Brough,, S. J., Bridgland‐Taylor,, M. H., Cui,, Y., Gavaghan,, D. J., & Abi‐Gerges,, N. (2014). Prediction of thorough QT study results using action potential simulations based on ion channel screens. Journal of Pharmacological and Toxicological Methods, 70, 246–254.

Mirams,, G. R., Pathmanathan,, P., Gray,, R. A., Challenor,, P., & Clayton,, R. H. (2016). Uncertainty and variability in computational and mathematical models of cardiac physiology. The Journal of Physiology, 594, 6833–6847.

Mitchell,, C. C., & Schaeffer,, D. G. (2003). A two‐current model for the dynamics of cardiac membrane. Bulletin of Mathematical Biology, 65, 767–793.

Moreno,, J. D., Zhu,, W., Mangold,, K., Chung,, W., & Silva,, J. R. (2019). A molecularly detailed NaV1.5 model reveals a new class I antiarrhythmic target. JACC: Basic to Translational Science, 4, 736–751.

Muszkiewicz,, A., Britton,, O. J., Gemmell,, P., Passini,, E., Sánchez,, C., Zhou,, X., … Rodriguez,, B. (2016). Variability in cardiac electrophysiology: Using experimentally‐calibrated populations of models to move beyond the single virtual physiological human paradigm. Progress in Biophysics and Molecular Biology, 120, 115–127.

Neher,, E. (1995). Voltage offsets in patch‐clamp experiments. In B. Sakmann, & E. Neher, (Eds.), Single‐channel recording (2nd ed., pp. 147–153). Boston, MA: Springer.

Nelder,, J. A., & Mead,, R. (1965). A simplex method for function minimization. The Computer Journal, 7, 308–313.

Ni,, H., Morotti,, S., & Grandi,, E. (2018). A heart for diversity: Simulating variability in cardiac arrhythmia research. Frontiers in Physiology, 9, 958.

Niederer,, S., Fink,, M., Noble,, D., & Smith,, N. (2009). A meta‐analysis of cardiac electrophysiology computational models. Experimental Physiology, 94, 486–495.

Niederer,, S. A., Campbell,, K. S., & Campbell,, S. G. (2019). A short history of the development of mathematical models of cardiac mechanics. Journal of Molecular and Cellular Cardiology, 127, 11–19.

Noble,, D. (1962). A modification of the Hodgkin‐Huxley equations applicable to Purkinje fibre action and pacemaker potentials. The Journal of Physiology, 160, 317–352.

Noble,, D. (1965). Electrical properties of cardiac muscle attributable to inward going (anomalous) rectification. Journal of Cellular and Comparative Physiology, 66, 127–135.

Noble,, D., Garny,, A., & Noble,, P. (2012). How the Hodgkin‐Huxley equations inspired the Cardiac Physiome Project. The Journal of Physiology, 590, 2613–2628.

Noble,, D., & Rudy,, Y. (2001). Models of cardiac ventricular action potentials: Iterative interaction between experiment and simulation. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 359, 1127–1142.

Nygren,, A., Fiset,, C., Firek,, L., Clark,, J. W., Lindblad,, D. S., Clark,, R. B., & Giles,, W. R. (1998). Mathematical model of an adult human atrial cell the role of K+ currents in repolarization. Circulation Research, 82, 63–81.

O`Hara,, T., Virág,, L., Varró,, A., & Rudy,, Y. (2011). Simulation of the undiseased human cardiac ventricular action potential: Model formulation and experimental validation. PLoS Computational Biology, 7, e1002061. https://doi.org/10.1371/journal.pcbi.1002061.

Oliver,, R. A., & Krassowska,, W. (2005). Reproducing cardiac restitution properties using the Fenton‐Karma membrane model. Annals of Biomedical Engineering, 33, 907–911.

Owen,, J. S., & Fiedler‐Kelly,, J. (2014). Introduction to population pharmacokinetic/pharmacodynamic analysis with nonlinear mixed effects models. Hoboken, NJ: John Wiley %26 Sons.

Paci,, M., Hyttinen,, J., Aalto‐Setälä,, K., & Severi,, S. (2013). Computational models of ventricular‐ and atrial‐like human induced pluripotent stem cell derived cardiomyocytes. Annals of Biomedical Engineering, 41, 2334–2348.

Paci,, M., Passini,, E., Klimas,, A., Severi,, S., Hyttinen,, J., Rodriguez,, B., & Entcheva,, E. (2019). All‐optical electrophysiology refines populations of in silico human iPS‐CMs for drug evaluation. bioRxiv, 799478.

Paci,, M., Passini,, E., Severi,, S., Hyttinen,, J., & Rodriguez,, B. (2017). Phenotypic variability in LQT3 human induced pluripotent stem cell‐derived cardiomyocytes and their response to antiarrhythmic pharmacologic therapy: An in silico approach. Heart Rhythm, 14, 1704–1712.

Pan,, M., Gawthrop,, P. J., Tran,, K., Cursons,, J., & Crampin,, E. J. (2019). A thermodynamic framework for modelling membrane transporters. Journal of Theoretical Biology, 481, 10–23.

Passini,, E., Britton,, O. J., Lu,, H. R., Rohrbacher,, J., Hermans,, A. N., Gallacher,, D. J., … Rodriguez,, B. (2017). Human in silico drug trials demonstrate higher accuracy than animal models in predicting clinical pro‐arrhythmic cardiotoxicity. Frontiers in Physiology, 8, 668.

Passini,, E., Mincholé,, A., Coppini,, R., Cerbai,, E., Rodriguez,, B., Severi,, S., & Bueno‐Orovio,, A. (2016). Mechanisms of pro‐arrhythmic abnormalities in ventricular repolarisation and anti‐arrhythmic therapies in human hypertrophic cardiomyopathy. Journal of Molecular and Cellular Cardiology, 96, 72–81.

Pathmanathan,, P., & Gray,, R. (2018). Validation and trustworthiness of multiscale models of cardiac electrophysiology. Frontiers in Physiology, 9, 106.

Pathmanathan,, P., & Gray,, R. A. (2013). Ensuring reliability of safety‐critical clinical applications of computational cardiac models. Frontiers in Physiology, 4, 358.

Pathmanathan,, P., Gray,, R. A., Romero,, V. J., & Morrison,, T. M. (2017). Applicability analysis of validation evidence for biomedical computational models. Journal of Verification, Validation and Uncertainty Quantification, 2, 021005.

Pathmanathan,, P., Shotwell,, M. S., Gavaghan,, D. J., Cordeiro,, J. M., & Gray,, R. A. (2015). Uncertainty quantification of fast sodium current steady‐state inactivation for multi‐scale models of cardiac electrophysiology. Progress in Biophysics and Molecular Biology, 117, 4–18.

Perissinotti,, L., Guo,, J., De Biase,, P., Clancy,, C., Duff,, H., & Noskov,, S. (2015). Kinetic model for NS1643 drug activation of WT and L529i variants of Kv11.1 (hERG1) potassium channel. Biophysical Journal, 108, 1414–1424.

Plumlee,, M., Joseph,, V. R., Yang,, H., Roshan Joseph,, V., & Yang,, H. (2016). Calibrating functional parameters in the ion channel models of cardiac cells. Journal of the American Statistical Association, 111, 500–509.

Podziemski,, P., & Żebrowski,, J. J. (2013). A simple model of the right atrium of the human heart with the sinoatrial and atrioventricular nodes included. Journal of Clinical Monitoring and Computing, 27, 481–498.

Ponnaluri,, A. V. S., Perotti,, L. E., Liu,, M., Qu,, Z., Weiss,, J. N., Ennis,, D. B., … Garfinkel,, A. (2016). Electrophysiology of heart failure using a rabbit model: From the failing myocyte to ventricular fibrillation. PLoS Computational Biology, 12, e1004968.

Potse,, M., Krause,, D., Kroon,, W., Murzilli,, R., Muzzarelli,, S., Regoli,, F., … Auricchio,, A. (2014). Patient‐specific modelling of cardiac electrophysiology in heart‐failure patients. Europace, 16, iv56–iv61.

Prakosa,, A., Arevalo,, H. J., Deng,, D., Boyle,, P. M., Nikolov,, P. P., Ashikaga,, H., … Trayanova,, N. A. (2018). Personalized virtual‐heart technology for guiding the ablation of infarct‐related ventricular tachycardia. Nature Biomedical Engineering, 2, 732–740.

Qu,, Z., Weiss,, J. N., & Garfinkel,, A. (1999). Cardiac electrical restitution properties and stability of reentrant spiral waves: A simulation study. American Journal of Physiology—Heart and Circulatory Physiology, 276, H269–H283.

Quinn,, T. A., Granite,, S., Allessie,, M. A., Antzelevitch,, C., Bollensdorff,, C., Bub,, G., … Kohl,, P. (2011). Minimum information about a cardiac electrophysiology experiment (MICEE): Standardised reporting for model reproducibility, interoperability, and data sharing. Progress in Biophysics and Molecular Biology, 107, 4–10.

Raba,, A. E., Cordeiro,, J. M., Antzelevitch,, C., & Beaumont,, J. (2013). Extending the conditions of application of an inversion of the Hodgkin‐Huxley gating model. Bulletin of Mathematical Biology, 75, 752–773.

Rajamani,, S., Anderson,, C. L., Valdivia,, C. R., Eckhardt,, L. L., Foell,, J. D., Robertson,, G. A., … January,, C. T. (2006). Specific serine proteases selectively damage KCNH2 (hERG1) potassium channels and IKr. American Journal of Physiology—Heart and Circulatory Physiology, 290, H1278–H1288.

Ranjan,, R., Khazen,, G., Gambazzi,, L., Ramaswamy,, S., Hill,, S. L., Schürmann,, F., & Markram,, H. (2011). Channelpedia: An integrative and interactive database for ion channels. Frontiers in Neuroinformatics, 5, 36.

Ranjan,, R., Logette,, E., Marani,, M., Herzog,, M., Tâche,, V., Scantamburlo,, E., … Markram,, H. (2019). A kinetic map of the homomeric voltage‐gated potassium channel (Kv) family. Frontiers in Cellular Neuroscience, 13, 358.

Rasmusson,, R., Clark,, J., Giles,, W., Shibata,, E., & Campbell,, D. (1990). A mathematical model of a bullfrog cardiac pacemaker cell. American Journal of Physiology—Heart and Circulatory Physiology, 259, H352–H369.

Raue,, A., Schilling,, M., Bachmann,, J., Matteson,, A., Schelke,, M., Kaschek,, D., … Timmer,, J. (2013). Lessons learned from quantitative dynamical modeling in systems biology. PLoS One, 8, e74335.

Ravagli,, E., Bucchi,, A., Bartolucci,, C., Paina,, M., Baruscotti,, M., DiFrancesco,, D., & Severi,, S. (2016). Cell‐specific dynamic clamp analysis of the role of funny if current in cardiac pacemaking. Progress in Biophysics and Molecular Biology, 120, 50–66.

Read,, M. N., Alden,, K., Timmis,, J., & Andrews,, P. S. (2018). Strategies for calibrating models of biology. Briefings in Bioinformatics, bby092, https://doi.org/10.1093/bib/bby092.

Redfern,, W. S., Carlsson,, L., Davis,, A. S., Lynch,, W. G., MacKenzie,, I., Palethorpe,, S., … Hammond,, T. G. (2003). Relationships between preclinical cardiac electrophysiology, clinical QT interval prolongation and torsade de pointes for a broad range of drugs: Evidence for a provisional safety margin in drug development. Cardiovascular Research, 58, 32–45.

Rees,, C. M., Yang,, J.‐H., Santolini,, M., Lusis,, A. J., Weiss,, J. N., & Karma,, A. (2018). The Ca^{2+} transient as a feedback sensor controlling cardiomyocyte ionic conductances in mouse populations. eLife, 7, e36717.

Rice,, J. J., & Jafri,, M. S. (2001). Modelling calcium handling in cardiac cells. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 359, 1143–1157.

Richter,, Y., Lind,, P. G., Seemann,, G., & Maass,, P. (2017). Anatomical and spiral wave reentry in a simplified model for atrial electrophysiology. Journal of Theoretical Biology, 419, 100–107.

Roden,, D. M., Balser,, J. R., George,, A. L., Jr., & Anderson,, M. E. (2002). Cardiac ion channels. Annual Review of Physiology, 64, 431–475.

Rudy,, Y., & Silva,, J. R. (2006). Computational biology in the study of cardiac ion channels and cell electrophysiology. Quarterly Reviews of Biophysics, 39, 57–116.

Ryan,, E. G., Drovandi,, C. C., McGree,, J. M., & Pettitt,, A. N. (2016). A review of modern computational algorithms for bayesian optimal design. International Statistical Review, 84, 128–154.

Sadrieh,, A., Domanski,, L., Pitt‐Francis,, J., Mann,, S. A., Hodkinson,, E. C., Ng,, C.‐A., … Hill,, A. P. (2014). Multiscale cardiac modelling reveals the origins of notched T waves in long QT syndrome type 2. Nature Communications, 5, 5069.

Sarkar,, A. X., & Sobie,, E. A. (2010). Regression analysis for constraining free parameters in electrophysiological models of cardiac cells. PLoS Computational Biology, 6, e1000914.

Saucerman,, J. J., Brunton,, L. L., Michailova,, A. P., & McCulloch,, A. D. (2003). Modeling *β*‐adrenergic control of cardiac myocyte contractility in silico. Journal of Biological Chemistry, 278, 47997–48003.

Schälte,, Y., Stapor,, P., & Hasenauer,, J. (2018). Evaluation of derivative‐free optimizers for parameter estimation in systems biology. IFAC‐PapersOnLine, 51, 98–101.

Sher,, A. A., Wang,, K., Wathen,, A., Maybank,, P. J., Mirams,, G. R., Abramson,, D., … Gavaghan,, D. J. (2013). A local sensitivity analysis method for developing biological models with identifiable parameters: Application to cardiac ionic channel modelling. Future Generation Computer Systems, 29, 591–598.

Sherman,, A. J., Shrier,, A., & Cooper,, E. (1999). Series resistance compensation for whole‐cell patch‐clamp studies using a membrane state estimator. Biophysical Journal, 77, 2590–2601.

Shotwell,, M. S., & Gray,, R. A. (2016). Estimability analysis and optimal design in dynamic multi‐scale models of cardiac electrophysiology. Journal of Agricultural, Biological, and Environmental Statistics, 21, 261–276.

Siekmann,, I., Sneyd,, J., & Crampin,, E. (2012). MCMC can detect nonidentifiable models. Biophysical Journal, 103, 2275–2286.

Sigg,, D., & Bezanilla,, F. (2003). A physical model of potassium channel activation: From energy landscape to gating kinetics. Biophysical Journal, 84, 3703–3716.

Silva,, J. R., Pan,, H., Wu,, D., Nekouzadeh,, A., Decker,, K. F., Cui,, J., … Rudy,, Y. (2009). A multiscale model linking ion‐channel molecular dynamics and electrostatics to the cardiac action potential. Proceedings of the National Academy of Sciences of the United States of America, 106, 11102–11106.

Smith,, N., & Crampin,, E. (2004). Development of models of active ion transport for whole‐cell modelling: Cardiac sodium–potassium pump as a case study. Progress in Biophysics and Molecular Biology, 85, 387–405.

Smucker,, B., Krzywinski,, M., & Altman,, N. (2018). Optimal experimental design. Nature Methods, 15, 559–560.

Stadtländer,, C. T. K.‐H. (2018). Systems biology: Mathematical modeling and model analysis. Journal of Biological Dynamics, 12, 11–15.

Starmer,, C. F., Grant,, A. O., & Strauss,, H. C. (1984). Mechanisms of use‐dependent block of sodium channels in excitable membranes by local anesthetics. Biophysical Journal, 46, 15–27.

Sterratt,, D., Graham,, B., Gillies,, A., & Willshaw,, D. (2011). Principles of computational modelling in neuroscience. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511975899.

Syed,, Z., Vigmond,, E., Nattel,, S., & Leon,, L. J. (2005). Atrial cell action potential parameter fitting using genetic algorithms. Medical and Biological Engineering and Computing, 43, 561–571.

Teed,, Z. R., & Silva,, J. R. (2016). A computationally efficient algorithm for fitting ion channel parameters. MethodsX, 3, 577–588.

Terkildsen,, J. R., Crampin,, E. J., & Smith,, N. P. (2007). The balance between inactivation and activation of the Na+‐K+ pump underlies the triphasic accumulation of extracellular K+ during myocardial ischemia. American Journal of Physiology—Heart and Circulatory Physiology, 293, H3036–H3045.

Tomek,, J., Bueno‐Orovio,, A., Passini,, E., Zhou,, X., Minchole,, A., Britton,, O., … Rodriguez,, B. (2019). Development, calibration, and validation of a novel human ventricular myocyte model in health, disease, and drug block. eLife, 8, e48890.

Tran,, K., Smith,, N. P., Loiselle,, D. S., & Crampin,, E. J. (2009). A thermodynamic model of the cardiac sarcoplasmic/endoplasmic Ca^{2+} (SERCA) pump. Biophysical Journal, 96, 2029–2042.

Tsien,, R., & Noble,, D. (1969). A transition state theory approach to the kinetics of conductance changes in excitable membranes. The Journal of Membrane Biology, 1, 248–273.

ten Tusscher,, K. H. W. J., Noble,, D., Noble,, P. J., & Panfilov,, A. V. (2004). A model for human ventricular tissue. American Journal of Physiology ‐ Heart and Circulatory Physiology, 286, H1573–H1589.

ten Tusscher,, K. H. W. J., & Panfilov,, A. V. (2006). Alternans and spiral breakup in a human ventricular tissue model. American Journal of Physiology—Heart and Circulatory Physiology, 291, H1088–H1100.

Tveito,, A., Jæger,, K. H., Huebsch,, N., Charrez,, B., Edwards,, A. G., Wall,, S., & Healy,, K. E. (2018). Inversion and computational maturation of drug response using human stem cell derived cardiomyocytes in microphysiological systems. Scientific Reports, 8, 17626.

Tveito,, A., Lines,, G. T., Edwards,, A. G., & McCulloch,, A. (2016). Computing rates of Markov models of voltage‐gated ion channels by inverting partial differential equations governing the probability density functions of the conducting and non‐conducting states. Mathematical Biosciences, 277, 126–135.

US National Research Council. (2012). Assessing the reliability of complex models: Mathematical and statistical foundations of verification, validation, and uncertainty quantification. Washington, DC: National Academies Press. https://doi.org/10.17226/13395.

Vandenberg,, C., & Bezanilla,, F. (1991). A sodium channel gating model based on single channel, macroscopic ionic, and gating currents in the squid giant axon. Biophysical Journal, 60, 1511–1533.

Vandenberg,, J. I., Perry,, M. D., Perrin,, M. J., Mann,, S. A., Ke,, Y., & Hill,, A. P. (2012). hERG K+ channels: Structure, function, and clinical significance. Physiological Reviews, 92, 1393–1478.

VanDongen,, A. M. (2004). Idealization and simulation of single ion channel data. In Methods in enzymology (Vol. 383, pp. 229–244). Elsevier. https://doi.org/10.1016/S0076-6879(04)83010-6.

Verkerk,, A. O., & Wilders,, R. (2013). Hyperpolarization‐activated current, if in mathematical models of rabbit sinoatrial node pacemaker cells. BioMed Research International, 2013, 1–18.

Vernon,, I., Goldstein,, M., & Bower,, R. (2014). Galaxy formation: Bayesian history matching for the observable universe. Statistical Science, 29, 81–90.

Villaverde,, A. F., Barreiro,, A., & Papachristodoulou,, A. (2016). Structural identifiability of dynamic systems biology models. PLoS Computational Biology, 12, e1005153.

Villaverde,, A. F., Evans,, N. D., Chappell,, M. J., & Banga,, J. R. (2019). Input‐dependent structural identifiability of nonlinear systems. IEEE Control Systems Letters, 3, 272–277.

Walch,, O. J., & Eisenberg,, M. C. (2016). Parameter identifiability and identifiable combinations in generalized Hodgkin–Huxley models. Neurocomputing, 199, 137–143.

Waltemath,, D., Adams,, R., Bergmann,, F. T., Hucka,, M., Kolpakov,, F., Miller,, A. K., … le Novère,, N. (2011). Reproducible computational biology experiments with SED‐ML‐the simulation experiment description markup language. BMC Systems Biology, 5, 198.

Wang,, G. J., & Beaumont,, J. (2004). Parameter estimation of the Hodgkin–Huxley gating model: An inversion procedure. SIAM Journal on Applied Mathematics, 64, 1249–1267.

Waterfall,, J. J., Casey,, F. P., Gutenkunst,, R. N., Brown,, K. S., Myers,, C. R., Brouwer,, P. W., … Sethna,, J. P. (2006). Sloppy‐model universality class and the Vandermonde matrix. Physical Review Letters, 97, 150601.

Weiss,, J. N., Karma,, A., MacLellan,, W. R., Deng,, M., Rau,, C., Rees,, C. M., … Lusis,, A. J. (2012). “Good enough solutions” and the genetics of complex diseases. Circulation Research, 111, 493–504.

White,, J. A., Sekar,, N., & Kay,, A. R. (1995). Errors in persistent inward currents generated by space‐clamp errors: A modeling study. Journal of Neurophysiology, 73, 2369–2377.

Whittaker,, D. G., Benson,, A. P., Teh,, I., Schneider,, J. E., & Colman,, M. A. (2019). Investigation of the role of myocyte orientations in cardiac arrhythmia using image‐based models. Biophysical Journal, 117, 2396–2408.

Whittaker,, D. G., Colman,, M. A., Ni,, H., Hancox,, J. C., & Zhang,, H. (2018). Human atrial arrhythmogenesis and sinus bradycardia in KCNQ1‐linked short QT syndrome: Insights from computational modelling. Frontiers in Physiology, 9, 1402. https://doi.org/10.3389/fphys.2018.01402.

Whittaker,, D. G., Ni,, H., Harchi,, A. E., Hancox,, J. C., & Zhang,, H. (2017). Atrial arrhythmogenicity of KCNJ2 mutations in short QT syndrome: Insights from virtual human atria. PLoS Computational Biology, 13, e1005593.

Wilhelms,, M., Hettmann,, H., Maleckar,, M. M., Koivumäki,, J. T., Dössel,, O., & Seemann,, G. (2013). Benchmarking electrophysiological models of human atrial myocytes. Frontiers in Physiology, 3, 487. https://doi.org/10.3389/fphys.2012.00487

Willms,, A. R., Baro,, D. J., Harris‐Warrick,, R. M., & Guckenheimer,, J. (1999). An improved parameter estimation method for Hodgkin‐Huxley models. Journal of Computational Neuroscience, 6, 145–168.

Winslow,, R. L., Cortassa,, S., O`Rourke,, B., Hashambhoy,, Y. L., Rice,, J. J., & Greenstein,, J. L. (2011). Integrative modeling of the cardiac ventricular myocyte. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 3, 392–413.

Winslow,, R. L., Walker,, M. A., & Greenstein,, J. L. (2016). Modeling calcium regulation of contraction, energetics, signaling, and transcription in the cardiac myocyte. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 8, 37–67.

Workman,, A. J., Pau,, D., Redpath,, C. J., Marshall,, G. E., Russell,, J. A., Kane,, K. A., … Rankin,, A. C. (2006). Post‐operative atrial fibrillation is influenced by beta‐blocker therapy but not by pre‐operative atrial cellular electrophysiology. Journal of Cardiovascular Electrophysiology, 17, 1230–1238.

Yang,, P.‐C., & Clancy,, C. E. (2012). In silico prediction of sex‐based differences in human susceptibility to cardiac ventricular tachyarrhythmias. Frontiers in Physiology, 3, 360. https://doi.org/10.3389/fphys.2012.00360.

Yu,, T., Lloyd,, C. M., Nickerson,, D. P., Cooling,, M. T., Miller,, A. K., Garny,, A., … Nielsen,, P. M. F. (2011). The physiome model repository 2. Bioinformatics, 27, 743–744.

Yue,, L., Feng,, J., Li,, G. R., & Nattel,, S. (1996). Transient outward and delayed rectifier currents in canine atrium: Properties and role of isolation methods. American Journal of Physiology—Heart and Circulatory Physiology, 270, H2157–H2168.

Zaniboni,, M., Riva,, I., Cacciani,, F., & Groppi,, M. (2010). How different two almost identical action potentials can be: A model study on cardiac repolarization. Mathematical Biosciences, 228, 56–70.

Zaydman,, M. A., Kasimova,, M. A., McFarland,, K., Beller,, Z., Hou,, P., Kinser,, H. E., … Cui,, J. (2014). Domain–domain interactions determine the gating, permeation, pharmacology, and subunit modulation of the IKs ion channel. eLife, 3, e03606.

Zhang,, H., Holden,, A. V., Kodama,, I., Honjo,, H., Lei,, M., Varghese,, T., & Boyett,, M. R. (2000). Mathematical models of action potentials in the periphery and center of the rabbit sinoatrial node. American Journal of Physiology—Heart and Circulatory Physiology, 279, H397–H421.

Zhou,, Q., Zygmunt,, A. C., Cordeiro,, J. M., Siso‐Nadal,, F., Miller,, R. E., Buzzard,, G. T., & Fox,, J. J. (2009). Identification of IKr kinetics and drug binding in native myocytes. Annals of Biomedical Engineering, 37, 1294–1309.