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Zhang Y, Toyoda F, Himeno Y, Noma A, Amano A. Cell-specific models of hiPSC-CMs developed by the gradient-based parameter optimization method fitting two different action potential waveforms. Sci Rep 2024; 14:13086. [PMID: 38849433 PMCID: PMC11161598 DOI: 10.1038/s41598-024-63413-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 05/28/2024] [Indexed: 06/09/2024] Open
Abstract
Parameter optimization (PO) methods to determine the ionic current composition of experimental cardiac action potential (AP) waveform have been developed using a computer model of cardiac membrane excitation. However, it was suggested that fitting a single AP record in the PO method was not always successful in providing a unique answer because of a shortage of information. We found that the PO method worked perfectly if the PO method was applied to a pair of a control AP and a model output AP in which a single ionic current out of six current species, such as IKr, ICaL, INa, IKs, IKur or IbNSC was partially blocked in silico. When the target was replaced by a pair of experimental control and IKr-blocked records of APs generated spontaneously in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), the simultaneous fitting of the two waveforms by the PO method was hampered to some extent by the irregular slow fluctuations in the Vm recording and/or sporadic alteration in AP configurations in the hiPSC-CMs. This technical problem was largely removed by selecting stable segments of the records for the PO method. Moreover, the PO method was made fail-proof by running iteratively in identifying the optimized parameter set to reconstruct both the control and the IKr-blocked AP waveforms. In the lead potential analysis, the quantitative ionic mechanisms deduced from the optimized parameter set were totally consistent with the qualitative view of ionic mechanisms of AP so far described in physiological literature.
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Affiliation(s)
- Yixin Zhang
- Graduate School of Life Sciences, Ritsumeikan University, Kusatsu, Japan
| | - Futoshi Toyoda
- Department of Physiology, Shiga University of Medical Science, Otsu, Japan
- Central Research Laboratory, Shiga University of Medical Science, Otsu, Japan
| | - Yukiko Himeno
- Graduate School of Life Sciences, Ritsumeikan University, Kusatsu, Japan.
- Department of Physiology, Shiga University of Medical Science, Otsu, Japan.
| | - Akinori Noma
- Graduate School of Life Sciences, Ritsumeikan University, Kusatsu, Japan
| | - Akira Amano
- Graduate School of Life Sciences, Ritsumeikan University, Kusatsu, Japan
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2
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Grandi E, Navedo MF, Saucerman JJ, Bers DM, Chiamvimonvat N, Dixon RE, Dobrev D, Gomez AM, Harraz OF, Hegyi B, Jones DK, Krogh-Madsen T, Murfee WL, Nystoriak MA, Posnack NG, Ripplinger CM, Veeraraghavan R, Weinberg S. Diversity of cells and signals in the cardiovascular system. J Physiol 2023; 601:2547-2592. [PMID: 36744541 PMCID: PMC10313794 DOI: 10.1113/jp284011] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 01/19/2023] [Indexed: 02/07/2023] Open
Abstract
This white paper is the outcome of the seventh UC Davis Cardiovascular Research Symposium on Systems Approach to Understanding Cardiovascular Disease and Arrhythmia. This biannual meeting aims to bring together leading experts in subfields of cardiovascular biomedicine to focus on topics of importance to the field. The theme of the 2022 Symposium was 'Cell Diversity in the Cardiovascular System, cell-autonomous and cell-cell signalling'. Experts in the field contributed their experimental and mathematical modelling perspectives and discussed emerging questions, controversies, and challenges in examining cell and signal diversity, co-ordination and interrelationships involved in cardiovascular function. This paper originates from the topics of formal presentations and informal discussions from the Symposium, which aimed to develop a holistic view of how the multiple cell types in the cardiovascular system integrate to influence cardiovascular function, disease progression and therapeutic strategies. The first section describes the major cell types (e.g. cardiomyocytes, vascular smooth muscle and endothelial cells, fibroblasts, neurons, immune cells, etc.) and the signals involved in cardiovascular function. The second section emphasizes the complexity at the subcellular, cellular and system levels in the context of cardiovascular development, ageing and disease. Finally, the third section surveys the technological innovations that allow the interrogation of this diversity and advancing our understanding of the integrated cardiovascular function and dysfunction.
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Affiliation(s)
- Eleonora Grandi
- Department of Pharmacology, University of California Davis, Davis, CA, USA
| | - Manuel F. Navedo
- Department of Pharmacology, University of California Davis, Davis, CA, USA
| | - Jeffrey J. Saucerman
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Donald M. Bers
- Department of Pharmacology, University of California Davis, Davis, CA, USA
| | - Nipavan Chiamvimonvat
- Department of Pharmacology, University of California Davis, Davis, CA, USA
- Department of Internal Medicine, University of California Davis, Davis, CA, USA
| | - Rose E. Dixon
- Department of Physiology and Membrane Biology, University of California Davis, Davis, CA, USA
| | - Dobromir Dobrev
- Institute of Pharmacology, West German Heart and Vascular Center, University Duisburg-Essen, Essen, Germany
- Department of Medicine, Montreal Heart Institute and Université de Montréal, Montréal, Canada
- Department of Molecular Physiology & Biophysics, Baylor College of Medicine, Houston, TX, USA
| | - Ana M. Gomez
- Signaling and Cardiovascular Pathophysiology-UMR-S 1180, INSERM, Université Paris-Saclay, Orsay, France
| | - Osama F. Harraz
- Department of Pharmacology, Larner College of Medicine, and Vermont Center for Cardiovascular and Brain Health, University of Vermont, Burlington, VT, USA
| | - Bence Hegyi
- Department of Pharmacology, University of California Davis, Davis, CA, USA
| | - David K. Jones
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Trine Krogh-Madsen
- Department of Physiology & Biophysics, Weill Cornell Medicine, New York, New York, USA
| | - Walter Lee Murfee
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
| | - Matthew A. Nystoriak
- Department of Medicine, Division of Environmental Medicine, Center for Cardiometabolic Science, University of Louisville, Louisville, KY, 40202, USA
| | - Nikki G. Posnack
- Department of Pediatrics, Department of Pharmacology and Physiology, The George Washington University, Washington, DC, USA
- Sheikh Zayed Institute for Pediatric and Surgical Innovation, Children’s National Heart Institute, Children’s National Hospital, Washington, DC, USA
| | | | - Rengasayee Veeraraghavan
- Department of Biomedical Engineering, The Ohio State University, Columbus, OH, USA
- Dorothy M. Davis Heart & Lung Research Institute, The Ohio State University – Wexner Medical Center, Columbus, OH, USA
| | - Seth Weinberg
- Department of Biomedical Engineering, The Ohio State University, Columbus, OH, USA
- Dorothy M. Davis Heart & Lung Research Institute, The Ohio State University – Wexner Medical Center, Columbus, OH, USA
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3
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Nieto Ramos A, Fenton FH, Cherry EM. Bayesian inference for fitting cardiac models to experiments: estimating parameter distributions using Hamiltonian Monte Carlo and approximate Bayesian computation. Med Biol Eng Comput 2023; 61:75-95. [PMID: 36322242 DOI: 10.1007/s11517-022-02685-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 10/02/2022] [Indexed: 01/07/2023]
Abstract
Customization of cardiac action potential models has become increasingly important with the recognition of patient-specific models and virtual patient cohorts as valuable predictive tools. Nevertheless, developing customized models by fitting parameters to data poses technical and methodological challenges: despite noise and variability associated with real-world datasets, traditional optimization methods produce a single "best-fit" set of parameter values. Bayesian estimation methods seek distributions of parameter values given the data by obtaining samples from the target distribution, but in practice widely known Bayesian algorithms like Markov chain Monte Carlo tend to be computationally inefficient and scale poorly with the dimensionality of parameter space. In this paper, we consider two computationally efficient Bayesian approaches: the Hamiltonian Monte Carlo (HMC) algorithm and the approximate Bayesian computation sequential Monte Carlo (ABC-SMC) algorithm. We find that both methods successfully identify distributions of model parameters for two cardiac action potential models using model-derived synthetic data and an experimental dataset from a zebrafish heart. Although both methods appear to converge to the same distribution family and are computationally efficient, HMC generally finds narrower marginal distributions, while ABC-SMC is less sensitive to the algorithmic settings including the prior distribution.
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Affiliation(s)
- Alejandro Nieto Ramos
- School of Mathematical Sciences, Rochester Institute of Technology, 1 Lomb Memorial Drive, 14623, Rochester, NY, USA.,Epilepsy Center, Neurological Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA
| | - Flavio H Fenton
- School of Physics, Georgia Institute of Technology, 837 State Street NW, 30332, Atlanta, GA, USA
| | - Elizabeth M Cherry
- School of Computational Science and Engineering, Georgia Institute of Technology, 756 West Peachtree Street, 30308, Atlanta, GA, USA.
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4
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Kohjitani H, Koda S, Himeno Y, Makiyama T, Yamamoto Y, Yoshinaga D, Wuriyanghai Y, Kashiwa A, Toyoda F, Zhang Y, Amano A, Noma A, Kimura T. Gradient-based parameter optimization method to determine membrane ionic current composition in human induced pluripotent stem cell-derived cardiomyocytes. Sci Rep 2022; 12:19110. [PMID: 36351955 PMCID: PMC9646722 DOI: 10.1038/s41598-022-23398-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 10/31/2022] [Indexed: 11/11/2022] Open
Abstract
Premature cardiac myocytes derived from human induced pluripotent stem cells (hiPSC-CMs) show heterogeneous action potentials (APs), probably due to different expression patterns of membrane ionic currents. We developed a method for determining expression patterns of functional channels in terms of whole-cell ionic conductance (Gx) using individual spontaneous AP configurations. It has been suggested that apparently identical AP configurations can be obtained using different sets of ionic currents in mathematical models of cardiac membrane excitation. If so, the inverse problem of Gx estimation might not be solved. We computationally tested the feasibility of the gradient-based optimization method. For a realistic examination, conventional 'cell-specific models' were prepared by superimposing the model output of AP on each experimental AP recorded by conventional manual adjustment of Gxs of the baseline model. Gxs of 4-6 major ionic currents of the 'cell-specific models' were randomized within a range of ± 5-15% and used as an initial parameter set for the gradient-based automatic Gxs recovery by decreasing the mean square error (MSE) between the target and model output. Plotting all data points of the MSE-Gx relationship during optimization revealed progressive convergence of the randomized population of Gxs to the original value of the cell-specific model with decreasing MSE. The absence of any other local minimum in the global search space was confirmed by mapping the MSE by randomizing Gxs over a range of 0.1-10 times the control. No additional local minimum MSE was obvious in the whole parameter space, in addition to the global minimum of MSE at the default model parameter.
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Affiliation(s)
- Hirohiko Kohjitani
- grid.258799.80000 0004 0372 2033Department of Cardiovascular Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Shigeya Koda
- grid.262576.20000 0000 8863 9909Graduate School of Life Sciences, Ritsumeikan University, Kusatsu, Japan
| | - Yukiko Himeno
- grid.262576.20000 0000 8863 9909Graduate School of Life Sciences, Ritsumeikan University, Kusatsu, Japan
| | - Takeru Makiyama
- grid.258799.80000 0004 0372 2033Department of Cardiovascular Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yuta Yamamoto
- grid.258799.80000 0004 0372 2033Department of Cardiovascular Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Daisuke Yoshinaga
- grid.258799.80000 0004 0372 2033Department Pediatrics, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yimin Wuriyanghai
- grid.258799.80000 0004 0372 2033Department of Cardiovascular Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Asami Kashiwa
- grid.258799.80000 0004 0372 2033Department of Cardiovascular Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Futoshi Toyoda
- grid.410827.80000 0000 9747 6806Department of Physiology, Shiga University of Medical Science, Otsu, Japan
| | - Yixin Zhang
- grid.262576.20000 0000 8863 9909Graduate School of Life Sciences, Ritsumeikan University, Kusatsu, Japan
| | - Akira Amano
- grid.262576.20000 0000 8863 9909Graduate School of Life Sciences, Ritsumeikan University, Kusatsu, Japan
| | - Akinori Noma
- grid.262576.20000 0000 8863 9909Graduate School of Life Sciences, Ritsumeikan University, Kusatsu, Japan
| | - Takeshi Kimura
- grid.258799.80000 0004 0372 2033Department of Cardiovascular Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
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5
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Loppini A, Erhardt J, Fenton FH, Filippi S, Hörning M, Gizzi A. Optical Ultrastructure of Large Mammalian Hearts Recovers Discordant Alternans by In Silico Data Assimilation. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:866101. [PMID: 36926104 PMCID: PMC10012998 DOI: 10.3389/fnetp.2022.866101] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 03/04/2022] [Indexed: 12/12/2022]
Abstract
Understanding and predicting the mechanisms promoting the onset and sustainability of cardiac arrhythmias represent a primary concern in the scientific and medical communities still today. Despite the long-lasting effort in clinical and physico-mathematical research, a critical aspect to be fully characterized and unveiled is represented by spatiotemporal alternans patterns of cardiac excitation. The identification of discordant alternans and higher-order alternating rhythms by advanced data analyses as well as their prediction by reliable mathematical models represents a major avenue of research for a broad and multidisciplinary scientific community. Current limitations concern two primary aspects: 1) robust and general-purpose feature extraction techniques and 2) in silico data assimilation within reliable and predictive mathematical models. Here, we address both aspects. At first, we extend our previous works on Fourier transformation imaging (FFI), applying the technique to whole-ventricle fluorescence optical mapping. Overall, we identify complex spatial patterns of voltage alternans and characterize higher-order rhythms by a frequency-series analysis. Then, we integrate the optical ultrastructure obtained by FFI analysis within a fine-tuned electrophysiological mathematical model of the cardiac action potential. We build up a novel data assimilation procedure demonstrating its reliability in reproducing complex alternans patterns in two-dimensional computational domains. Finally, we prove that the FFI approach applied to both experimental and simulated signals recovers the same information, thus closing the loop between the experiment, data analysis, and numerical simulations.
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Affiliation(s)
- Alessandro Loppini
- Nonlinear Physics and Mathematical Modeling Laboratory, University Campus Bio-Medico of Rome, Rome, Italy
| | - Julia Erhardt
- Biobased Materials Laboratory, Institute of Biomaterials and Biomolecular Systems, Faculty of Energy, Process and Biotechnology, University of Stuttgart, Stuttgart, Germany
| | - Flavio H Fenton
- School of Physics, Georgia Institute of Technology, Atlanta, GA, United States
| | - Simonetta Filippi
- Nonlinear Physics and Mathematical Modeling Laboratory, University Campus Bio-Medico of Rome, Rome, Italy
| | - Marcel Hörning
- Biobased Materials Laboratory, Institute of Biomaterials and Biomolecular Systems, Faculty of Energy, Process and Biotechnology, University of Stuttgart, Stuttgart, Germany
| | - Alessio Gizzi
- Nonlinear Physics and Mathematical Modeling Laboratory, University Campus Bio-Medico of Rome, Rome, Italy
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6
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Coveney S, Corrado C, Oakley JE, Wilkinson RD, Niederer SA, Clayton RH. Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators. Front Physiol 2021; 12:693015. [PMID: 34366883 PMCID: PMC8339909 DOI: 10.3389/fphys.2021.693015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 06/28/2021] [Indexed: 11/13/2022] Open
Abstract
Calibration of cardiac electrophysiology models is a fundamental aspect of model personalization for predicting the outcomes of cardiac therapies, simulation testing of device performance for a range of phenotypes, and for fundamental research into cardiac function. Restitution curves provide information on tissue function and can be measured using clinically feasible measurement protocols. We introduce novel "restitution curve emulators" as probabilistic models for performing model exploration, sensitivity analysis, and Bayesian calibration to noisy data. These emulators are built by decomposing restitution curves using principal component analysis and modeling the resulting coordinates with respect to model parameters using Gaussian processes. Restitution curve emulators can be used to study parameter identifiability via sensitivity analysis of restitution curve components and rapid inference of the posterior distribution of model parameters given noisy measurements. Posterior uncertainty about parameters is critical for making predictions from calibrated models, since many parameter settings can be consistent with measured data and yet produce very different model behaviors under conditions not effectively probed by the measurement protocols. Restitution curve emulators are therefore promising probabilistic tools for calibrating electrophysiology models.
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Affiliation(s)
- Sam Coveney
- Insigneo Institute for In-Silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Cesare Corrado
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Jeremy E. Oakley
- School of Mathematics and Statistics, University of Sheffield, Sheffield, United Kingdom
| | - Richard D. Wilkinson
- School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
| | - Steven A. Niederer
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Richard H. Clayton
- Insigneo Institute for In-Silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
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7
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Aronis KN, Prakosa A, Bergamaschi T, Berger RD, Boyle PM, Chrispin J, Ju S, Marine JE, Sinha S, Tandri H, Ashikaga H, Trayanova NA. Characterization of the Electrophysiologic Remodeling of Patients With Ischemic Cardiomyopathy by Clinical Measurements and Computer Simulations Coupled With Machine Learning. Front Physiol 2021; 12:684149. [PMID: 34335294 PMCID: PMC8317643 DOI: 10.3389/fphys.2021.684149] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 06/22/2021] [Indexed: 11/13/2022] Open
Abstract
Rationale Patients with ischemic cardiomyopathy (ICMP) are at high risk for malignant arrhythmias, largely due to electrophysiological remodeling of the non-infarcted myocardium. The electrophysiological properties of the non-infarcted myocardium of patients with ICMP remain largely unknown. Objectives To assess the pro-arrhythmic behavior of non-infarcted myocardium in ICMP patients and couple computational simulations with machine learning to establish a methodology for the development of disease-specific action potential models based on clinically measured action potential duration restitution (APDR) data. Methods and Results We enrolled 22 patients undergoing left-sided ablation (10 ICMP) and compared APDRs between ICMP and structurally normal left ventricles (SNLVs). APDRs were clinically assessed with a decremental pacing protocol. Using genetic algorithms (GAs), we constructed populations of action potential models that incorporate the cohort-specific APDRs. The variability in the populations of ICMP and SNLV models was captured by clustering models based on their similarity using unsupervised machine learning. The pro-arrhythmic potential of ICMP and SNLV models was assessed in cell- and tissue-level simulations. Clinical measurements established that ICMP patients have a steeper APDR slope compared to SNLV (by 38%, p < 0.01). In cell-level simulations, APD alternans were induced in ICMP models at a longer cycle length compared to SNLV models (385–400 vs 355 ms). In tissue-level simulations, ICMP models were more susceptible for sustained functional re-entry compared to SNLV models. Conclusion Myocardial remodeling in ICMP patients is manifested as a steeper APDR compared to SNLV, which underlies the greater arrhythmogenic propensity in these patients, as demonstrated by cell- and tissue-level simulations using action potential models developed by GAs from clinical measurements. The methodology presented here captures the uncertainty inherent to GAs model development and provides a blueprint for use in future studies aimed at evaluating electrophysiological remodeling resulting from other cardiac diseases.
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Affiliation(s)
- Konstantinos N Aronis
- Section of Electrophysiology, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States.,Department of Biomedical Engineering, The Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Adityo Prakosa
- Department of Biomedical Engineering, The Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Teya Bergamaschi
- Department of Biomedical Engineering, The Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Ronald D Berger
- Section of Electrophysiology, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Patrick M Boyle
- Department of Biomedical Engineering, The Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Jonathan Chrispin
- Section of Electrophysiology, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Suyeon Ju
- Department of Biomedical Engineering, The Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Joseph E Marine
- Section of Electrophysiology, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Sunil Sinha
- Section of Electrophysiology, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Harikrishna Tandri
- Section of Electrophysiology, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Hiroshi Ashikaga
- Section of Electrophysiology, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Natalia A Trayanova
- Section of Electrophysiology, Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD, United States.,Department of Biomedical Engineering, The Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States
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8
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Belletti R, Romero L, Martinez-Mateu L, Cherry EM, Fenton FH, Saiz J. Arrhythmogenic Effects of Genetic Mutations Affecting Potassium Channels in Human Atrial Fibrillation: A Simulation Study. Front Physiol 2021; 12:681943. [PMID: 34135774 PMCID: PMC8201780 DOI: 10.3389/fphys.2021.681943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 05/05/2021] [Indexed: 11/16/2022] Open
Abstract
Genetic mutations in genes encoding for potassium channel protein structures have been recently associated with episodes of atrial fibrillation in asymptomatic patients. The aim of this study is to investigate the potential arrhythmogenicity of three gain-of-function mutations related to atrial fibrillation-namely, KCNH2 T895M, KCNH2 T436M, and KCNE3-V17M-using modeling and simulation of the electrophysiological activity of the heart. A genetic algorithm was used to tune the parameters' value of the original ionic currents to reproduce the alterations experimentally observed caused by the mutations. The effects on action potentials, ionic currents, and restitution properties were analyzed using versions of the Courtemanche human atrial myocyte model in different tissues: pulmonary vein, right, and left atrium. Atrial susceptibility of the tissues to spiral wave generation was also investigated studying the temporal vulnerability. The presence of the three mutations resulted in an overall more arrhythmogenic substrate. Higher current density, action potential duration shortening, and flattening of the restitution curves were the major effects of the three mutations at the single-cell level. The genetic mutations at the tissue level induced a higher temporal vulnerability to the rotor's initiation and progression, by sustaining spiral waves that perpetuate until the end of the simulation. The mutation with the highest pro-arrhythmic effects, exhibiting the widest sustained VW and the smallest meandering rotor's tip areas, was KCNE3-V17M. Moreover, the increased susceptibility to arrhythmias and rotor's stability was tissue-dependent. Pulmonary vein tissues were more prone to rotor's initiation, while in left atrium tissues rotors were more easily sustained. Re-entries were also progressively more stable in pulmonary vein tissue, followed by the left atrium, and finally the right atrium. The presence of the genetic mutations increased the susceptibility to arrhythmias by promoting the rotor's initiation and maintenance. The study provides useful insights into the mechanisms underlying fibrillatory events caused by KCNH2 T895M, KCNH2 T436M, and KCNE3-V17M and might aid the planning of patient-specific targeted therapies.
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Affiliation(s)
- Rebecca Belletti
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Valencia, Spain
| | - Lucia Romero
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Valencia, Spain
| | - Laura Martinez-Mateu
- Departamento de Teoría de la Señal y Comunicaciones y Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, Madrid, Spain
| | - Elizabeth M. Cherry
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Flavio H. Fenton
- School of Physics, Georgia Institute of Technology, Atlanta, GA, United States
| | - Javier Saiz
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Valencia, Spain
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9
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Whittaker DG, Clerx M, Lei CL, Christini DJ, Mirams GR. Calibration of ionic and cellular cardiac electrophysiology models. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2020; 12:e1482. [PMID: 32084308 PMCID: PMC8614115 DOI: 10.1002/wsbm.1482] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 01/17/2020] [Accepted: 01/18/2020] [Indexed: 12/30/2022]
Abstract
Cardiac electrophysiology models are among the most mature and well-studied mathematical models of biological systems. This maturity is bringing new challenges as models are being used increasingly to make quantitative rather than qualitative predictions. As such, calibrating the parameters within ion current and action potential (AP) models to experimental data sets is a crucial step in constructing a predictive model. This review highlights some of the fundamental concepts in cardiac model calibration and is intended to be readily understood by computational and mathematical modelers working in other fields of biology. We discuss the classic and latest approaches to calibration in the electrophysiology field, at both the ion channel and cellular AP scales. We end with a discussion of the many challenges that work to date has raised and the need for reproducible descriptions of the calibration process to enable models to be recalibrated to new data sets and built upon for new studies. This article is categorized under: Analytical and Computational Methods > Computational Methods Physiology > Mammalian Physiology in Health and Disease Models of Systems Properties and Processes > Cellular Models.
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Affiliation(s)
- Dominic G. Whittaker
- Centre for Mathematical Medicine & Biology, School of Mathematical SciencesUniversity of NottinghamNottinghamUK
| | - Michael Clerx
- Computational Biology & Health Informatics, Department of Computer ScienceUniversity of OxfordOxfordUK
| | - Chon Lok Lei
- Computational Biology & Health Informatics, Department of Computer ScienceUniversity of OxfordOxfordUK
| | | | - Gary R. Mirams
- Centre for Mathematical Medicine & Biology, School of Mathematical SciencesUniversity of NottinghamNottinghamUK
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10
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Hoffman MJ, Cherry EM. Sensitivity of a data-assimilation system for reconstructing three-dimensional cardiac electrical dynamics. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2020; 378:20190388. [PMID: 32448069 PMCID: PMC7287341 DOI: 10.1098/rsta.2019.0388] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/25/2020] [Indexed: 05/21/2023]
Abstract
Modelling of cardiac electrical behaviour has led to important mechanistic insights, but important challenges, including uncertainty in model formulations and parameter values, make it difficult to obtain quantitatively accurate results. An alternative approach is combining models with observations from experiments to produce a data-informed reconstruction of system states over time. Here, we extend our earlier data-assimilation studies using an ensemble Kalman filter to reconstruct a three-dimensional time series of states with complex spatio-temporal dynamics using only surface observations of voltage. We consider the effects of several algorithmic and model parameters on the accuracy of reconstructions of known scroll-wave truth states using synthetic observations. In particular, we study the algorithm's sensitivity to parameters governing different parts of the process and its robustness to several model-error conditions. We find that the algorithm can achieve an acceptable level of error in many cases, with the weakest performance occurring for model-error cases and more extreme parameter regimes with more complex dynamics. Analysis of the poorest-performing cases indicates an initial decrease in error followed by an increase when the ensemble spread is reduced. Our results suggest avenues for further improvement through increasing ensemble spread by incorporating additive inflation or using a parameter or multi-model ensemble. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.
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Affiliation(s)
- Matthew J. Hoffman
- School of Mathematical Sciences, Rochester Institute of Technology, Rochester, NY 14623, USA
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Cusimano N, Gizzi A, Fenton F, Filippi S, Gerardo-Giorda L. Key aspects for effective mathematical modelling of fractional-diffusion in cardiac electrophysiology: a quantitative study. COMMUNICATIONS IN NONLINEAR SCIENCE & NUMERICAL SIMULATION 2020; 84:105152. [PMID: 32863678 PMCID: PMC7453933 DOI: 10.1016/j.cnsns.2019.105152] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Microscopic structural features of cardiac tissue play a fundamental role in determining complex spatio-temporal excitation dynamics at the macroscopic level. Recent efforts have been devoted to the development of mathematical models accounting for non-local spatio-temporal coupling able to capture these complex dynamics without the need of resolving tissue heterogeneities down to the micro-scale. In this work, we analyse in detail several important aspects affecting the overall predictive power of these modelling tools and provide some guidelines for an effective use of space-fractional models of cardiac electrophysiology in practical applications. Through an extensive computational study in simplified computational domains, we highlight the robustness of models belonging to different categories, i.e., physiological and phenomenological descriptions, against the introduction of non-locality, and lay down the foundations for future research and model validation against experimental data. A modern genetic algorithm framework is used to investigate proper parameterisations of the considered models, and the crucial role played by the boundary assumptions in the considered settings is discussed. Several numerical results are provided to support our claims.
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Affiliation(s)
- N. Cusimano
- Basque Center for Applied Mathematics, Alameda de Mazarredo 14, 48009 Bilbao, Spain
| | - A. Gizzi
- Department of Engineering, University of Rome Campus Bio-Medico, via A. del Portillo 21, 00128 Rome, Italy
| | - F.H. Fenton
- School of Physics, Georgia Insitute of Technology, 837 State Street NW, Atlanta, GA 30332, United States
| | - S. Filippi
- Department of Engineering, University of Rome Campus Bio-Medico, via A. del Portillo 21, 00128 Rome, Italy
| | - L. Gerardo-Giorda
- Basque Center for Applied Mathematics, Alameda de Mazarredo 14, 48009 Bilbao, Spain
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Kaboudian A, Cherry EM, Fenton FH. Real-time interactive simulations of large-scale systems on personal computers and cell phones: Toward patient-specific heart modeling and other applications. SCIENCE ADVANCES 2019; 5:eaav6019. [PMID: 30944861 PMCID: PMC6436932 DOI: 10.1126/sciadv.aav6019] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 12/14/2018] [Indexed: 05/26/2023]
Abstract
Cardiac dynamics modeling has been useful for studying and treating arrhythmias. However, it is a multiscale problem requiring the solution of billions of differential equations describing the complex electrophysiology of interconnected cells. Therefore, large-scale cardiac modeling has been limited to groups with access to supercomputers and clusters. Many areas of computational science face similar problems where computational costs are too high for personal computers so that supercomputers or clusters currently are necessary. Here, we introduce a new approach that makes high-performance simulation of cardiac dynamics and other large-scale systems like fluid flow and crystal growth accessible to virtually anyone with a modest computer. For cardiac dynamics, this approach will allow not only scientists and students but also physicians to use physiologically accurate modeling and simulation tools that are interactive in real time, thereby making diagnostics, research, and education available to a broader audience and pushing the boundaries of cardiac science.
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Affiliation(s)
- Abouzar Kaboudian
- School of Physics, Georgia Institute of Technology, Atlanta, GA, USA
| | - Elizabeth M. Cherry
- School of Mathematical Sciences, Rochester Institute of Technology, Rochester, NY, USA
| | - Flavio H. Fenton
- School of Physics, Georgia Institute of Technology, Atlanta, GA, USA
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Coveney S, Clayton RH. Fitting two human atrial cell models to experimental data using Bayesian history matching. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2018; 139:43-58. [PMID: 30145156 DOI: 10.1016/j.pbiomolbio.2018.08.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Revised: 08/02/2018] [Accepted: 08/06/2018] [Indexed: 12/18/2022]
Abstract
Cardiac cell models are potentially valuable tools for applications such as quantitative safety pharmacology, but have many parameters. Action potentials in real cardiac cells also vary from beat to beat, and from one cell to another. Calibrating cardiac cell models to experimental observations is difficult, because the parameter space is large and high-dimensional. In this study we have demonstrated the use of history matching to calibrate the maximum conductance of ion channels and exchangers in two detailed models of the human atrial action potential against measurements of action potential biomarkers. History matching is an approach developed in other modelling communities, based on constructing fast-running Gaussian process emulators of the model. Emulators were constructed from a small number of model runs (around 102), and then run many times (>106) at low computational cost, each time with a different set of model parameters. Emulator outputs were compared with experimental biomarkers using an implausibility measure, which took into account experimental variance as well as emulator variance. By repeating this process, the region of non-implausible parameter space was iteratively reduced. Both cardiac cell models were successfully calibrated to experimental datasets, resulting in sets of parameters that could be sampled to produce variable action potentials. However, model parameters did not occupy a small range of values. Instead, the history matching process exposed inputs that can co-vary across a wide range and still be consistent with a particular biomarker. We also found correlations between some biomarkers, indicating a need for better descriptors of action potential shape.
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Affiliation(s)
- Sam Coveney
- Insigneo Institute for in-silico Medicine and Department of Computer Science, University of Sheffield, UK.
| | - Richard H Clayton
- Insigneo Institute for in-silico Medicine and Department of Computer Science, University of Sheffield, UK.
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