Models Of Cardiac Electrophysiology Research Articles

Myocardial ischemia simulation based on a multi-scale heart electrophysiology model.

Myocardial ischemia, caused by insufficient myocardial blood supply, is a leading cause of human death worldwide. Therefore, it is crucial to prioritize the prevention and treatment of this condition. Mathematical modeling is a powerful technique for studying heart diseases. The aim of this study was to discuss the quantitative relationship between extracellular potassium concentration and the degree of myocardial ischemia directly related to it. A human cardiac electrophysiological multiscale model was developed to calculate action potentials of all cells simultaneously, enhancing efficiency over traditional reaction-diffusion models. Contrary to the commonly held view that myocardial ischemia is caused by an increase in extracellular potassium concentration, our simulation results indicate that level 1 ischemia is associated with a decrease in extracellular potassium concentration. This unusual finding provides a new perspective on the mechanisms underlying myocardial ischemia and has the potential to lead to the development of new diagnostic and treatment strategies.

A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data.

Computational models of cardiac electrophysiology have gradually matured during the past few decades and are now being personalised to provide patient-specific therapy guidance for improving suboptimal treatment outcomes. The predictive features of these personalised electrophysiology models hold the promise of providing optimal treatment planning, which is currently limited in the clinic owing to reliance on a population-based or average patient approach. The generation of a personalised electrophysiology model entails a sequence of steps for which a range of activation mapping, calibration methods and therapy simulation pipelines have been suggested. However, the optimal methods that can potentially constitute a clinically relevant in silico treatment are still being investigated and face limitations, such as uncertainty of electroanatomical data recordings, generation and calibration of models within clinical timelines and requirements to validate or benchmark the recovered tissue parameters. This paper is aimed at reporting techniques on the personalisation of cardiac computational models, with a focus on calibrating cardiac tissue conductivity based on electroanatomical mapping data.

ForCEPSS—A framework for cardiac electrophysiology simulations standardization

Background and Objective:Simulation of cardiac electrophysiology (CEP) is an important research tool that is increasingly being adopted in industrial and clinical applications. Typical workflows for CEP simulation consist of a sequence of processing stages starting with building an anatomical model and then calibrating its electrophysiological properties to match observable data. While the calibration stages are common and generalizable, most CEP studies re-implement these steps in complex and highly variable workflows. This lack of standardization renders the execution of computational CEP studies in an efficient, robust, and reproducible manner a significant challenge. Here, we propose ForCEPSS as an efficient and robust, yet flexible, software framework for standardizing CEP simulation studies. Methods and Results:Key processing stages of CEP simulation studies are identified and implemented in a standardized workflow that builds on openCARP11https://opencarp.org/. Plank et al. (2021) and the Python-based carputils22https://git.opencarp.org/openCARP/carputils. framework. Stages include (i) the definition and initialization of action potential phenotypes, (ii) the tissue scale calibration of conduction properties, (iii) the functional initialization to approximate a limit cycle corresponding to the dynamic reference state according to an experimental protocol, and, (iv) the execution of the CEP study where the electrophysiological response to a perturbation of the limit cycle is probed. As an exemplar application, we employ ForCEPSS to prepare a CEP study according to the Virtual Arrhythmia Risk Prediction protocol used for investigating the arrhythmogenic risk of developing infarct-related ventricular tachycardia (VT) in ischemic cardiomyopathy patients. We demonstrate that ForCEPSS enables a fully automated execution of all stages of this complex protocol. Conclusion:ForCEPSS offers a novel comprehensive, standardized, and automated CEP simulation workflow. The high degree of automation accelerates the execution of CEP simulation studies, reduces errors, improves robustness, and makes CEP studies reproducible. Verification of simulation studies within the CEP modeling community is thus possible. As such, ForCEPSS makes an important contribution towards increasing transparency, standardization, and reproducibility of in silico CEP experiments.

Digital Twinning of Cardiac Electrophysiology Models From the Surface ECG: A Geodesic Backpropagation Approach.

The eikonal equation has become an indispensable tool for modeling cardiac electrical activation accurately and efficiently. In principle, by matching clinically recorded and eikonal-based electrocardiograms (ECGs), it is possible to build patient-specific models of cardiac electrophysiology in a purely non-invasive manner. Nonetheless, the fitting procedure remains a challenging task. The present study introduces a novel method, Geodesic-BP, to solve the inverse eikonal problem. Geodesic-BP is well-suited for GPU-accelerated machine learning frameworks, allowing us to optimize the parameters of the eikonal equation to reproduce a given ECG. We show that Geodesic-BP can reconstruct a simulated cardiac activation with high accuracy in a synthetic test case, even in the presence of modeling inaccuracies. Furthermore, we apply our algorithm to a publicly available dataset of a biventricular rabbit model, with promising results. Given the future shift towards personalized medicine, Geodesic-BP has the potential to help in future functionalizations of cardiac models meeting clinical time constraints while maintaining the physiological accuracy of state-of-the-art cardiac models.

Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.

The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.

Simultaneous data assimilation and cardiac electrophysiology model correction using differentiable physics and deep learning.

Modelling complex systems, like the human heart, has made great progress over the last decades. Patient-specific models, called 'digital twins', can aid in diagnosing arrhythmias and personalizing treatments. However, building highly accurate predictive heart models requires a delicate balance between mathematical complexity, parameterization from measurements and validation of predictions. Cardiac electrophysiology (EP) models range from complex biophysical models to simplified phenomenological models. Complex models are accurate but computationally intensive and challenging to parameterize, while simplified models are computationally efficient but less realistic. In this paper, we propose a hybrid approach by leveraging deep learning to complete a simplified cardiac model from data. Our novel framework has two components, decomposing the dynamics into a physics based and a data-driven term. This construction allows our framework to learn from data of different complexity, while simultaneously estimating model parameters. First, using in silico data, we demonstrate that this framework can reproduce the complex dynamics of cardiac transmembrane potential even in the presence of noise in the data. Second, using ex vivo optical data of action potentials (APs), we demonstrate that our framework can identify key physical parameters for anatomical zones with different electrical properties, as well as to reproduce the AP wave characteristics obtained from various pacing locations. Our physics-based data-driven approach may improve cardiac EP modelling by providing a robust biophysical tool for predictions.

Assessing drug safety by identifying the axis of arrhythmia in cardiomyocyte electrophysiology

Many classes of drugs can induce fatal cardiac arrhythmias by disrupting the electrophysiology of cardiomyocytes. Safety guidelines thus require all new drugs to be assessed for pro-arrhythmic risk prior to conducting human trials. The standard safety protocols primarily focus on drug blockade of the delayed-rectifier potassium current (IKr). Yet the risk is better assessed using four key ion currents (IKr, ICaL, INaL, IKs). We simulated 100,000 phenotypically diverse cardiomyocytes to identify the underlying relationship between the blockade of those currents and the emergence of ectopic beats in the action potential. We call that relationship the axis of arrhythmia. It serves as a yardstick for quantifying the arrhythmogenic risk of any drug from its profile of multi-channel block alone. We tested it on 109 drugs and found that it predicted the clinical risk labels with an accuracy of 88.1–90.8%. Pharmacologists can use our method to assess the safety of novel drugs without resorting to animal testing or unwieldy computer simulations.

ERG potassium channels and T-type calcium channels contribute to the pacemaker and atrioventricular conduction in zebrafish larvae.

Bradyarrhythmias result from inhibition of automaticity, prolonged repolarization, or slow conduction in the heart. The ERG channels mediate the repolarizing current IKr in the cardiac action potential, whereas T-type calcium channels (TTCC) are involved in the sinoatrial pacemaker and atrioventricular conduction in mammals. Zebrafish have become a valuable research model for human cardiac electrophysiology and disease. Here, we investigate the contribution of ERG channels and TTCCs to the pacemaker and atrioventricular conduction in zebrafish larvae and determine the mechanisms causing atrioventricular block. Zebrafish larvae expressing ratiometric fluorescent Ca2+ biosensors in the heart were used to measure Ca2+ levels and rhythm in beating hearts invivo, concurrently with contraction and hemodynamics. The atrioventricular delay (the time between the start of atrial and ventricular Ca2+ transients) was used to measure impulse conduction velocity and distinguished between slow conduction and prolonged refractoriness as the cause of the conduction block. ERG blockers caused bradycardia and atrioventricular block by prolonging the refractory period in the atrioventricular canal and in working ventricular myocytes. In contrast, inhibition of TTCCs caused bradycardia and second-degree block (Mobitz type I) by slowing atrioventricular conduction. TTCC block did not affect ventricular contractility, despite being highly expressed in cardiomyocytes. Concomitant measurement of Ca2+ levels and ventricular size showed mechano-mechanical coupling: increased preload resulted in a stronger heart contraction invivo. ERG channels and TTCCs influence the heart rate and atrioventricular conduction in zebrafish larvae. The zebrafish lines expressing Ca2+ biosensors in the heart allow us to investigate physiological feedback mechanisms and complex arrhythmias.

Boundary integral formulation of the cell-by-cell model of cardiac electrophysiology

We propose a boundary element method for the accurate solution of the cell-by-cell bidomain model of electrophysiology. The cell-by-cell model, also called Extracellular-Membrane-Intracellular (EMI) model, is a system of reaction–diffusion equations describing the evolution of the electric potential within each domain: intra- and extra-cellular space and the cellular membrane. The system is parabolic but degenerate because the time derivative is only in the membrane domain. In this work, we adopt a boundary-integral formulation for removing the degeneracy in the system and recast it to a parabolic equation on the membrane. The formulation is also numerically advantageous since the number of degrees of freedom is sensibly reduced compared to the original model. Specifically, we prove that the boundary-element discretization of the EMI model is equivalent to a system of ordinary differential equations, and we consider a time discretization based on the multirate explicit stabilized Runge–Kutta method. We numerically show that our scheme convergences exponentially in space for the single-cell case. We finally provide several numerical experiments of biological interest.

Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability.

Introduction: Predicting ventricular arrhythmia Torsade de Pointes (TdP) caused by drug-induced cardiotoxicity is essential in drug development. Several studies used single biomarkers such as qNet and Repolarization Abnormality (RA) in a single cardiac cell model to evaluate TdP risk. However, a single biomarker may not encompass the full range of factors contributing to TdP risk, leading to divergent TdP risk prediction outcomes, mainly when evaluated using unseen data. We addressed this issue by utilizing multi-in silico features from a population of human ventricular cell models that could capture a representation of the underlying mechanisms contributing to TdP risk to provide a more reliable assessment of drug-induced cardiotoxicity. Method: We generated a virtual population of human ventricular cell models using a modified O'Hara-Rudy model, allowing inter-individual variation. and Hill coefficients from 67 drugs were used as input to simulate drug effects on cardiac cells. Fourteen features (, , , , , , , , , , , , qNet, qInward) could be generated from the simulation and used as input to several machine learning models, including k-nearest neighbor (KNN), Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN). Optimization of the machine learning model was performed using a grid search to select the best parameter of the proposed model. We applied five-fold cross-validation while training the model with 42 drugs and evaluated the model's performance with test data from 25 drugs. Result: The proposed ANN model showed the highest performance in predicting the TdP risk of drugs by providing an accuracy of 0.923 (0.908-0.937), sensitivity of 0.926 (0.909-0.942), specificity of 0.921 (0.906-0.935), and AUC score of 0.964 (0.954-0.975). Discussion and conclusion: According to the performance results, combining the electrophysiological model including inter-individual variation and optimization of machine learning showed good generalization ability when evaluated using the unseen dataset and produced a reliable drug-induced TdP risk prediction system.

Fast and reliable reduced‐order models for cardiac electrophysiology

AbstractMathematical models of the human heart increasingly play a vital role in understanding the working mechanisms of the heart, both under healthy functioning and during disease. The ultimate aim is to aid medical practitioners diagnose and treat the many ailments affecting the heart. Towards this, modeling cardiac electrophysiology is crucial as the heart's electrical activity underlies the contraction mechanism and the resulting pumping action. Apart from modeling attempts, the pursuit of efficient, reliable, and fast solution algorithms has been of great importance in this context. The governing equations and the constitutive laws describing the electrical activity in the heart are coupled, nonlinear, and involve a fast moving wave front, which is generally solved by the finite element method. The numerical treatment of this complex system as part of a virtual heart model is challenging due to the necessity of fine spatial and temporal resolution of the domain. Therefore, efficient surrogate models are needed to predict the electrical activity in the heart under varying parameters and inputs much faster than the finely resolved models. In this work, we develop an adaptive, projection‐based reduced‐order surrogate model for cardiac electrophysiology. We introduce an a posteriori error estimator that can accurately and efficiently quantify the accuracy of the surrogate model. Using the error estimator, we systematically update our surrogate model through a greedy search of the parameter space. Furthermore, using the error estimator, the parameter search space is dynamically updated such that the most relevant samples get chosen at every iteration. The proposed adaptive surrogate model is tested on three benchmark models to illustrate its efficiency, accuracy, and ability of generalization.

MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs from electrophysiological simulations

Mechanistic cardiac electrophysiology models allow for personalized simulations of the electrical activity in the heart and the ensuing electrocardiogram (ECG) on the body surface. As such, synthetic signals possess known ground truth labels of the underlying disease and can be employed for validation of machine learning ECG analysis tools in addition to clinical signals. Recently, synthetic ECGs were used to enrich sparse clinical data or even replace them completely during training leading to improved performance on real-world clinical test data. We thus generated a novel synthetic database comprising a total of 16,900 12 lead ECGs based on electrophysiological simulations equally distributed into healthy control and 7 pathology classes. The pathological case of myocardial infraction had 6 sub-classes. A comparison of extracted features between the virtual cohort and a publicly available clinical ECG database demonstrated that the synthetic signals represent clinical ECGs for healthy and pathological subpopulations with high fidelity. The ECG database is split into training, validation, and test folds for development and objective assessment of novel machine learning algorithms.

Model-driven optimal experimental design for calibrating cardiac electrophysiology models

Background and Objective: Models of the cardiomyocyte action potential have contributed immensely to the understanding of heart function, pathophysiology, and the origin of heart rhythm disturbances. However, action potential models are highly nonlinear, making them difficult to parameterise and limiting to describing ‘average cell’ dynamics, when cell-specific models would be ideal to uncover inter-cell variability but are too experimentally challenging to be achieved. Here, we focus on automatically designing experimental protocols that allow us to better identify cell-specific maximum conductance values for each major current type.Methods and Results: We developed an approach that applies optimal experimental designs to patch-clamp experiments, including both voltage-clamp and current-clamp experiments. We assessed the models calibrated to these new optimal designs by comparing them to the models calibrated to some of the commonly used designs in the literature. We showed that optimal designs are not only overall shorter in duration but also able to perform better than many of the existing experiment designs in terms of identifying model parameters and hence model predictive power.Conclusions: For cardiac cellular electrophysiology, this approach will allow researchers to define their hypothesis of the dynamics of the system and automatically design experimental protocols that will result in theoretically optimal designs.

Assessment of Risk for Ventricular Tachycardia based on Extensive Electrophysiology Simulations.

Patients that have suffered a myocardial infarction are at high risk of developing ventricular tachycardia. Patient stratification is often determined by characterization of the underlying myocardial substrate by cardiac imaging methods. In this study, we show that computer modeling of cardiac electrophysiology based on personalized fast 3D simulations can help to assess patient risk to arrhythmia. We perform a large simulation study on 21 patient digital twins and reproduce successfully the clinical outcomes. In addition, we provide the sites which are prone to sustain ventricular tachycardias, i.e, onset sites around the scar region, and validate if they colocalize with exit sites from slow conduction channels.Clinical relevance- Fast electrophysiological simulations can provide advanced patient stratification indices and predict arrhythmic susceptibility to suffer from ventricular tachycardia in patients that have suffered a myocardial infarction.

Stability of Front Solutions of the Bidomain Allen–Cahn Equation on an Infinite Strip

The bidomain model is the standard model for cardiac electrophysiology. In this paper, we study the bidomain Allen–Cahn equation, in which the Laplacian of the classical Allen–Cahn equation is replaced by the bidomain operator, a Fourier multiplier operator whose symbol is given by a homogeneous rational function of degree two. The bidomain Allen–Cahn equation supports planar front solutions much like the classical case. In contrast to the classical case, however, these fronts are not necessarily stable due to a lack of maximum principle; they can indeed become unstable depending on the parameters of the system. In this paper, we prove nonlinear stability and instability results for bidomain Allen–Cahn fronts on an infinite two-dimensional strip. We show that previously established spectral stability/instability results in imply stability/instability in the space of bounded uniformly continuous functions by establishing suitable decay estimates of the resolvent kernel of the linearized operator.

PO-01-212 A NOVEL DEEP LEARNING MODEL FOR PATIENT-SPECIFIC COMPUTATIONAL MODELING OF CARDIAC ELECTROPHYSIOLOGY

Patient-specific computational modeling of cardiac electrophysiology (EP) has been shown to have a potential utility in a wide range of arrhythmia prognostics and treatment applications. Yet, it requires extensive computational resources, posing a critical challenge for expanding its application in the clinic. Hence, there is a pressing need to develop accurate and trustable personalized EP heart models with affordable computational costs. We develop a novel approach to modeling patient-specific cardiac electrophysiology, which utilizes, at a much-reduced cost, deep learning in lieu of solving the equations for electrical wave propagation. The new approach is developed based on an operator-learning neural network (DeepONet) to predict the propagation of electric signals in different patient-specific heart chambers (Fig. a). First, we parametrize the geometry of atria and ventricles from a cohort of patients (300+) with principal component analysis (PCA), which implicitly encodes geometric information such as wall thickness, fiber orientation, or fibrosis of each heart (Fig. b). Next, we perform pacing using the computational approach on each geometry and collect the simulated data as the training dataset of the deep-learning model. Then, we train our model to predict electric signal propagation given the encoded geometric information with the pacing location. After training, the accuracy of the deep learning approach is demonstrated by comparing its predictions with simulation results on unseen geometries: the averaged maximum relative difference is lower than 10%, and the mean relative error is lower than 5%. Regarding time cost, our model makes predictions within a fraction of a second on a personal laptop, whereas a standard computational model takes up to 12+ hours on clusters with multiple CPUs (Fig. c). We propose a novel deep-learning approach for modeling patient-specific cardiac electrophysiology with high accuracy and reduced computational cost. This development paves the way for the utilization of personalized cardiac EP modeling in clinical applications, as part of the clinical workflow, in ablation target predictions, and in arrhythmia morphology assessment.

Efficient and certified solution of parametrized one-way coupled problems through DEIM-based data projection across non-conforming interfaces

One of the major challenges of coupled problems is to manage nonconforming meshes at the interface between two models and/or domains, due to different numerical schemes or domain discretizations employed. Moreover, very often complex submodels depend on (e.g., physical or geometrical) parameters, thus making the repeated solutions of the coupled problem through high-fidelity, full-order models extremely expensive, if not unaffordable. In this paper, we propose a reduced order modeling (ROM) strategy to tackle parametrized one-way coupled problems made by a first, master model and a second, slave model; this latter depends on the former through Dirichlet interface conditions. We combine a reduced basis method, applied to each subproblem, with the discrete empirical interpolation method to efficiently interpolate or project Dirichlet data across either conforming or non-conforming meshes at the domains interface, building a low-dimensional representation of the overall coupled problem. The proposed technique is numerically verified by considering a series of test cases involving both steady and unsteady problems, after deriving a posteriori error estimates on the solution of the coupled problem in both cases. This work arises from the need to solve staggered cardiac electrophysiological models and represents the first step towards the setting of ROM techniques for the more general two-way Dirichlet-Neumann coupled problems solved with domain decomposition sub-structuring methods, when interface non-conformity is involved.

A matrix–free high–order solver for the numerical solution of cardiac electrophysiology

We propose a matrix–free solver for the numerical solution of the cardiac electrophysiology model consisting of the monodomain nonlinear reaction–diffusion equation coupled with a system of ordinary differential equations for the ionic species. Our numerical approximation is based on the high–order Spectral Element Method (SEM) to achieve accurate numerical discretization while employing a much smaller number of Degrees of Freedom than first–order Finite Elements. We combine vectorization with sum–factorization, thus allowing for a very efficient use of high–order polynomials in a high performance computing framework. We validate the effectiveness of our matrix–free solver in a variety of applications and perform different electrophysiological simulations ranging from a simple slab of cardiac tissue to a realistic four–chamber heart geometry. We compare SEM to SEM with Numerical Integration (SEM–NI), showing that they provide comparable results in terms of accuracy and efficiency. In both cases, increasing the local polynomial degree p leads to better numerical results and smaller computational times than reducing the mesh size h. We also implement a matrix–free Geometric Multigrid preconditioner that results in a comparable number of linear solver iterations with respect to a state–of–the–art matrix–based Algebraic Multigrid preconditioner. As a matter of fact, the matrix–free solver proposed here yields up to 45× speed–up with respect to a conventional matrix–based solver.

Molecular simulations of state-specific drug interactions with multiple cardiac ion channels to reveal mechanisms of arrhythmogenesis.

Unintended drug block of cardiac ion channels remains a major problem in drug development. The voltage-gated potassium channel KV11.1 also known as hERG is a major drug anti-target binding a diverse set of small molecule drugs that potently reduce the critical repolarizing current IKr. Many drugs that bind the hERG channel promote deadly arrythmias while some hERG blockers present significantly lower proarrhythmic risk. Two hypotheses were proposed to elucidate this discrepancy: (1) preferential drug binding to the inactivated state of the hERG channel confers greater proarrhythmic risk and (2) simultaneous drug binding to other cardiac ion channels can ameliorate the risk associated with hERG channel block. Here, we present a state-specific molecular modeling assessment of drug binding to different conformations of the hERG and voltage-gated sodium NaV1.5 and calcium CaV1.2 channels. We have developed structural models of these cardiac ion channels in open and inactivated conformations and performed all-atom molecular dynamics (MD) simulations to validate structural stabilities and assess ion conduction. Ligand docking was then performed using Site Identification by Ligand Competitive Saturation (SILCS), a pre-compute ensemble molecular docking technique. SILCS allows us to perform a high-throughput assessment of ligand binding affinities using molecular fragment energy maps derived from MD simulations of state-specific ion channel models. Bayesian machine learning was used to provide improved correlation of SILCS-computed affinities with experimental data. Using SILCS multi-ligand docking we also estimated interactions of drugs with sex hormones in the hERG channel pore to assess a potential molecular mechanism for an increased proarrhythmia risk in females. We aim to use SILCS computed state-specific drug affinity data to inform multi-scale functional kinetic models of cardiac electrophysiology to estimate emergent drug effects on the cardiac action potential and heart rhythm.

Tipping the scales of understanding: An engineering approach to design and implement whole-body cardiac electrophysiology experimental models.

The study of cardiac electrophysiology is built on experimental models that span all scales, from ion channels to whole-body preparations. Novel discoveries made at each scale have contributed to our fundamental understanding of human cardiac electrophysiology, which informs clinicians as they detect, diagnose, and treat complex cardiac pathologies. This expert review describes an engineering approach to developing experimental models that is applicable across scales. The review also outlines how we applied the approach to create a set of multiscale whole-body experimental models of cardiac electrophysiology, models that are driving new insights into the response of the myocardium to acute ischemia. Specifically, we propose that researchers must address three critical requirements to develop an effective experimental model: 1) how the experimental model replicates and maintains human physiological conditions, 2) how the interventions possible with the experimental model capture human pathophysiology, and 3) what signals need to be measured, at which levels of resolution and fidelity, and what are the resulting requirements of the measurement system and the access to the organs of interest. We will discuss these requirements in the context of two examples of whole-body experimental models, a closed chest in situ model of cardiac ischemia and an isolated-heart, torso-tank preparation, both of which we have developed over decades and used to gather valuable insights from hundreds of experiments.