A long-sought, and thus far elusive, goal has been to develop drugs to manage diseases of excitability. One such disease that affects millions each year is cardiac arrhythmia, which occurs when electrical impulses in the heart become disordered, sometimes causing sudden death. Pharmacological management of cardiac arrhythmia has failed because it is not possible to predict how drugs that target cardiac ion channels, and have intrinsically complex dynamic interactions with ion channels, will alter the emergent electrical behavior generated in the heart. Here, we applied a computational model, which was informed and validated by experimental data, that defined key measurable parameters necessary to simulate the interaction kinetics of the anti-arrhythmic drugs flecainide and lidocaine with cardiac sodium channels. We then used the model to predict the effects of these drugs on normal human ventricular cellular and tissue electrical activity in the setting of a common arrhythmia trigger, spontaneous ventricular ectopy. The model forecasts the clinically relevant concentrations at which flecainide and lidocaine exacerbate, rather than ameliorate, arrhythmia. Experiments in rabbit hearts and simulations in human ventricles based on magnetic resonance images validated the model predictions. This computational framework initiates the first steps toward development of a virtual drug-screening system that models drug-channel interactions and predicts the effects of drugs on emergent electrical activity in the heart.
The traditional cardiac model-building paradigm involves constructing a composite model using data collected from many cells. Equations are derived for each relevant cellular component (e.g., ion channel, exchanger) independently. After the equations for all components are combined to form the composite model, a subset of parameters is tuned, often arbitrarily and by hand, until the model output matches a target objective, such as an action potential. Unfortunately, such models often fail to accurately simulate behavior that is dynamically dissimilar (e.g., arrhythmia) to the simple target objective to which the model was fit. In this study, we develop a new approach in which data are collected via a series of complex electrophysiology protocols from single cardiac myocytes and then used to tune model parameters via a parallel fitting method known as a genetic algorithm (GA). The dynamical complexity of the electrophysiological data, which can only be fit by an automated method such as a GA, leads to more accurately parameterized models that can simulate rich cardiac dynamics. The feasibility of the method is first validated computationally, after which it is used to develop models of isolated guinea pig ventricular myocytes that simulate the electrophysiological dynamics significantly better than does a standard guinea pig model. In addition to improving model fidelity generally, this approach can be used to generate a cell-specific model. By so doing, the approach may be useful in applications ranging from studying the implications of cell-to-cell variability to the prediction of intersubject differences in response to pharmacological treatment.
A dynamic control technique was used to suppress a cardiac arrhythmia called an alternans rhythm in a piece of dissected rabbit heart. Our control algorithm adapted to drifting system parameters, making it well suited for the control of physiological rhythms. Control of cardiac alternans rhythms may have important clinical implications since they often precede serious cardiac arrhythmias and are a harbinger of sudden cardiac death.[S0031-9007 (97)03337-1] PACS numbers: 87.22. -q, 05.45. + b, 07.05.Dz, 87.10.+e Control techniques from the field of nonlinear dynamics [1] have been used to control both chaotic [2] and nonchaotic [3] dynamical systems. Since these control methods do not require knowledge of the system's governing equations, they are particularly applicable in biology where detailed mathematical models are usually unavailable. Control of biological dynamics is important for medical science since abnormal physiological rhythms can be life threatening [4]. Attempts have already been made to control both experimental [5] and model [6,7] biological systems. However, none of these studies used control algorithms which adapted to evolving system parameters. Since physiological environments typically drift over time, practical biological control schemes must adapt to these changes. Here, we utilize an algorithm which controls an evolving cardiac arrhythmia called an alternans rhythm in the rabbit heart.Cardiac alternans rhythms are characterized by an alternation of the timing or morphology of the heart's electrical activity from one beat to the next. While the clinical importance of cardiac alternans has only recently been recognized [8], their discovery dates back to the earliest recordings of cardiac electrical signals [9]. We generated cardiac alternans by electrically stimulating a piece of dissected rabbit heart [10]. Each stimulus delivered to the upper atrium caused a wave of electrical activity to propagate through the atrium, the atrioventricular (AV) node and out the His bundle which is the output of the AV node [ Fig. 1(a)]. We measured the electrical activity near an atrial input of the AV node and at the His bundle output [ Fig. 1(b)]. X was the time for the impulse to pass through the AV node. The output impulse was reinjected into the atrium after a time delay l. When l was made sufficiently small, the conduction time through the AV node began to alternate [11] [ Fig. 1(b)].The dynamics of AV nodal conduction can be characterized by a one-dimensional mapwhere X n is the AV nodal conduction time following the nth atrial stimulus, l is the time delay from His bundle activation to the next atrial stimulus, and f is a nonlinear, decreasing function of both arguments which relates the successive conduction times [11]. The map is represented as a graph in Fig. 2(a). This map determines the sequence of AV nodal conduction times, X 1 , X 2 , X 3 , . . . , X n given some initial conduction time, X 0 , for fixed l. The intersection of the curve with the line of identity (X n11 X n ) defines the period-...
Across individuals within a population, several levels of variability are observed, from the differential expression of ion channels at the molecular level, to the various action potential morphologies observed at the cellular level, to divergent responses to drugs at the organismal level. However, the limited ability of experiments to probe complex interactions between components has hitherto hindered our understanding of the factors that cause a range of behaviours within a population. Variability is a challenging issue that is encountered in all physiological disciplines, but recent work suggests that novel methods for analysing mathematical models can assist in illuminating its causes. In this review, we discuss mathematical modelling studies in cardiac electrophysiology and neuroscience that have enhanced our understanding of variability in a number of key areas. Specifically, we discuss parameter sensitivity analysis techniques that may be applied to generate quantitative predictions based on considering behaviours within a population of models, thereby providing novel insight into variability. Our discussion focuses on four issues that have benefited from the utilization of these methods: (1) the comparison of different electrophysiological models of cardiac myocytes, (2) the determination of the individual contributions of different molecular changes in complex disease phenotypes, (3) the identification of the factors responsible for the variable response to drugs, and (4) the constraining of free parameters in electrophysiological models of heart cells. Together, the studies that we discuss suggest that rigorous analyses of mathematical models can generate quantitative predictions regarding how molecular-level variations contribute to functional differences between experimental samples. These strategies may be applicable not just in cardiac electrophysiology, but in a wide range of disciplines. University. During her Doctorate, she built mathematical models to understand the effects of changes in cardiac ion channel expression on clinically relevant and measurable properties of the heart. Currently, she is a Fellow at the Collège des Ingénieurs in Paris, where she is pursuing a specialized MBA for engineers and scientists while working for a leading French company. David Christini (middle) received a BS degree in electrical engineering from the Pennsylvania State University and MS and PhD degrees in biomedical engineering from Boston University. He is a Professor in the Departments of Medicine and Physiology and Biophysics, Weill Cornell Medical College, New York. He uses computational and experimental methods to study cellular-to organ-level cardiac electrophysiological dynamics, with an emphasis on understanding the mechanisms underlying arrhythmia initiation and in developing new arrhythmia therapies. Eric Sobie (right) is an Associate Professor in the Department of Pharmacology and Systems Therapeutics at Mount Sinai School of Medicine in New York City. He holds a BSE degree from Duke Unive...
We describe a system for real-time control of biological and other experiments. This device, based around the Real-Time Linux operating system, was tested specifically in the context of dynamic clamping, a demanding real-time task in which a computational system mimics the effects of nonlinear membrane conductances in living cells. The system is fast enough to represent dozens of nonlinear conductances in real time at clock rates well above 10 kHz. Conductances can be represented in deterministic form, or more accurately as discrete collections of stochastically gating ion channels. Tests were performed using a variety of complex models of nonlinear membrane mechanisms in excitable cells, including simulations of spatially extended excitable structures, and multiple interacting cells. Only in extreme cases does the computational load interfere with high-speed "hard" real-time processing (i.e., real-time processing that never falters). Freely available on the worldwide web, this experimental control system combines good performance. immense flexibility, low cost, and reasonable ease of use. It is easily adapted to any task involving real-time control, and excels in particular for applications requiring complex control algorithms that must operate at speeds over 1 kHz.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.