Electrophysiological Modeling of Fibroblasts and their Interaction with Myocytes

Sachse, Frank B.; Moreno, Alonso P.; Abildskov, J.A.

doi:10.1007/s10439-007-9405-8

Cited by 94 publications

(127 citation statements)

References 44 publications

(57 reference statements)

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“…According to the experimental reports, C f ranges from 6.3 to 75 pF (Vasquez et al, 2004;MacCannell et al, 2007). The G gap between the myocytes ranges from 262 to 937 nS for end-to-end coupling (Yao et al, 2003), and the G gap between a myocyte and a fibroblast or between two fibroblasts (G gap_j ) ranges from 0.3 to 8 nS in cultured cells (Rook et al, 1992), whereas 0-100 nS were used in other modeling studies (MacCannell et al, 2007;Jacquemet and Henriquez, 2008;Sachse et al, 2008). In this study, G gap and C f were set to 500 nS and 25 pF, respectively.…”

Section: Tissue Modelingmentioning

confidence: 99%

Fibroblast proliferation alters cardiac excitation conduction and contraction: a computational study

Zhan

Xia

Shou

et al. 2014

J. Zhejiang Univ. Sci. B

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Abstract:In this study, the effects of cardiac fibroblast proliferation on cardiac electric excitation conduction and mechanical contraction were investigated using a proposed integrated myocardial-fibroblastic electromechanical model. At the cellular level, models of the human ventricular myocyte and fibroblast were modified to incorporate a model of cardiac mechanical contraction and cooperativity mechanisms. Cellular electromechanical coupling was realized with a calcium buffer. At the tissue level, electrical excitation conduction was coupled to an elastic mechanics model in which the finite difference method (FDM) was used to solve electrical excitation equations, and the finite element method (FEM) was used to solve mechanics equations. The electromechanical properties of the proposed integrated model were investigated in one or two dimensions under normal and ischemic pathological conditions. Fibroblast proliferation slowed wave propagation, induced a conduction block, decreased strains in the fibroblast proliferous tissue, and increased dispersions in depolarization, repolarization, and action potential duration (APD). It also distorted the wave-front, leading to the initiation and maintenance of re-entry, and resulted in a sustained contraction in the proliferous areas. This study demonstrated the important role that fibroblast proliferation plays in modulating cardiac electromechanical behaviour and which should be considered in planning future heart-modeling studies.

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Section: Tissue Modelingmentioning

confidence: 99%

Fibroblast proliferation alters cardiac excitation conduction and contraction: a computational study

Zhan

Xia

Shou

et al. 2014

J. Zhejiang Univ. Sci. B

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“…Cellular models ( [15]) have been developed and by combining these cellular models with the structural models, the electrical activities of the whole heart are studied, especially the mechanism of different arrhythmia ( [17], [4], [13]). Intrinsic heart rate variability has been modeled to synthesize optimal control of pacemaker pacing.…”

Section: Related Workmentioning

confidence: 99%

Automated closed-loop model checking of implantable pacemakers using abstraction trees

et al. 2017

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Autonomous medical devices such as implantable cardiac pacemakers are capable of diagnosing the patient condition and delivering therapy without human intervention. Their ability to autonomously affect the physiological state of the patient makes them safety-critical. Sufficient evidence for the safety and efficacy of the device software, which makes these autonomous decisions, should be provided before these devices can be released on the market. Formal methods like model checking can provide safety evidence that the devices can safely operate under a large variety of physiological conditions. The challenge is to develop physiological models that are general enough to cover the large variability of human physiology, and also expressive enough to provide physiological contexts to counter-examples returned by the model checker. In this paper, the authors develop a set of physiological abstraction rules that introduce physiological constraints to heart models. By applying these abstraction rules to a initial set of heart models, an abstraction tree is created. The root model covers all possible inputs to a pacemaker and derived models cover inputs from different heart conditions. If a counter-example is returned by the model checker, the abstraction tree is traversed so that the most concrete counter-example(s) with physiological contexts can be returned to the domain experts for validity check. The abstraction tree framework replaces the manual abstraction and refinement framework, which reduced the amount of domain knowledge required to perform closed-loop model checking. It encourages the use of model checking during the development of autonomous medical devices, and identifies safety risks earlier in the design process. ABSTRACTAutonomous medical devices such as implantable cardiac pacemakers are capable of diagnosing the patient condition and delivering therapy without human intervention. Their ability to autonomously affect the physiological state of the patient makes them safety-critical. Sufficient evidence for the safety and efficacy of the device software, which makes these autonomous decisions, should be provided before these devices can be released on the market. Formal methods like model checking can provide safety evidence that the devices can safely operate under a large variety of physiological conditions. The challenge is to develop physiological models that are general enough to cover the large variability of human physiology, and also expressive enough to provide physiological contexts to counter-examples returned by the model checker. In this paper, the authors develop a set of physiological abstraction rules that introduce physiological constraints to heart models. By applying these abstraction rules to a initial set of heart models, an abstraction tree is created. The root model covers all possible inputs to a pacemaker and derived models cover inputs from different heart conditions. If a counter-example is returned by the model checker, the abstraction tree is traversed so that the ...

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“…This is the most widely used model. Another contemporary model developed by Sachse et al [92] included a Markovian description of the outward potassium current. This study examined the impact of fibroblasts on conduction in one-dimensional strand of myocytes, obtaining a reduced conduction and upstroke velocity.…”

Section: Consequences Of Fibrosis Derived From Computational Studiesmentioning

confidence: 99%

Intermittent Failure Dynamics Characterization

et al. 2012

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Heart failure constitutes a major public health problem worldwide. Affected patients experience a number of changes in the electrical function of the heart that predispose to potentially lethal cardiac arrhythmias. Due to the multitude of electrophysiological changes that may occur during heart failure, the scientific literature is complex and sometimes ambiguous, perhaps because these findings are highly dependent on the etiology, the stage of heart failure, and the experimental model used to study these changes. Nevertheless, a number of common features of failing hearts have been documented. Prolongation of the action potential (AP) involving ion channel remodeling and alterations in calcium handling have been established as the hallmark characteristics of myocytes isolated from failing hearts. Intercellular uncoupling and fibrosis are identified as major arrhythmogenic factors. Multi-scale computational simulations are a powerful tool that complements experimental and clinical research. The development of biophysically detailed computer models of single myocytes and cardiac tissues has contributed greatly to our understanding of processes underlying excitation and repolarization in the heart. The electrical, structural, and metabolic remodeling that arises in cardiac tissues during heart failure has been addressed from different computational perspectives to further understand the arrhythmogenic substrate. This review summarizes the contributions from computational modeling and simulation to predict the underlying mechanisms of heart failure phenotypes and their implications for arrhythmogenesis, ranging from the cellular level to whole-heart simulations. The main aspects of heart failure are presented in several related sections. An overview of the main electrophysiological and structural changes that have been observed experimentally in failing hearts is followed by the description and discussion of the simulation work in this field at the cellular level, and then in 2D and 3D cardiac structures. The implications for arrhythmogenesis in heart failure are also discussed including therapeutic measures, such as drug effects and cardiac resynchronization therapy. Finally, the future challenges in heart failure modeling and simulation will be discussed.

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Electrophysiological Modeling of Fibroblasts and their Interaction with Myocytes

Cited by 94 publications

References 44 publications

Fibroblast proliferation alters cardiac excitation conduction and contraction: a computational study

Fibroblast proliferation alters cardiac excitation conduction and contraction: a computational study

Automated closed-loop model checking of implantable pacemakers using abstraction trees

Intermittent Failure Dynamics Characterization

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