Hierarchical reconstructions of cardiac tissue

Poole, M.J.; Holden, Arun V.; Tucker, John V.

doi:10.1016/s0960-0779(01)00169-2

Cited by 14 publications

(7 citation statements)

References 27 publications

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“…This approach parallels recent works by Poole et al [19] which presented preliminary results of a multi-formalism approach on 1D segments of cardiac tissue, rather than an exhaustive use of supercalculation. They expanded the general theory of synchronous concurent algorithms (SCA) which consists in unifying the different types of models on a global clock measuring discrete time.…”

Section: Multi-formalism Approachsupporting

confidence: 59%

“…One great advantage of this method is the possibility of simulating physiopathological states even in a hybrid approach. Compared to other projects in the same field [19], which use Aliev-Panfilov model (a morphological description of the action potential), the proposed method lets us integrate physiologically detailed models, such as the BR model, while still minimizing the global computing expenses.…”

Section: Discussionmentioning

confidence: 99%

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Multi-formalism Modelling of Cardiac Tissue

Defontaine

Hernández

Carrault

2005

Functional Imaging and Modeling of the Heart

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Abstract. Many models of the cardiovascular system (e.g. cardiac electrical activity, autonomous nervous system, . . . ) have been proposed for the last decades. Research is now focusing on the integration of these different models, in order to study more complicated physiopathological states in clinical applications context. To get round the practical limitations of existing models, multi-formalism modelling appears as a way to ease the integration of these different models together. This paper presents an original methodology allowing to combine different types of description formalisms. This method has been applied to define a multi-formalism model of cardiac action potential propagation on a 2D grid of endocardial cells, combining cellular automata and a set of cells defined by the Beeler-Reuter model. Results, obtained under physiologic and ischemic conditions, highlight the improvements in term of computing compared with mono-formalism systems, while keeping the necessary explanatory strength for a practical clinical use.

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Section: Multi-formalism Approachsupporting

confidence: 59%

Section: Discussionmentioning

confidence: 99%

Multi-formalism Modelling of Cardiac Tissue

Defontaine

Hernández

Carrault

2005

Functional Imaging and Modeling of the Heart

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“…Even so, a pair of equations from Fitzhugh [4] and Nagumo [5] can represent the propagation of excitation through the myocardium very well, a huge reduction in computation time. Poole et al [6] looked upon such reductionist approaches in a broader way, comparing the ordinary and partial differential equation models with cellular automata models.…”

Section: Multiscale Modeling Of Cardiac Performance: Modeling Celmentioning

confidence: 99%

Strategies and Tactics in Multiscale Modeling of Cell-to-Organ Systems

2006

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Modeling is essential to integrating knowledge of human physiology. Comprehensive self-consistent descriptions expressed in quantitative mathematical form define working hypotheses in testable and reproducible form, and though such models are always "wrong" in the sense of being incomplete or partly incorrect, they provide a means of understanding a system and improving that understanding. Physiological systems, and models of them, encompass different levels of complexity. The lowest levels concern gene signaling and the regulation of transcription and translation, then biophysical and biochemical events at the protein level, and extend through the levels of cells, tissues and organs all the way to descriptions of integrated systems behavior. The highest levels of organization represent the dynamically varying interactions of billions of cells. Models of such systems are necessarily simplified to minimize computation and to emphasize the key factors defining system behavior; different model forms are thus often used to represent a system in different ways. Each simplification of lower level complicated function reduces the range of accurate operability at the higher level model, reducing robustness, the ability to respond correctly to dynamic changes in conditions. When conditions change so that the complexity reduction has resulted in the solution departing from the range of validity, detecting the deviation is critical, and requires special methods to enforce adapting the model formulation to alternative reduced-form modules or decomposing the reduced-form aggregates to the more detailed lower level modules to maintain appropriate behavior. The processes of error recognition, and of mapping between different levels of model complexity and shifting the levels of complexity of models in response to changing conditions, are essential for adaptive modeling and computer simulation of large-scale systems in reasonable time.

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“…It should be emphasized that no one model would encompass the 10 9 dynamic range of spatial scales (from the 1-nm pore size of an ion channel to the 1-m scale of the human body) or 10 15 dynamic range of temporal scales (from the 1 s typical of Brownian motion to the 70 years or 10 9 s typical of a human lifetime). Rather, it requires a hierarchy of models, such that the parameters of one model in the hierarchy can be understood in terms of the physics or chemistry of the model appropriate to the spatial or temporal scale at the level below [26]. This hierarchy of models must range from gene networks, signal transduction pathways and stochastic models of single channels at the fine scale, up to systems of ordinary differential equations, representing cell level function, and partial differential equations, representing the continuum properties of tissues and organs, at the coarse scale.…”

Section: Spatial and Temporal Scalesmentioning

confidence: 99%

The IUPS human physiome project

Hunter

Robbins

Noble

2002

Pfl�gers Archiv European Journal of Physiology

170

100

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The Physiome Project of the International Union of Physiological Sciences (IUPS) is attempting to provide a comprehensive framework for modelling the human body using computational methods which can incorporate the biochemistry, biophysics and anatomy of cells, tissues and organs. A major goal of the project is to use computational modelling to analyse integrative biological function in terms of underlying structure and molecular mechanisms. To support that goal the project is establishing web-accessible physiological databases dealing with model-related data, including bibliographic information, at the cell, tissue, organ and organ system levels. Here we discuss the background and goals of the project, the problems of modelling across multiple spatial and temporal scales, and the development of model ontologies and markup languages at all levels of biological function.

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Hierarchical reconstructions of cardiac tissue

Cited by 14 publications

References 27 publications

Multi-formalism Modelling of Cardiac Tissue

Multi-formalism Modelling of Cardiac Tissue

Strategies and Tactics in Multiscale Modeling of Cell-to-Organ Systems

The IUPS human physiome project

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