Electrophysiological Modeling of Cardiac Ventricular Function: From Cell to Organ

Winslow, Raimond L.; Scollan, D. F.; Holmes, Allen A.; Yung, Christina K.; Zhang, J.; Jafri, M. Saleet

doi:10.1146/annurev.bioeng.2.1.119

Cited by 110 publications

(66 citation statements)

References 112 publications

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“…Examples using Markov models of cardiac sodium channel have been studied by Rudy and colleagues [50]. In addition, the cellular model can also reach up to a whole organ model including both the electrical and mechanical behaviors of heart [51,52]. We predict that incorporation of molecular/genetic models, cellular models and structural/functional models will be one of the most exciting prospects of computational biology in the coming years.…”

Section: Developing a Multi-scale Mathematical Model Of LV Remodelingmentioning

confidence: 98%

Multi-Scale Modeling and Analysis of Left Ventricular Remodeling Post Myocardial Infarction: Integration of Experimental and Computational Approaches

Jin¹,

Lindsey²

2010

Application of Machine Learning

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Progressive remodeling of the left ventricle (LV) following myocardial infarction (MI) involves spatiotemporal interactions among multiple cell types and the extracellular matrix environment. Despite the extensive experimental studies designed to elucidate the regulatory mechanisms, there is a growing recognition that the complexity of LV remodeling precludes the efficient identification of early diagnostic indicators after myocardial infarction. Currently, systemic approaches are needed to reduce this complexity. Previous studies in other systems demonstrate that establishing a multi-scale analytical model of LV remodeling response to MI will likely help in the development of prognostic therapies. In this review, we discuss the current approaches used for mathematical modeling of the LV, advantages and disadvantages of the approaches, and methods used to validate these models.

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Section: Developing a Multi-scale Mathematical Model Of LV Remodelingmentioning

confidence: 98%

Multi-Scale Modeling and Analysis of Left Ventricular Remodeling Post Myocardial Infarction: Integration of Experimental and Computational Approaches

Jin¹,

Lindsey²

2010

Application of Machine Learning

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“…These cells models have been integrated into large-scale, biophysically and anatomically detailed models of electrical conduction in the cardiac ventricles which have been used to investigate the molecular basis of life-threatening arrhythmias. 41,46 Such model development is not limited to the heart. As an additional example, anatomically-based models and fluid dynamics simulation of airflow in the lungs and bronchial tree are under development and approaches to physiologically-based modeling of respiratory function have been proposed to investigate transport phenomena and particle distribution within the airways.…”

Section: Integrative Systems Physiologymentioning

confidence: 99%

Bioengineering and Systems Biology

Ideker¹,

Winslow

Lauffenburger

2006

Ann Biomed Eng

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A field known as Systems Biology is emerging, from roots in the molecular biology and genomic biology revolutions-the succession of which has led biomedical scientists to recognize that living systems can be studied not only in terms of their mechanistic, molecular-level components but also in terms of many of them simultaneously. This prospect of understanding how biological entities function through the framework of integrated operation of component parts holds extraordinary promise for medical applications, as well for broader societal applications such as the environment, agriculture, materials/manufacturing, and national defense.Some definitions of Systems Biology are available. Ideker et al. 22 suggest the following: "Systems Biology does not investigate individual genes or proteins one at a time, as has been the highly successful mode of biology for the past 30 years. Rather, it investigates the behavior and relationships of all the elements in a particular biological system while it is functioning." A description by Kitano 27 is that "To understand biology at the system level, we must examine the structure and dynamics of cellular and organismal function, rather than the characteristics of isolated parts of a cell or organism." The National Institute of General Medical Sciences at NIH 1 provides a slightly different perspective: "Systems Biology seeks to predict the quantitative behavior of an in vivo biological process under realistic perturbation, where the quantitative treatment derives its power from explicit inclusion of the process components, their interactions, and realistic values for their concentrations, locations, and local states. Address correspondence to Douglas A. Lauffenburger, Biological Engineering Division, Massachusetts Institute of Technology ment can be undertaken in a high-throughput, multivariate manner using various kinds of array technologies. Because this multivariate data then is relatively recalcitrant to hypothesis generation by means of unaided human intuition, computational algorithms for Mining the data to generate hypotheses concerning the potential interpretation of these data sets is necessary. In order to consequently develop new predictions for experimental test (or design), computational Modeling is required for similar reason: unaided human intuition likely cannot produce effective predictions concerning complex, interconnected, nonlinear molecular systems. Finally, in order to test those model predictions or create a new technology or product, molecular-level Manipulation is needed, employing genetic, biochemical, or materials interventions. Thus, Systems Biology involves a multivariate approach comprising topological and dynamical properties and aimed ultimately at quantitative prediction, for basic scientific understanding or technological design. It must be noted that the complexity of living systems does not reside solely in the number of components and interactions treated, nor in their associated structural and physicochemical properties, but also in the hi...

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“…In cellular biology, decades of genetic, biochemical, and biophysical studies have generated a large amount of experimental data that can be used to deduce reaction mechanisms and rate constant parameters, which in turn makes kinetic modeling a preferable choice for describing cellular processes (16,19,23). This point is particularly obvious for many well-characterized systems, including bacterial chemotaxis signaling networks (53,54), developmental pattern formation in Drosophila (55)(56)(57)(58), aggregation stage network of Dictyostelium (59), viral infection (60-65), circadian rhythms (66,67), E. coli stress response circuit (68), single E. coli cell growth (69), yeast cell-cycle control (70,71), and physiological processes (72)(73)(74).…”

Section: Toward Computational Systems Biology 171mentioning

confidence: 99%

Toward Computational Systems Biology

2004

CBB

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The development and successful application of high-throughput technologies are transforming biological research. The large quantities of data being generated by these technologies have led to the emergence of systems biology, which emphasizes large-scale, parallel characterization of biological systems and integration of fragmentary information into a coherent whole. Complementing the reductionist approach that has dominated biology for the last century, mathematical modeling is becoming a powerful tool to achieve an integrated understanding of complex biological systems and to guide experimental efforts of engineering biological systems for practical applications. Here I give an overview of current mainstream approaches in modeling biological systems, highlight specific applications of modeling in various settings, and point out future research opportunities and challenges.

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Electrophysiological Modeling of Cardiac Ventricular Function: From Cell to Organ

Cited by 110 publications

References 112 publications

Multi-Scale Modeling and Analysis of Left Ventricular Remodeling Post Myocardial Infarction: Integration of Experimental and Computational Approaches

Multi-Scale Modeling and Analysis of Left Ventricular Remodeling Post Myocardial Infarction: Integration of Experimental and Computational Approaches

Bioengineering and Systems Biology

Toward Computational Systems Biology

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