2015
DOI: 10.1007/s10237-015-0664-2
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Semi-stochastic cell-level computational modelling of cellular forces: application to contractures in burns and cyclic loading

Abstract: A phenomenological model is formulated to model cellular forces on extracellular material. The model is capable of modelling both expansion and contractile forces. This work is based on the assumption of linear elasticity, which allows a superposition argument to arrive at fundamental expressions for cellular forces. It is also shown how the cellular forces can be implemented using different strategies, as well as an extension to cellular point sources. Illustrations are given for modelling a (permanent) contr… Show more

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Cited by 20 publications
(26 citation statements)
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“…In our modelling the boundary of the domain coincided with the wound boundary and herewith the important process of contraction in burns could be modelled. The interested reader is able to read [53] to get to know more about the integration of the finite-element method into the cell-based modelling.…”
Section: Finite-element Approach For Mechanical Balancementioning
confidence: 99%
See 3 more Smart Citations
“…In our modelling the boundary of the domain coincided with the wound boundary and herewith the important process of contraction in burns could be modelled. The interested reader is able to read [53] to get to know more about the integration of the finite-element method into the cell-based modelling.…”
Section: Finite-element Approach For Mechanical Balancementioning
confidence: 99%
“…The mechanical forces exerted by the cells have to be monitored on the boundaries of the wound and therefore, finite-element simulations have been used. The details are presented in [53].…”
Section: Wound Contractionmentioning
confidence: 99%
See 2 more Smart Citations
“…Vermolen & Gefen [57] and Boon et al [58] have further extended the cell-based models to the simulation of wound contractures that occur in deep burns. The models were modified by combining mechanical balances with the cellbased models.…”
Section: Cell-based Modelsmentioning
confidence: 99%