A mathematical model for the simulation of the contraction of burns View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-07

AUTHORS

Daniël C. Koppenol, Fred J. Vermolen, Gabriela V. Koppenol-Gonzalez, Frank B. Niessen, Paul P. M. van Zuijlen, Kees Vuik

ABSTRACT

A continuum hypothesis-based model is developed for the simulation of the contraction of burns in order to gain new insights into which elements of the healing response might have a substantial influence on this process. Tissue is modeled as a neo-Hookean solid. Furthermore, (myo)fibroblasts, collagen molecules, and a generic signaling molecule are selected as model components. An overview of the custom-made numerical algorithm is presented. Subsequently, good agreement is demonstrated with respect to variability in the evolution of the surface area of burns over time between the outcomes of computer simulations and measurements obtained in an experimental study. In the model this variability is caused by varying the values for some of its parameters simultaneously. A factorial design combined with a regression analysis are used to quantify the individual contributions of these parameter value variations to the dispersion in the surface area of healing burns. The analysis shows that almost all variability in the surface area can be explained by variability in the value for the myofibroblast apoptosis rate and, to a lesser extent, the value for the collagen molecule secretion rate. This suggests that most of the variability in the evolution of the surface area of burns over time in the experimental study might be attributed to variability in these two rates. Finally, a probabilistic analysis is used in order to investigate in more detail the effect of variability in the values for the two rates on the healing process. Results of this analysis are presented and discussed. More... »

PAGES

1-31

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00285-016-1075-4

    DOI

    http://dx.doi.org/10.1007/s00285-016-1075-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1041833544

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/27826736


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