In silico exploration of post-burn contraction using uncertainty quantification

Research output: ThesisDissertation (TU Delft)

2 Downloads (Pure)


Burns can make patients’ lives quite miserable. Apart from prominent and thickened, or hypertrophic, scars, the skin may be characterized by contraction. When this contraction is so severe that the patient loses joint mobility, it is called contracture. Then a patient may have difficulties with sports or other daily activities. The consequence can be an enormous psychosocial burden for the patient. Understanding contraction mechanisms is essential to improve and optimize the treatment of contractures. This understanding can arise from clinical (in vivo) and experimental (in vitro) observations but can also be explored using mathematical models (in silico). Mathematical models describe quantitative relations and can explain specific trends and make predictions. Further, in silico models forman alternative for animal experiments. One such mathematical model is the Biomorphoelastic model for post-burn contraction [1]. This model arises from conservation laws expressed in partial differential equations on a continuous (macro) scale. We study this model’s one- and twodimensional counterparts...
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Delft University of Technology
  • Vuik, C., Supervisor
  • Vermolen, F.J., Supervisor
  • van Zuijlen, Paul P.M., Supervisor, External person
Award date18 Sep 2023
Print ISBNs978-94-6384-463-5
Publication statusPublished - 2023


Dutch Burns Foundation


  • Skin Burns
  • Post-burn Scars
  • Post-burn Contraction
  • Contracture Formation, Morphoelasticity
  • Fibroblasts
  • Myofibroblasts,
  • Cell Proliferation
  • Signaling Molecules
  • Collagen,
  • Dermal Displacements
  • Dermal Strains
  • Moving-grid
  • Finite Element Method
  • Relative Scar/Wound Area
  • Strain Energy
  • Stability Analysis
  • Sensitivity Analysis
  • Feasibility Study
  • Artificial Intelligence
  • Machine Learning
  • Feed-forward Neural Network
  • Medical Application
  • Monte Carlo Simulations

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