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Quantifying the mechanics and growth of cells and tissues in 3D using high resolution computational models

View ORCID ProfilePaul Van Liedekerke, Johannes Neitsch, Tim Johann, Enrico Warmt, Ismael Gonzales Valverde, Stefan Höhme, Steffen Grosser, Josef Käs, Dirk Drasdo
doi: https://doi.org/10.1101/470559
Paul Van Liedekerke
1INRIA de Paris and Sorbonne Universités UPMC Univ paris 6, LJLL, France
4IfADo - Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany
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  • ORCID record for Paul Van Liedekerke
  • For correspondence: Paul.Van_Liedekerke@inria.fr Dirk.Drasdo@inria.fr
Johannes Neitsch
2IZBI, University of Leipzig, Leipzig, Germany
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Tim Johann
4IfADo - Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany
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Enrico Warmt
3Institute of experimental physics, University of Leipzig, Leipzig, Germany
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Ismael Gonzales Valverde
5M2BE - Multiscale in Mechanical and Biological Engineering - University of Zaragoza, Spain
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Stefan Höhme
2IZBI, University of Leipzig, Leipzig, Germany
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Steffen Grosser
3Institute of experimental physics, University of Leipzig, Leipzig, Germany
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Josef Käs
3Institute of experimental physics, University of Leipzig, Leipzig, Germany
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Dirk Drasdo
1INRIA de Paris and Sorbonne Universités UPMC Univ paris 6, LJLL, France
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  • For correspondence: Paul.Van_Liedekerke@inria.fr Dirk.Drasdo@inria.fr
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Abstract

Mathematical models are increasingly designed to guide experiments in biology, biotechnology, as well as to assist in medical decision making. They are in particular important to understand emergent collective cell behavior. For this purpose, the models, despite still abstractions of reality, need to be quantitative in all aspects relevant for the question of interest. The focus in this paper is to study the regeneration of liver after drug-induced depletion of hepatocytes, in which surviving dividing and migrating hepatocytes must squeeze through a blood vessel network to fill the emerged lesions. Here, the cells’ response to mechanical stress might significantly impact on the regeneration process. We present a 3D high-resolution cell-based model integrating information from measurements in order to obtain a refined quantitative understanding of the cell-biomechanical impact on the closure of drug-induced lesions in liver. Our model represents each cell individually, constructed as a physically scalable network of viscoelastic elements, capable of mimicking realistic cell deformation and supplying information at subcellular scales. The cells have the capability to migrate, grow and divide, and infer the nature of their mechanical elements and their parameters from comparisons with optical stretcher experiments. Due to triangulation of the cell surface, interactions of cells with arbitrarily shaped (triangulated) structures such as blood vessels can be captured naturally. Comparing our simulations with those of so-called center-based models, in which cells have a rigid shape and forces are exerted between cell centers, we find that the migration forces a cell needs to exert on its environment to close a tissue lesion, is much smaller than predicted by center-based models. This effect is expected to be even more present in chronic liver disease, where tissue stiffens and excess collagen narrows pores for cells to squeeze through.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 14, 2018.
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Quantifying the mechanics and growth of cells and tissues in 3D using high resolution computational models
Paul Van Liedekerke, Johannes Neitsch, Tim Johann, Enrico Warmt, Ismael Gonzales Valverde, Stefan Höhme, Steffen Grosser, Josef Käs, Dirk Drasdo
bioRxiv 470559; doi: https://doi.org/10.1101/470559
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Quantifying the mechanics and growth of cells and tissues in 3D using high resolution computational models
Paul Van Liedekerke, Johannes Neitsch, Tim Johann, Enrico Warmt, Ismael Gonzales Valverde, Stefan Höhme, Steffen Grosser, Josef Käs, Dirk Drasdo
bioRxiv 470559; doi: https://doi.org/10.1101/470559

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