Model-based data analysis of tissue growth in thin 3D printed scaffolds

被引:21
作者
Browning, Alexander P. [1 ,2 ]
Maclaren, Oliver J. [3 ]
Buenzli, Pascal R. [1 ]
Lanaro, Matthew [4 ]
Allenby, Mark C. [4 ]
Woodruff, Maria A. [4 ]
Simpson, Matthew J. [1 ,2 ]
机构
[1] Queensland Univ Technol, Sch Math Sci, Brisbane, Qld, Australia
[2] QUT, Arc Ctr Excellence Math & Stat Frontiers, Brisbane, Qld, Australia
[3] Univ Auckland, Dept Engn Sci, Auckland 1142, New Zealand
[4] Queensland Univ Technol, Ctr Biomed Technol, Sch Mech Med & Proc Engn, Brisbane, Qld, Australia
基金
澳大利亚研究理事会;
关键词
Tissue engineering; Uncertainty quantification; 3D printing; Parameter estimation; Porous-Fisher; Reaction-diffusion; PROFILE LIKELIHOOD; CELL-PROLIFERATION; GEOMETRY; DESIGN; IDENTIFIABILITY; OPTIMIZATION; DIFFUSIVITY; FABRICATION; ASSAY; WAVE;
D O I
10.1016/j.jtbi.2021.110852
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Tissue growth in three-dimensional (3D) printed scaffolds enables exploration and control of cell behaviour in more biologically realistic geometries than that allowed by traditional 2D cell culture. Cell proliferation and migration in these experiments have yet to be explicitly characterised, limiting the ability of experimentalists to determine the effects of various experimental conditions, such as scaffold geometry, on cell behaviour. We consider tissue growth by osteoblastic cells in melt electro-written scaffolds that comprise thin square pores with sizes that were deliberately increased between experiments. We collect highly detailed temporal measurements of the average cell density, tissue coverage, and tissue geometry. To quantify tissue growth in terms of the underlying cell proliferation and migration processes, we introduce and calibrate a mechanistic mathematical model based on the Porous-Fisher reaction-diffusion equation. Parameter estimates and uncertainty quantification through profile likelihood analysis reveal consistency in the rate of cell proliferation and steady-state cell density between pore sizes. This analysis also serves as an important model verification tool: while the use of reaction-diffusion models in biology is widespread, the appropriateness of these models to describe tissue growth in 3D scaffolds has yet to be explored. We find that the Porous-Fisher model is able to capture features relating to the cell density and tissue coverage, but is not able to capture geometric features relating to the circularity of the tissue interface. Our analysis identifies two distinct stages of tissue growth, suggests several areas for model refinement, and provides guidance for future experimental work that explores tissue growth in 3D printed scaffolds. (C) 2021 Elsevier Ltd. All rights reserved.
引用
收藏
页数:14
相关论文
共 74 条
  • [1] A level-set method for the evolution of cells and tissue during curvature-controlled growth
    Alias, Mohd Almie
    Buenzli, Pascal R.
    [J]. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING, 2020, 36 (01)
  • [2] Osteoblasts infill irregular pores under curvature and porosity controls: a hypothesis-testing analysis of cell behaviours
    Alias, Mohd Almie
    Buenzli, Pascal R.
    [J]. BIOMECHANICS AND MODELING IN MECHANOBIOLOGY, 2018, 17 (05) : 1357 - 1371
  • [3] Growth and remodelling of living tissues: perspectives, challenges and opportunities
    Ambrosi, Davide
    Ben Amar, Martine
    Cyron, Christian J.
    DeSimone, Antonio
    Goriely, Alain
    Humphrey, Jay D.
    Kuhl, Ellen
    [J]. JOURNAL OF THE ROYAL SOCIETY INTERFACE, 2019, 16 (157)
  • [4] [Anonymous], 2013, ALL LIKELIHOOD STAT
  • [5] Patient-Specific Metrics of Invasiveness Reveal Significant Prognostic Benefit of Resection in a Predictable Subset of Gliomas
    Baldock, Anne L.
    Ahn, Sunyoung
    Rockne, Russell
    Johnston, Sandra
    Neal, Maxwell
    Corwin, David
    Clark-Swanson, Kamala
    Sterin, Greg
    Trister, Andrew D.
    Malone, Hani
    Ebiana, Victoria
    Sonabend, Adam M.
    Mrugala, Maciej
    Rockhill, Jason K.
    Silbergeld, Daniel L.
    Lai, Albert
    Cloughesy, Timothy
    McKhann, Guy M., II
    Bruce, Jeffrey N.
    Rostomily, Robert C.
    Canoll, Peter
    Swanson, Kristin R.
    [J]. PLOS ONE, 2014, 9 (10):
  • [6] Cell Microenvironment Engineering and Monitoring for Tissue Engineering and Regenerative Medicine: The Recent Advances
    Barthes, Julien
    Oezcelik, Hayriye
    Hindie, Mathilde
    Ndreu-Halili, Albana
    Hasan, Anwarul
    Vrana, Nihal Engin
    [J]. BIOMED RESEARCH INTERNATIONAL, 2014, 2014
  • [7] Rational design and fabrication of multiphasic soft network composites for tissue engineering articular cartilage: A numerical model-based approach
    Bas, Onur
    Lucarotti, Sara
    Angella, Davide D.
    Castro, Nathan J.
    Meinert, Christoph
    Wunner, Felix M.
    Rank, Ernst
    Vozzi, Giovanni
    Klein, Travis J.
    Catelas, Isabelle
    De-Juan-Pardo, Elena M.
    Hutmacher, Dietmar W.
    [J]. CHEMICAL ENGINEERING JOURNAL, 2018, 340 : 15 - 23
  • [8] Julia: A Fresh Approach to Numerical Computing
    Bezanson, Jeff
    Edelman, Alan
    Karpinski, Stefan
    Shah, Viral B.
    [J]. SIAM REVIEW, 2017, 59 (01) : 65 - 98
  • [9] A three-dimensional model for tissue deposition on complex surfaces
    Bidan, Cecile M.
    Wang, Frances M.
    Dunlop, John W. C.
    [J]. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING, 2013, 16 (10) : 1056 - 1070
  • [10] Geometry as a Factor for Tissue Growth: Towards Shape Optimization of Tissue Engineering Scaffolds
    Bidan, Cecile M.
    Kommareddy, Krishna P.
    Rumpler, Monika
    Kollmannsberger, Philip
    Fratzl, Peter
    Dunlop, John W. C.
    [J]. ADVANCED HEALTHCARE MATERIALS, 2013, 2 (01) : 186 - 194