In silico modelling of soft tissue scaffolds

Type of content
Theses / Dissertations
Publisher's DOI/URI
Thesis discipline
Chemical Engineering
Degree name
Doctor of Philosophy
Publisher
University of Canterbury
Journal Title
Journal ISSN
Volume Title
Language
English
Date
2020
Authors
Panidepu, Harshal
Abstract

Globally the number of people waiting for a lifesaving organ transplant heavily outweighs the number of organs available. This work is about using computer simulations to understand how to utilize “bioprinting” technology to address this issue. Bioprinting refers to 3D printing biocompatible scaffolds that provide support to cell growth in a three dimensional shape. Despite tremendous advances in tissue engineering, vascularization remains a key unsolved challenge in these bioprinted tissues.

Previous studies have reported oxygen limitation as a major problem leading to hypoxia and cell death of bioprinted tissues. The project theme is to explore scaffolds of different architecture, shapes and thicknesses, to quantify oxygen availability in these structures. Emphasis was placed on analysing scaffolds bioprinted as Triply Periodic Minimal Surfaces (TPMS). Computational Fluid Dynamic (CFD) studies coupled with high performance computing was used to investigate oxygen diffusion through scaffolds, using the Lattice Boltzmann Method (LBM). The CFD model simulated blood flow through TPMS structures while oxygen diffusion and consumption were simultaneously occurring within the tissue- scaffold system. The LBM model was developed with an open source software ‘Palabos’, which was coded in C++.

There are four distinct bodies of work presented in this thesis. In the first work, the LBM results of oxygen diffusion distances in tissues were verified with analytical model predictions (Krogh’s model), for a simple capillary case. The validation was done in the radial direction of the capillary, both in the presence and in absence of hydrogel. The second focus was to identify entrance and exit effects in the TPMS scaffold systems, which were addressed by using exit lengths and improving the steady state tolerance criteria of the simulations. Mesh independence studies were performed with LBM using the Schwarz Diamond (SD) TPMS structure as a case study. Supercomputing facilities were used to accelerate computing using the Message Passing Interface framework and slurm job scheduling system. The third focus was to understand the nature of oxygen concentration profiles in the axial direction of TPMS scaffolds, and to analyse the effect of scaffold permeability and thickness on the profiles. New concepts such as Specific Normal Area (SNA) were defined to quantify the thickness of TPMS scaffolds. Using gradient estimation of the axial concentration profiles and linear extrapolation techniques, tissue survival lengths were estimated for these tissue scaffolds, for the first time. The fourth aspect was to understand the factors that lead to enhanced mass transfer in TPMS structures. The Péclet number was quantified for flow through TPMS tissue shapes of different porosities, and the relative importance of advection and diffusion was analyzed. In this thesis, the mass transfer efficiency of nutrients was estimated with respect to partition coefficient, a novel way to rank scaffold shapes. In conclusion, SD is proposed as an efficient TPMS structure for i) favourable structure-induced permeation properties ii) minimum variation in its SNA and shorter diffusion paths iii) a reasonably high partition coefficient among TPMS structures. The CFD model provides a proof of concept for sustainable TPMS tissue-scaffold systems that could sustain up to 50% of the cells in a given space in the order of ~10 cm depending on the structural porosity, when the oxygen concentration in the perfused blood is ≥ 0.15 mM. The numerical model enables in silico testing of likely tissue viability, before bioprinting them in a particular shape. Thus, the LBM model reduces the time and cost involved in performing tissue vascularization studies in cell culture labs, using computational predictions to eliminate non-viable tissue scaffolds from a set of geometries under consideration.

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