Issue 7, 2013

Modeling and simulations of multicellular aggregate self-assembly in biofabrication using kinetic Monte Carlo methods

Abstract

We employ a three-dimensional (3D) lattice model based on the kinetic Monte Carlo (KMC) method to study cell self-assembly and cellular aggregate fusion of multicellular aggregate systems. This model is developed to describe and predict time evolution of postprinting morphological structure formation during morphogenesis of tissues or organs in a novel biofabrication process known as bioprinting. In this new technology, live multicellular aggregates are used as the fundamental building blocks, known as the bio-ink, to make tissue or organ constructs via the layer-by-layer deposition technique in biocompatible hydrogels; the printed bio-constructs embedded in the hydrogels are then placed in bioreactors to undergo the fusion process to form the desired functional tissue or organ products. In this paper, we implement the lattice model in an efficient list-based KMC algorithm to simulate the making of a set of tissue/organ constructs in several designer's geometries (a ring, a sheet and a tube), which involves a large number of cells of a single type and various other extracellular matrix materials like agarose etc. We then study the process of cell sorting in fusion of cellular aggregates involving multiple cell types with diverse adhesivities and compatible extracellular hydrogels. The KMC simulations reported in this paper agree qualitatively with the available experimental results. In the meantime, they are also comparable to the results obtained from cellular particle dynamics (CPD) simulations.

Graphical abstract: Modeling and simulations of multicellular aggregate self-assembly in biofabrication using kinetic Monte Carlo methods

Article information

Article type
Paper
Submitted
11 Sep 2012
Accepted
05 Dec 2012
First published
03 Jan 2013

Soft Matter, 2013,9, 2172-2186

Modeling and simulations of multicellular aggregate self-assembly in biofabrication using kinetic Monte Carlo methods

Y. Sun and Q. Wang, Soft Matter, 2013, 9, 2172 DOI: 10.1039/C2SM27090K

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