Scientific carrier

Master of Philosophy Thesis

Engineered tissues have many potential applications both as experimental systems and in therapeutic devices and treatments. Specially, there is potential to repair damaged spinal cord, however work is still needed to predict the process of tissue growth. Computational physics has potential to significantly accelerate the progress of development and to improve the properties of engineered neural tissue (ENT). Research has found that correlation exists between physical forces and the arrangement of cells in engineered tissue. In this thesis, emphasis is put on the interaction between atrocities within engineered neural tissue (ENT). The main aim of this work is to create a computational model which will be able to predict the arrangement of these cells in ENT. Computational techniques of soft matter physics are used to describe the final growth state of cells in ENT. In this thesis, we treat tissue as a two dimensional mesh. Interactions are described by both a simple Hamiltonian related to cell orientation and the dipole force model (DFM) and a more advanced Hamiltonian, which takes into account the influence of the extracellular matrix and cells. To find the minimum energy state of the system a simulated annealing algorithm was used. The first set of results presents a two-dimensional map of real artificial engineered neural tissue and shows the statistical distribution of the average angles and positions of groups of cells within the tissue. Next, we present results generated by a simple Hamiltonian and more advanced model to investigate information about orientation, arrangement and shape of the whole tissue. It is found that an extended DFM where cells can move and change orientation is able to reproduce several features of the experimental data including an approximation to the delta regions and the aligned behaviour in the centre of the sample.

The article

Microscopic biophysical model of self-organization in tissue due to feedback between cell- and macroscopic-scale forces.

Abstract

We develop a microscopic biophysical model for self-organization and reshaping of artificial tissue, that is codriven by microscopic active forces between cells and an extracellular matrix (ECM), and macroscopic forces that develop within the tissue, finding close agreement with experiment. Microscopic active forces are stimulated by μm -scale interactions between cells and the ECM within which they exist, and when large numbers of cells act together these forces drive, and are by, macroscopic-scale self-organization and reshaping of tissues in a feedback loop. To understand this loop, there is a need to (1) construct microscopic biophysical models that can simulate these processes for the very large number of cells found in tissues, (2) validate and calibrate those models against experimental data, and (3) understand the active feedback between cells and the matrix, and its relationship to macroscopic self-organization and reshaping of tissue. Our microscopic biophysical model consists of a contractile network representing the ECM, that interacts with a large number of cells via dipole forces, to describe macroscopic self-organization and reshaping of tissue. We solve the model using simulated annealing, finding close agreement with experiments on artificial neural tissue. We discuss the calibration of model parameters. We conclude that feedback between microscopic cell-ECM dipole interactions and tissue-scale forces is a key factor in driving macroscopic self-organization and reshaping of tissue. We discuss the application of the biophysical model to the simulation and rational design of artificial tissues.