Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.759671
Title: New mathematical methods for the study of stem cell differentiation
Author: Camacho Aguilar, Elena
ISNI:       0000 0004 7431 7000
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 2018
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Thesis embargoed until 29 Jun 2020
Access from Institution:
Abstract:
The question of how the fertilized egg develops into an adult organism is one of the most fundamental ones in Biology. A very important stage in the development of the embryo is cell differentiation, in which unspecialised cells, called stem cells, become specialised ones, such as skin or nerve cells depending on the signals that they receive. This is controlled by a very large network of genes that interact with each other, the state of which defines the characteristics of the cell. With the recent development of experimental techniques that allow us to obtain very detailed information about the changes in cells, new data analysis methods and mathematical models are required for the understanding of stem cell differentiation. A common approach to the mathematical modelling of stem cell differentiation is by means of gene regulatory network (GRN) models describing the gene regulation behind the process. However, the number of variables and parameters in these models rapidly scales up as one tries to study more genes in the network, difficulting its analysis. This thesis aims to assess these problems and it is structured into two main parts. In the first one, which comprises Chapters 3 and 4, we will develop a phenotypic quasi-potential landscape model for vulval development in C. elegans to illustrate how catastrophe theory can be a powerful tool to construct and understand these recently emerging types of models. Moreover, will use advanced statistical techniques to fit the built model to the experimental data. The second part, in Chapter 5, will be devoted to developing a methodology to understand protein expression data in order to reverse engineer the gene regulatory network from it and create a mathematical model that explains such experimental data.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.759671  DOI: Not available
Keywords: QA Mathematics ; QH426 Genetics
Share: