Joseph, Georghios ORCID: 0000-0003-1281-7176 (2018) Application of data science to inform surface engineering for in vitro neural stem cell control. Doctoral thesis, Keele University.

[img]
Preview
Text
JosephPhD2018.pdf

Download (8MB) | Preview

Abstract

The interest in the clinical use of stem cell therapies is increasing rapidly, with a need for more control over cell populations cultured/expanded in vitro. This is particularly relevant for the treatment of neurological disorders such as Parkinson's disease where positive outcome measures of clinical trials will be limited by the number of derived neurons and their specific sub-types. The aim is to generate enhanced neural cell populations from stem cells through the design of the cell-material interface.
The niche micro-environment is complex, being responsible for cell attachment, proliferation and differentiation. Material engineering approaches to better control cell responses have looked towards surface chemical, topographical and mechanical cues. The many permutations of these factors pose a major challenge in the optimisation of biomaterial design. Machine learning techniques will be used to assess the impact of surface properties on the biological micro-environment. Cell interaction/response provides computational outputs, with input variables being derived from material properties such as surface chemical characteristics (logP, charge, density, wettability, etc.) and topography (nano- and micro-scale, aspect ratio, etc). The aim is to unravel the relationship between cells and biomaterial surface of in vitro cell culture. In vitro experiments and in silico modelling will continually inform each other towards the optimisation of neural cell characteristic responses.

Item Type: Thesis (Doctoral)
Uncontrolled Keywords: Neural stem cells, nerve tissue engineering, silanes, machine-learning, predictive modelling, mathematical optimisation
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Natural Sciences > School of Computing and Maths
Depositing User: Lisa Bailey
Date Deposited: 11 Dec 2018 15:52
Last Modified: 12 Dec 2018 10:35
URI: http://eprints.keele.ac.uk/id/eprint/5577

Actions (login required)

View Item View Item