Application of data science to inform surface engineering for in vitro neural stem cell control

Joseph, Georghios

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.

Citation

Joseph, G. (2018). Application of data science to inform surface engineering for in vitro neural stem cell control. (Thesis). Keele University. Retrieved from https://keele-repository.worktribe.com/output/412289

• Thesis Type: Thesis
• Publicly Available Date: May 26, 2023
• Keywords: Neural stem cells, nerve tissue engineering, silanes, machine-learning, predictive modelling, mathematical optimisation
• Public URL: https://keele-repository.worktribe.com/output/412289
• Award Date: 2018-10