Modeling adult skeletal stem cell response to laser-machined topographies through deep learning
Abstract
The response of adult human bone marrow stromal stem cells to surface topographies generated through femtosecond laser machining can be predicted by a deep neural network. The network is capable of predicting cell response to a statistically significant level, including positioning predictions with a probability P < 0.001, and therefore can be used as a model to determine the minimum line separation required for cell alignment, with implications for tissue structure development and tissue engineering. The application of a deep neural network, as a model, reduces the amount of experimental cell culture required to develop an enhanced understanding of cell behavior to topographical cues and, critically, provides rapid prediction of the effects of novel surface structures on tissue fabrication and cell signaling.
Keywords
Cite
@article{arxiv.2006.00248,
title = {Modeling adult skeletal stem cell response to laser-machined topographies through deep learning},
author = {Benita S. Mackay and Matthew Praeger and James A. Grant-Jacob and Janos Kanczler and Robert W. Eason and Richard O. C. Oreffo and Ben Mills},
journal= {arXiv preprint arXiv:2006.00248},
year = {2020}
}
Comments
Article accepted for publication in Tissue & Cell (ISSN 0040-8166) 11th Sep 2020