Heydari, T., Heidari, M., Mashinchian, O., Wojcik, M., Xu, K., Dalby, M. J. , Mahmoudi, M. and Ejtehadi, M. R. (2017) Development of a virtual cell model to predict cell response to substrate topography. ACS Nano, 11(9), pp. 9084-9092. (doi: 10.1021/acsnano.7b03732) (PMID:28742318)
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Abstract
Cells can sense and respond to changes in the topographical, chemical, and mechanical information in their environment. Engineered substrates are increasingly being developed that exploit these physical attributes to direct cell responses (most notably mesenchymal stem cells) and therefore control cell behavior toward desired applications. However, there are very few methods available for robust and accurate modeling that can predict cell behavior prior to experimental evaluations, and this typically means that many cell test iterations are needed to identify best material features. Here, we developed a unifying computational framework to create a multicomponent cell model, called the "virtual cell model" that has the capability to predict changes in whole cell and cell nucleus characteristics (in terms of shape, direction, and even chromatin conformation) on a range of cell substrates. Modeling data were correlated with cell culture experimental outcomes in order to confirm the applicability of the virtual cell model and demonstrating the ability to reflect the qualitative behavior of mesenchymal stem cells. This may provide a reliable, efficient, and fast high-throughput approach for the development of optimized substrates for a broad range of cellular applications including stem cell differentiation.
| Item Type: | Articles |
|---|---|
| Keywords: | Micro/nanosubstrates, multicomponent cell model, stiffness, topography, virtual cell. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Dalby, Professor Matthew |
| Authors: | Heydari, T., Heidari, M., Mashinchian, O., Wojcik, M., Xu, K., Dalby, M. J., Mahmoudi, M., and Ejtehadi, M. R. |
| College/School: | College of Medical Veterinary and Life Sciences > School of Molecular Biosciences |
| Journal Name: | ACS Nano |
| Publisher: | American Chemical Society |
| ISSN: | 1936-0851 |
| ISSN (Online): | 1936-086X |
| Published Online: | 25 July 2017 |
| Copyright Holders: | Copyright © 2017 American Chemical Society |
| First Published: | First published in ACS Nano 11(9): 9084-9092 |
| Publisher Policy: | Reproduced under a Creative Commons license |
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