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Development of a Virtual Cell Model to Predict Cell Response to Substrate Topography Publisher Pubmed



Heydari T1 ; Heidari M2 ; Mashinchian O3, 4 ; Wojcik M5 ; Xu K5, 6 ; Dalby MJ7 ; Mahmoudi M8, 9 ; Ejtehadi MR1
Authors
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Authors Affiliations
  1. 1. Department of Physics, Sharif University of Technology, Tehran, 11155-9161, Iran
  2. 2. Max Planck Institute for Polymer Research, Ackermannweg 10, Mainz, 55128, Germany
  3. 3. Nestle Institute of Health Sciences (NIHS), EPFL, Innovation Park, Lausanne, 1015, Switzerland
  4. 4. Doctoral Program in Biotechnology and Bioengineering, Institute of Bioengineering, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, 1015, Switzerland
  5. 5. Department of Chemistry, University of California, Berkeley, 94720, CA, United States
  6. 6. Division of Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, United States
  7. 7. Centre for Cell Engineering, Institute of Molecular, Cell and Systems Biology, College of Medical Veterinary and Life Sciences, University of Glasgow, Joseph Black Building, Glasgow, G12 8QQ, United Kingdom
  8. 8. Nanotechnology Research Center, Department of Nanotechnology, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, 14155-6451, Iran
  9. 9. Department of Anesthesiology, Brigham and Women's Hospital, Harvard Medical School, Boston, 02115, MA, United States

Source: ACS Nano Published:2017


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. © 2017 American Chemical Society.
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