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Comparison of deformation analysis of a biological cell under an injection force using analytical, experimental and finite element methods and Artificial Neural Network

Sarvi, M. N ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1115/IMECE2011-63791
  3. Abstract:
  4. Biological cell injection is a sensitive and important work which is implemented in injection of foreign materials into individual cells. Microinjection is significantly developed in the field of drug discovery and genetics so predicting the behavior of cell in microinjection is remarkably important because a tiny excessive manipulation force can destroy the tissue of the biological cell. There are a few analytical methods available to simulate the cell injection, hence the numerical methods such as FEM are suitable to be used to model the microinjection. In this study, a new spherical super element is presented to model the biological cells and deformation of a specific cell under an external force is performed. The relationship between the injection force and the deformation of biological cell is demonstrated by using super element formulations. For validating the model, results are compared with findings of analytical and experimental methods and an Artificial Neural Network (ANN) model. The advantage of this element is that only a few super elements can predict the static behavior of biological cell in microinjection properly instead of implementing a large number of conventional elements, so using the super element to model the cell can decrease the run time with suitable accuracy
  5. Keywords:
  6. Biological cell ; Finite element method ; Microinjection ; Spherical super element ; Analytical method ; Artificial neural network models ; Biological cells ; Cell injection ; Deformation analysis ; Drug discovery ; Experimental methods ; External force ; Foreign materials ; Individual cells ; Injection force ; Micro-injection ; Runtimes ; Static behaviors ; Super elements ; Cells ; Comminution ; Cytology ; Deformation ; Exhibitions ; Mechanical engineering ; Neural networks ; Tissue ; Molecular biology
  7. Source: ASME 2011 International Mechanical Engineering Congress and Exposition, IMECE 2011 ; Volume 2 , 2011 , Pages 499-507 ; 9780791854884 (ISBN)
  8. URL: http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=1642491