Ahmadian, M., Vossoughi, G., Abbasi, A., Raeissi, P. (2010). Cell Deformation Modeling Under External Force Using Artificial Neural Network. Journal of Solid Mechanics, 2(2), 190-198.

M.T Ahmadian; G.R Vossoughi; A.A Abbasi; P Raeissi. "Cell Deformation Modeling Under External Force Using Artificial Neural Network". Journal of Solid Mechanics, 2, 2, 2010, 190-198.

Ahmadian, M., Vossoughi, G., Abbasi, A., Raeissi, P. (2010). 'Cell Deformation Modeling Under External Force Using Artificial Neural Network', Journal of Solid Mechanics, 2(2), pp. 190-198.

Ahmadian, M., Vossoughi, G., Abbasi, A., Raeissi, P. Cell Deformation Modeling Under External Force Using Artificial Neural Network. Journal of Solid Mechanics, 2010; 2(2): 190-198.

Cell Deformation Modeling Under External Force Using Artificial Neural Network

^{1}Center of Excellence in Design, Robotics and Automation (CEDRA), School of Mechanical Engineering, Sharif University of Technology

^{2}School of Mechanical Engineering, Sharif University of Technology

^{3}Iran University of Medical Sciences, Tehran

Abstract

Embryogenesis, regeneration and cell differentiation in microbiological entities are influenced by mechanical forces. Therefore, development of mechanical properties of these materials is important. Neural network technique is a useful method which can be used to obtain cell deformation by the means of force-geometric deformation data or vice versa. Prior to insertion in the needle injection process, deformation and geometry of cell under external point-load is a key element to understand the interaction between cell and needle. In this paper, the goal is the prediction of cell membrane deformation under a certain force and to visually estimate the force of indentation on the membrane from membrane geometries. The neural network input and output parameters are associated to a three dimensional model without the assumption of the adherent affects. The neural network is modeled by applying error back propagation algorithm. In order to validate the strength of the developed neural network model, the results are compared with the experimental data on mouse oocyte and mouse embryos that are captured from literature. The results of the modeling match nicely the experimental findings.

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