Cell Deformation Modeling Under External Force Using Artificial Neural Network
محورهای موضوعی : Engineering
M.T Ahmadian
1
(Center of Excellence in Design, Robotics and Automation (CEDRA), School of Mechanical Engineering, Sharif University of Technology)
G.R Vossoughi
2
(Center of Excellence in Design, Robotics and Automation (CEDRA), School of Mechanical Engineering, Sharif University of Technology)
A.A Abbasi
3
(School of Mechanical Engineering, Sharif University of Technology)
P Raeissi
4
(Iran University of Medical Sciences, Tehran)
کلید واژه: Artificial Neural Network, Biological cells, Error back propagation algorithm,
چکیده مقاله :
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.
[1] Lim, C.T., Zhou, E.H., Quek, S.T., 2006, Mechanical models for living cells - a review, Journal of Biomechanics 39: 195-216.
[2] Sen, S., Subramanian S., Discher D.E., 2005, Indentation and adhesive probing of a cell membrane with AFM: Theoretical model and experiments, Biophysical Journal 89: 3203-3213.
[3] Lulevich V., Zink T., Chen H.Y., Liu F.T., Liu G.Y., 2006, Cell Mechanics using atomic force microscopy - based single-cell compression, Langmuir 22:8151-8155.
[4] Dao M., Lim C.T., Suresh S., 2003, Mechanics of the human red blood cell deformed by optical tweezers, Journal of the Mechanics and Physics of Solids 51: 2259-2280.
[5] Thoumine O.,Ott A., 1997, Time scale dependent viscoelastic and contractile regimes in fibroblasts probed by microplate manipulation, Journal of Cell Science 110: 2109-2116.
[6] Vaziri A., Kaazempur Mofrad M.R., 2007, Mechanics and deformation of the nucleus in micropipette aspiration Experiment, Journal of Biomechanics 40: 2053-2062.
[7] He J.H., Xu W., Zhu L., 2007, Analytical model for extracting mechanical properties of a single cell in a tapered micropipette, Applied Physics Letters 90: 023901.
[8] Sterjovski Z., Nolan D., Carpenter K.R., Dunne D.P., Norrish J., 2005, Artificial neural networks for modelling the mechanical properties of steels in various applications, Journal of Materials Processing Technology 170: 536-544.
[9] Dashtbayazi M.R., Shokuhfar A., Simchi A., 2007, Artificial neural network modeling of mechanical alloying process for synthesizing of metal matrix nanocomposite powders, Materials Science and Engineering A 466: 274-283.
[10] Sun Y., Wan K.T., Roberts K.P., Bischof J.C., Nelson B.J., 2003, Mechanical Property Characterization of Mouse Zona Pellucida, IEEE Transactions on Nanobioscience 2: 279-286.
[11] Zahalak G.I., McConnaughey W.B., Elson E.L., 1990, Determination of cellular mechanical properties by cell poking, with an application to leukocytes, Journal of biomechanical engineering 112: 283-294.
[12] Bahrami A., Mousavi Anijdan S.H., Madaah Hosseini H.R.,2005, Effective parameters modeling in compression of an austenitic stainless steel using artificial neural network, Computational Materials Science 34: 335-341.
[13] Yazdanmehr M., Mousavi Anijdan S.H., Samadi A., Bahrami A., 2009, Mechanical behavior modeling of nanocrystalline NiAl compound by a feed-forward back-propagation multi-layer perceptron ANN, Computational Materials Science 44: 1231-1235.
[14] Samarasinghe S., 2006, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Reorganization, Auerbach Publications, Taylor & Francis Group, Boca Roton, New York.
[15] Sarangapani J., 2006, Neural Network Control of Nonlinear Discrete-Time Systems, CRC Press ,Taylor & Francis Group, Boca Roton, London, New York.
[16] Flückiger M., 2004, Cell Membrane Mechanical Modeling for Microrobotic Cell Manipulation, Diploma Thesis, ETHZ Swiss Federal Institute of Technology, Zurich, WS03/04.
