Animal cells and systems

The Korean Society for Integrative Biology (한국통합생물학회)
권호 표지
DOI QR코드

DOI QR Code

Deformation prediction by a feed forward artificial neural network during mouse embryo micromanipulation

  • Abbasi, Ali A. (School of Mechanical Engineering, Sharif University of Technology) ;
  • Vossoughi, G.R. (Center of Excellence in Design, Robotics and Automation (CEDRA), School of Mechanical Engineering, Sharif University of Technology) ;
  • Ahmadian, M.T. (Center of Excellence in Design, Robotics and Automation (CEDRA), School of Mechanical Engineering, Sharif University of Technology)
  • Received : 2011.06.12
  • Accepted : 2011.10.03
  • Published : 2012.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, a neural network (NN) modeling approach has been used to predict the mechanical and geometrical behaviors of mouse embryo cells. Two NN models have been implemented. In the first NN model dimple depth (w), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were used as inputs of the model while indentation force (f) was considered as output. In the second NN model, indentation force (f), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were considered as inputs of the model and dimple depth was predicted as the output of the model. In addition, sensitivity analysis has been carried out to investigate the influence of the significance of input parameters on the mechanical behavior of mouse embryos. Experimental data deduced by Fl$\ddot{u}$ckiger (2004) were collected to obtain training and test data for the NN. The results of these investigations show that the correlation values of the test and training data sets are between 0.9988 and 1.0000, and are in good agreement with the experimental observations.

Keywords

References

  1. Ahmadian MT, Vossoughi GR, Abbasi AA, Raeissi P. 2010. Cell deformation modeling under external force using artificial neural network. J Solid Mech. 2:190-198.
  2. Bahrami A, Mousavi Anijdan SH, Madaah Hosseini HR, Shafyei A, Narimani R. 2005. Effective parameters modeling in compression of an austenitic stainless steel using artificial neural network. Comput Mater Sci. 34:335-341. https://doi.org/10.1016/j.commatsci.2005.01.006
  3. Dao M, Lim CT, Suresh S. 2003. Mechanics of the human red blood cell deformed by optical tweezers. J Mech Phys Solids. 51:2259-2280. https://doi.org/10.1016/j.jmps.2003.09.019
  4. Dashtbayazi MR, Shokuhfar A, Simchi A. 2007. Artificial neural network modeling of mechanical alloying process for synthesizing of metal matrix nanocomposite powders. Mater Sci Eng A. 466:274-283. https://doi.org/10.1016/j.msea.2007.02.075
  5. Fluckiger M. 2004. Cell membrane mechanical modeling for microrobotic cell manipulation [diploma thesis]. Zurich: ETHZ Swiss Federal Institute of Technology, WS03/04.
  6. He JH, Xu W, Zhu L. 2007. Analytical model for extracting mechanical properties of a single cell in a tapered micropipette. Appl Phys Lett. 90:023901-023903. https://doi.org/10.1063/1.2430936
  7. Jajarmi P, Eivani AR. 2009. Modeling the electrical resistivity of Zn-Mn-S nanocrystalline semiconductors. Comput Mater Sci. 46:124-127. https://doi.org/10.1016/j.commatsci.2009.02.013
  8. Kim Y, Shin JH, Kim J. 2008. Atomic force microscopy probing for biomechanical characterization of living cells. Paper presented at: Proceedings of the 2nd Biennial IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, Scottsdale, AZ, USA.
  9. Lee A, Rhee M. 2009. Identification and expression patterns of kif3bz during the zebrafish embryonic development. Anim Cells Syst. 13:411-418. https://doi.org/10.1080/19768354.2009.9647237
  10. Lim CT, Zhou EH, Quek ST. 2006. Mechanical models for living cells - a review. J Biomechan. 39:195-216. https://doi.org/10.1016/j.jbiomech.2004.12.008
  11. Lulevich V, Zink T, Chen HY, Liu FT, Liu GY. 2006. Cell mechanics using atomic force microscopy-based singlecell compression. Langmuir. 22:8151-8155. https://doi.org/10.1021/la060561p
  12. Mandal S, Sivaprasad PV, Venugopal S, Murthy KPN. 2009. Arti?cial neural network modeling to evaluate and predict the deformation behavior of stainless steel type AISI 304L during hot torsion. Appl Soft Comput. 9:237-244. https://doi.org/10.1016/j.asoc.2008.03.016
  13. Nguyen HT, Prasad NR, Walker CR, Walker AE. 1944. A first course in fuzzy and neural control. Boca Raton: Chapman & Hall/CRC. p. 237-241.
  14. Sen S, Subramanian S, Discher DE. 2005. Indentation and adhesive probing of a cell membrane with AFM: theoretical model and experiments. Biophys J. 89:3203-3213. https://doi.org/10.1529/biophysj.105.063826
  15. Sun Y, Wan KT, Roberts KP, Bischof JC, Nelson BJ. 2003. Mechanical property characterization of mouse zona pellucida. IEEE Trans Nanobiosci. 2:279-286. https://doi.org/10.1109/TNB.2003.820273
  16. Tan Y, Sun D, Huang W, Cheng SH. 2010. Characterizing mechanical properties of biological cells by microinjection. IEEE Trans Nanobiosci. 9:171-180. https://doi.org/10.1109/TNB.2010.2050598
  17. Thoumine O, Ott A. 1997. Time scale dependent viscoelastic and contractile regimes in fibroblasts probed by microplate manipulation. J Cell Sci. 110:2109-2116.
  18. Vaziri A, Kaazempur Mofrad MR. 2007. Mechanics and deformation of the nucleus in micropipette aspiration experiment. J Biomechan. 40:2053-2062. https://doi.org/10.1016/j.jbiomech.2006.09.023
  19. Yang Y, Zhang Q. 1997. A hierarchical analysis for rock engineering using artificial neural networks. Rock Mech Rock Engg. 30:207-222. https://doi.org/10.1007/BF01045717
  20. Yazdanmehr M, Mousavi Anijdan SH, Samadi A, Bahrami A. 2009. Mechanical behavior modeling of nanocrystalline nial compound by a feed-forward back propagation multi-layer perceptron ANN. Comput Mater Sci. 44:1231-1235. https://doi.org/10.1016/j.commatsci.2008.08.006