Skip to main content

Linear and Nonlinear Modeling of Protein Kinase B/AkT

  • Conference paper
  • First Online:
Proceedings of International Conference on ICT for Sustainable Development

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 409))

  • 1395 Accesses

Abstract

AkT is the main protein which was frequently occurred in the human cancer. There are several pathways of AkT which were shown in this paper which lead to cell death/survival. In this paper, we have used the mathematical analysis (linear modeling and non linear modeling) to make a best model of the survival/death proteins, i.e., epidermal growth factor, tumor necrosis factor, and insulin using ten different combinations. The model was made using linear modeling using different regression analysis techniques in which different parameters like mean sq error, root mean sq error, mean abs error, relative sq error, root relative sq error, and relative abs error were analyzed. Later on, Kolmogorov–Smirnov, Anderson–Darling, and chi-square tests were done using different distribution functions. Results with half-normal distribution function are the best as their AD and chi-square values are the maximum. Nonlinear modeling was done using neural network with MLP and RBF approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Staal, S. P. (1987). Molecular cloning of the akt oncogene and its human homologues AKT1 and AKT2: Amplification of AKT1 in a primary human gastric adenocarcinoma. Proceedings of the National Academy of Sciences of the USA, 84, 5034–5037.

    Article  Google Scholar 

  2. Vanhaesebroeck, B., & Alessi, D. (2000). The PI3K-PDK1 connection: More than just a road to PKB. Biochemical Journal, 346, 561–576.

    Google Scholar 

  3. Coffer, P. J., Jin, J., & Woodgett, J. R. (1998). Protein kinase B (c-Akt): A multifunctional mediator of phosphatidylinositol 3-kinase activation. Biochemical Journal, 335, 1–13.

    Article  Google Scholar 

  4. Brazil, D. P., & Hemmings, B. A. (2001). Ten years of protein kinase B signalling: A hard Akt to follow. Trends in Biochemical Sciences, 26, 657–664.

    Article  Google Scholar 

  5. Hemmings, B. A. (1997). Akt signaling: Linking membrane events to life and death decisions. Science, 275, 628–630.

    Article  Google Scholar 

  6. Cohen, P., Alessi, D. R., & Cross, D. A. E. (1997). PDK1, one of the missing links in insulin signal transduction? FEBS Letters, 410, 3–10.

    Article  Google Scholar 

  7. Jain, S. (2012). Communication of signals and responses leading to cell survival/cell death using engineered regulatory networks. PhD Thesis, Jaypee University of Information Technology, Solan, Himachal Pradesh, India.

    Google Scholar 

  8. Jain, S., Bhooshan, S. V., & Naik P. K., Compendium model of AkT for cell survival/death and its equivalent BioCircuit. International Journal of Soft Computing and Engineering, 2(3), 91–97.

    Google Scholar 

  9. Weiss, R. (2001). Cellular computation and communications using engineered genetic regulatory networks. PhD Thesis, MIT.

    Google Scholar 

  10. Gaudet, S., Janes Kevin, A., Albeck John, G., Pace Emily, A., Lauffenburger Douglas, A., & Sorger Peter, K. (2005). A compendium of signals and responses trigerred by prodeath and prosurvival cytokines Manuscript M500158-MCP200.

    Google Scholar 

  11. Brockhaus, M., Schoenfeld, H. J., Schlaeger, E. J., Hunziker, W., Lesslauer, W., & Loetscher, H. (1990). Identification of two types of tumor necrosis factor receptors on human cell lines by monoclonal antibodies. Proceedings of the National Academy of Sciences of the USA, 87, 3127–3131.

    Google Scholar 

  12. Thoma, B., Grell, M., Pfizenmaier, K., & Scheurich, P. (1990). Identification of a 60-kD tumor necrosis factor (TNF) receptor as the major signal transducing component in TNF responses. Journal of Experimental Medicine, 172, 1019–1023.

    Article  Google Scholar 

  13. Ullrich, A., & Schlessinger, J. (1990). Signal transduction by receptors with tyrosine kinase activity. Cell, 61, 203–211.

    Article  Google Scholar 

  14. Arteaga, C. (2003). Targeting HER1/EGFR: A molecular approach to cancer therapy. Seminars in Oncology, 30, 314.

    Google Scholar 

  15. Lizcano, J. M., & Alessi, D. R. (2002). The insulin signalling pathway. Current Biology, 12, 236–238.

    Google Scholar 

  16. White, Morris F. (1997). The insulin signaling system and the IRS proteins. Diabetologia, 40, S2–S17.

    Article  Google Scholar 

  17. Jain, S., Naik, P. K., & Bhooshan, S. V. (2011). Mathematical modeling deciphering balance between cell survival and cell death using insulin. Network Biology, 1(1), 46–58.

    Google Scholar 

  18. Kim, D., & Chung, J. (2002). Akt: Versatile mediator of cell survival and beyond. Journal of Biochemistry and Molecular Biology, 35(1), 106–115.

    Article  Google Scholar 

  19. Brunet, A., Bonni, A., Zigmond, M. J., Lin, M. Z., Juo, P., & Hu, L. S. (1999). Akt promotes cell survival by phosphorylating and inhibiting a Forkhead transcription factor. Cell, 96, 857–868.

    Article  Google Scholar 

  20. Datta, S. R., Dudek, H., Tao, X., Masters, S., Gotoh, H., & Fu, Y. (1997). Akt phosphorylation of BAD couples survival signals to the cell-intrinsic death machinery. Cell, 91, 231–241.

    Article  Google Scholar 

  21. Cardone, M. H., Roy, N., Stennicke, H. R., Salvesen, G. S., Franke, T. F., & Stanbridge, F. (1998). Regulation of cell death protease caspase-9 by phosphorylation. Science, 282, 1318–1321.

    Article  Google Scholar 

  22. Jain, S., & Chauhan, D. S. (2015). Mathematical analysis of receptors for survival proteins. International Journal of Pharma and Bio Sciences, 6(3), 164–176.

    Google Scholar 

  23. Mandic, D., & Chambers, J. (2001). Recurrent neural networks for prediction: Learning algorithms, architectures and stability. New York: John Wiley & Sons.

    Book  Google Scholar 

  24. Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford, UK: Oxford University Press.

    Google Scholar 

  25. Rumelhart, D. E., Williams, R. J., & Hinton, G. E. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shruti Jain .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Science+Business Media Singapore

About this paper

Cite this paper

Jain, S., Chauhan, D.S. (2016). Linear and Nonlinear Modeling of Protein Kinase B/AkT. In: Satapathy, S., Joshi, A., Modi, N., Pathak, N. (eds) Proceedings of International Conference on ICT for Sustainable Development. Advances in Intelligent Systems and Computing, vol 409. Springer, Singapore. https://doi.org/10.1007/978-981-10-0135-2_7

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-0135-2_7

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0133-8

  • Online ISBN: 978-981-10-0135-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics