Skip to main content
SpringerLink
Log in

Modeling PI3K/PDK1/Akt and MAPK Signaling Pathways Using Continuous Petri Nets

  • Conference paper
  • First Online:
Intelligent Computing Theories and Application (ICIC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10362))

Included in the following conference series:

  • 2097 Accesses

Abstract

Malignant melanoma is an invasive skin cancer commonly resistant to conventional therapeutic approaches. Genetic and molecular alterations as mutations of BRAF gene, able to constitutively activate MAPK and PI3K/PDK1/Akt signalling pathways, seem to be responsible of malignant melanocytic transformation and lead to aberrant cellular physiological processes. Specific regulators and modulators of both signaling pathways may represent promising therapeutic targets to investigate drug resistance typical of BRAF-inhibitors such as Dabrafenib. We developed a continuous Petri Net model that simulates both MAPK and PI3K/PDK1/Akt pathways and their interactions in order to analyze the complex kinase cascades in melanoma and to predict new crucial nodes involved in drug resistance like in the Ras arm.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Russo, A.E., Torrisi, E., Bevelacqua, Y., Perrotta, R., Libra, M., McCubrey, J.A., Spandidos, D.A., Stivala, F., Malaponte, G.: Melanoma: molecular pathogenesis and emerging target therapies (Review). Int. J. Oncol. 34, 414–420 (2014)

    Google Scholar 

  2. Ferlay, J., Soerjomataram, I., Ervik, M., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., Parkin, D.M., Forman, D., Bray, F.: Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136, 359–386 (2013)

    Article  Google Scholar 

  3. Manzano, J.L., Layos, L., Bugés, C., de Los Llanos Gil, M., Vila, L., Martínez-Balibrea, E., Martínez-Cardús, A.: Resistant mechanisms to BRAF inhibitors in melanoma. Ann. Transl. Med. 4, 237 (2016)

    Article  Google Scholar 

  4. Libra, M., Malaponte, G., Navolanic, P.M., Gangemi, P., Bevelacqua, V., Proietti, L., Bruni, B., Stivala, F., Mazzarino, M.C., Travali, S., McCubrey, J.A.: Analysis of BRAF mutation in primary and metastatic melanoma. Cell Cycle 4, 1382–1384 (2005)

    Article  Google Scholar 

  5. Garnett, M.J., Marais, R.: Guilty as charged: B-RAF is a human oncogene. Cancer Cell 6, 313–319 (2004)

    Article  Google Scholar 

  6. Brodland, G.W.: How computational models can help unlock biological systems. Semin. Cell Dev. Biol. 47–48, 62–73 (2015)

    Article  Google Scholar 

  7. Pappalardo, F., Fichera, E., Paparone, N., Lombardo, A., Pennisi, M., Russo, G., Leotta, M., Pappalardo, F., Pedretti, A., De Fiore, F., Motta, S.: A computational model to predict the immune system activation by citrus-derived vaccine adjuvants. Bioinformatics 32, 2672–2680 (2016)

    Article  Google Scholar 

  8. Castiglione, F., Pappalardo, F., Bianca, C., Russo, G., Motta, S.: Modeling biology spanning different scales: An open challenge. Biomed. Res. Int. 2014, 902545 (2014)

    Article  Google Scholar 

  9. Gullo, F., van der Garde, M., Russo, G., Pennisi, M., Motta, S., Pappalardo, F., Watt, S.: Computational modeling of the expansion of human cord blood CD133 + hematopoietic stem/progenitor cells with different cytokine combinations. Bioinformatics 31, 2514–2522 (2015)

    Article  Google Scholar 

  10. Pappalardo, F., Palladini, A., Pennisi, M., Castiglione, F., Motta, S.: Mathematical and computational models in tumor immunology. Math. Model. Nat. Phenom. 7, 186–203 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  11. Pappalardo, F., Russo, G., Candido, S., Pennisi, M., Cavalieri, S., Motta, S., McCubrey, J.A., Nicoletti, F., Libra, M.: Computational modeling of PI3K/AKT and MAPK signaling pathways in melanoma Cancer. PLoS ONE 11, e0152104 (2016)

    Article  Google Scholar 

  12. Vennepureddy, A., Thumallapallya, N., Nehrua, V.M., Atallahb, J.P., Terjanianb, T.: Drugs and Combination Therapies for the Treatment of Metastatic Melanoma. J. Clin. Med. Res. 8, 63–75 (2016)

    Article  Google Scholar 

  13. Murata, T.: Petri nets: properties, analysis and applications. Proc. IEEE 77, 541–580 (1989)

    Article  Google Scholar 

  14. Petri, C.A., Reisig, W.: Petri Net. Scholarpedia 3, 6477 (2008)

    Article  Google Scholar 

  15. Brown, K.S., Hill, C.C., Calero, G.A., Myers, C.R., Lee, K.H., Sethna, J.P., Cerione, R.A.: The statistical mechanics of complex signaling networks: nerve growth factor signaling. Phys. Biol. 1, 184–195 (2004)

    Article  Google Scholar 

  16. Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y., Hattori, M.: The KEGG resource for deciphering the genome. Nucleic Acids Res. 32, 277–280 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy

    Giulia Russo

  2. Department of Mathematics and Computer Science, University of Catania, Catania, Italy

    Marzio Pennisi

  3. University of Catania, Catania, Italy

    Roberta Boscarino

  4. Department of Drug Sciences, University of Catania, Catania, Italy

    Francesco Pappalardo

Authors
  1. Giulia Russo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marzio Pennisi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roberta Boscarino
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Francesco Pappalardo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Francesco Pappalardo .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan Carlos Figueroa-García

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Russo, G., Pennisi, M., Boscarino, R., Pappalardo, F. (2017). Modeling PI3K/PDK1/Akt and MAPK Signaling Pathways Using Continuous Petri Nets. In: Huang, DS., Jo, KH., Figueroa-García, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-63312-1_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63311-4

  • Online ISBN: 978-3-319-63312-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation