Tumor Growth Simulation Profiling

  • Claire Jean-Quartier
  • Fleur Jeanquartier
  • David Cemernek
  • Andreas Holzinger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9832)

Abstract

Cancer constitutes a condition and is referred to a group of numerous different diseases, that are characterized by uncontrolled cell growth. Tumors, in the broader sense, are described by abnormal cell growth and are not exclusively cancerous. The molecular basis involves a process of multiple steps and underlying signaling pathways, building up a complex biological framework. Cancer research is based on both disciplines of quantitative and life sciences which can be connected through Bioinformatics and Systems Biology. Our study aims to provide an enhanced computational model on tumor growth towards a comprehensive simulation of miscellaneous types of neoplasms. We create model profiles by considering data from selected types of tumors. Growth parameters are evaluated for integration and compared to the different disease examples.

Herein, we describe an extension to the recently presented visualization tool for tumor growth. The integration of profiles offers exemplary simulations on different types of tumors. The enhanced bio-computational simulation provides an approach to predicting tumor growth towards personalized medicine.

Keywords

Computational biology Cancer types Tumor growth Simulation HCI Visualization Systems biology Kinetics Data visualization 

References

  1. 1.
    Drake, J.W., Charlesworth, B., Charlesworth, D., Crow, J.F.: Rates of spontaneous mutation. Genetics 148(4), 1667–1686 (1998)Google Scholar
  2. 2.
    Lodish, H., Berk, A., Zipursky, S.L., et al.: Molecular Cell Biology, 4th edn. W. H. Freeman, New York (2000)Google Scholar
  3. 3.
    Hanahan, D., Weinberg, R.A.: Hallmarks of cancer. Cell 674(5), 646–646 (2011)CrossRefGoogle Scholar
  4. 4.
    Cortés, J., et al.: New approach to cancer therapy based on a molecularly defined cancer classification. CA: Cancer J. Clin. 64(1), 70–74 (2014)Google Scholar
  5. 5.
    Rodríguez-Enríquez, S., Pacheco-Velázquez, S.C., Gallardo-Pérez, J.C., Marn-Hernández, A., Aguilar-Ponce, J.L., Ruiz-García, E., Ruizgodoy-Rivera, L.M., Meneses-García, A., Moreno-Sánchez, R.: Multi-biomarker pattern for tumor identification and prognosis. J. Cell Biochem. 112(10), 2703–15 (2011)CrossRefGoogle Scholar
  6. 6.
    Wang, Y., Tetko, I.V., Hall, M.A., Frank, E., Facius, A., Mayer, K.F., Mewes, H.W.: Gene selection from microarray data for cancer classification-a machine learning approach. Comput. Biol Chem. 29(1), 37–46 (2005)CrossRefMATHGoogle Scholar
  7. 7.
    Vickers, A.J.: Prediction models in cancer care. CA: Cancer J. Clin. 61(5), 315–326 (2011). doi:10.3322/caac.20118 Google Scholar
  8. 8.
    Li, X.L., Oduola, W.O., Qian, L., Dougherty, E.R.: Integrating multiscale modeling with drug effects for cancer treatment. Cancer Inform. 14(Suppl. 5), 21–31 (2016)CrossRefGoogle Scholar
  9. 9.
    Enderling, H., Rejniak, K.A.: Simulating cancer: computational models in oncology. Front Oncol. 3, 233 (2013)Google Scholar
  10. 10.
    Edelman, L.B., Eddy, J.A., Price, N.D.: In silico models of cancer. Wiley Interdisc. Rev. Syst. Biol. Med. 2(4), 438–459 (2010)CrossRefGoogle Scholar
  11. 11.
    Benzekry, S., Lamont, C., Beheshti, A., Tracz, A., Ebos, J.M., Hlatky, L., Hahnfeldt, P.: Classical mathematical models for description and prediction of experimental tumor growth. PLoS Comput. Biol. 10(8), e1003800 (2014)CrossRefGoogle Scholar
  12. 12.
    Rejniak, K.A., Anderson, A.R.A.: Hybrid models of tumor growth. Wiley Interdiscip Rev. Syst. Biol. Med. 3(1), 115–125 (2011)CrossRefGoogle Scholar
  13. 13.
    Jeanquartier, F., Jean-Quartier, C., Cemernek, D., Holzinger, A.: In Silico Modeling For Tumor Growth Visualization. - Manuscript in revision (2016). https://github.com/davcem/cpm-cytoscape/
  14. 14.
    Graner, F., Glazier, J.A.: Simulation of biological cell sorting using a two-dimensional extended Potts model. Phys. Rev. Lett. 69, 2013–2016 (1992)CrossRefGoogle Scholar
  15. 15.
    Szab, A., Merks, R.M.: Cellular potts modeling of tumor growth, tumor invasion, and tumor evolution. Frontiers in oncology 3 (2013)Google Scholar
  16. 16.
    Giverso, C., Scianna, M., Preziosi, L., Lo Buono, N., Funaro, A.: Individual cell-based model for in-vitro mesothelial invasion of ovarian cancer. Math. Model. Nat. Phenom. 5(1), 203–223 (2010)MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Osborne, J.M.: Multiscale model of colorectal cancer using the cellular potts framework. Cancer Inform. 14(Suppl. 4), 83–93 (2015)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Rubenstein, B.M., Kaufman, L.J.: The role of extracellular matrix in glioma invasion: a cellular potts model approach. Biophys J. 95(12), 5661–5680 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Claire Jean-Quartier
    • 1
  • Fleur Jeanquartier
    • 1
  • David Cemernek
    • 1
  • Andreas Holzinger
    • 1
  1. 1.Holzinger Group, Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical Informatics, Statistics and DocumentationMedical University of GrazGrazAustria

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