1. Proceedings
  2. I3M
  3. 2019
  4. MAS
  5. 10.46354/i3m.2019.mas.009

Simulation and analysis of a multi-scale tumor model using agent clustered network

  • a Ghazal Tashakor  , 
  • b Remo Suppi  
  • ab Department Computer Architecture & Operating Systems, School of Engineering, Campus Bellaterra, 
    Universitat Autònoma de Barcelona - Cerdanyola del Vallès, Barcelona, 08193, Spain
Cite as
Tashakor G., Suppi R. (2019). Simulation and analysis of a multi-scale tumor model using agent clustered network. Proceedings of the 18th International Conference on Modelling and Applied Simulation (MAS 2019), pp. 64-71. DOI: https://doi.org/10.46354/i3m.2019.mas.009
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Abstract

The increasing application of network models to translate and analysis of biological systems discusses the necessity of novel methodological and informatics insights for dealing with biological complexity. Today, using tools from graph theory to simulate the dynamical system and to understand the behavior of a biological network system such as tumor growth is unavoidable. Perhaps the most significant level of network analysis comes from mining the network measures and configuration data which allow us to explore deeper in multi-scale and multi-level biological models. This paper presents a graph agent-based tumor model which allows us to mine network measures and visualizing the evolving behavior of tumor in molecular and cellular levels. Besides, in this paper, we have applied three applicable techniques to discover and classify subgraphs in a growing network of agents for the use of a cluster computing system.

References

  1. Estrada E, Rodríguez-Velázquez JA. Subgraph centrality and clustering in complex hypernetworks. Physica A: Statistical Mechanics and its Applications. 2006 May 15;364:581-94.
  2. Rejniak KA, Anderson AR. Hybrid models of tumor growth. Wiley Interdisciplinary Reviews: Systems Biology and Medicine. 2011 Jan;3(1):115-25.
  3. Railsback SF, Grimm V. Agent-based and individualbased modeling: a practical introduction. Princeton university press; 2019 Mar 26.
  4. McGillivray P, Clarke D, Meyerson W, Zhang J, Lee D, Gu M, Kumar S, Zhou H, Gerstein M. Network analysis as a grand unifier in biomedical data science. Annual Review of Biomedical Data Science. 2018 Jul 20;1:153-80.
  5. Aittokallio T, Schwikowski B. Graph-based methods for analysing networks in cell biology. Briefings in bioinformatics. 2006 Sep 1;7(3):243-55. [Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982
  6. Grimm V, Revilla E, Berger U, Jeltsch F, Mooij WM, Railsback SF, Thulke HH, Weiner J, Wiegand T, DeAngelis DL. Pattern-oriented modeling of agent-based complex systems: lessons from
    ecology. science. 2005 Nov 11;310(5750):987-91.
  7. Tashakor G, Luque E, Suppi R. High Performance Computing for tumor Propagation Agent-based Model. In: XXIII Congreso Argentino de Ciencias de la Computación (La Plata, 2017)
  8. Tashakor G, SuppiR. : Agent-based model for tumoranalysis using Python+Mesa. In: European
    Modeling and Simulation Symposium, pp. 248-253, EMSS (2018)
  9. Masad D, Kazil J. MESA : an agent-based modeling framework. In: Proceedings of the 14th Python in Science Conference, pp. 53-60, SCIPY(2015)
  10. Aric A. Hagberg, Daniel A. Schult and Pieter J. Swart.: Exploring network structure, dynamics, and function using NetworkX, In: Proceedings of the 7th Python in Science Conference, pp. 11–15. SciPy (2008)
  11. Yang R, Bai Y, Qin Z, Yu T. EgoNet: identification of human disease ego-network modules. BMC
    genomics. 2014 Dec;15(1):314.
  12. Hu P, Niu Z, He T, Chan KC. Learning Deep Representations in Large Integrated Network for
    Graph Clustering. In2018 IEEE First International Conference on Artificial Intelligence and
    Knowledge Engineering (AIKE) 2018 Sep 26 (pp.101-105). IEEE.
  13. Shen R, Goonesekere NC, Guda C. Mining functional subgraphs from cancer protein-protein interaction networks. BMC systems biology. 2012 Dec;6(3):S2.
  14. Goodspeed A, Heiser LM, Gray JW, Costello JC. Tumor-derived cell lines as molecular models of
    cancer pharmacogenomics. Molecular Cancer Research. 2016 Jan 1;14(1):3-13.
  15. Cao L, Gorodetsky V, Mitkas PA. Agent mining: The synergy of agents and data mining. IEEE
    Intelligent Systems. 2009 May;24(3):64-72.
  16. Sugimori M, Hayakawa Y, Boman BM, Fields JZ, Awaji M, Kozano H, Tamura R, Yamamoto S,
    Ogata T, Yamada M, Endo S. Discovery of powerlaw growth in the self-renewal of heterogeneous glioma stem cell populations. PloS one. 2015 Aug 18;10(8):e0135760.
  17. Liu W, Pellegrini M, Wang X. Detecting communities based on network topology. Scientific reports. 2014 Jul 18;4:5739. Zhang W. Network informatics: A new science. Selforganizology. 2016;3(2):43-50.
  18. Klamt S, Haus UU, Theis F. Hypergraphs and cellular networks. PLoS computational biology. 2009 May 29;5(5):e1000385.
  19. Pavlopoulos GA, Secrier M, Moschopoulos CN, Soldatos TG, Kossida S, Aerts J, Schneider R,
    Bagos PG. Using graph theory to analyze biological networks. BioData mining. 2011 Dec;4(1):10.