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A new method for shrinking tumor based on microenvironmental factors: Introducing a stochastic agent-based model of avascular tumor growth

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  • Sabzpoushan, S.H.
  • Pourhasanzade, Fateme

Abstract

In this paper, by using cellular automata formalism, a two-dimensional stochastic agent-based model for avascular tumor growth is presented. The model is based on biological assumptions, physical structure, agents and their states, and transition rules. The interaction between immune and cancer cells is probabilistic. The immune cell recruitment, which usually occurs after the detection of tumor cells, is considered. The insights gain in the proposed work to model the immune-tumor interaction is of great importance to control the tumor growth via micro-environment factors. The parameters used in the present model are in compatible with cancer biology using in vivo experimental data. The results show that the proposed model not only is able to simulate the tumor growth graphically, but also the in vivo tumor growth quantitatively and qualitatively. In this paper, by introducing a new concept; critical point, a new idea is proposed to shrink the tumor or slow down its growth rate. The tumor will grow slowly if the division probability of the proliferative tumor cells depends on the microenvironmental conditions. The proposed idea has been validated using an in silico simulation.

Suggested Citation

  • Sabzpoushan, S.H. & Pourhasanzade, Fateme, 2018. "A new method for shrinking tumor based on microenvironmental factors: Introducing a stochastic agent-based model of avascular tumor growth," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 771-787.
    • Handle: RePEc:eee:phsmap:v:508:y:2018:i:c:p:771-787
    DOI: 10.1016/j.physa.2018.05.131
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    References listed on IDEAS

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    1. Tina Schuetz & Stefan Becker & Andreas Mang & Alina Toma & Thorsten Buzug, 2013. "Modelling of glioblastoma growth by linking a molecular interaction network with an agent-based model," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 19(5), pages 417-433.
    2. Züleyha, Artuç & Ziya, Merdan & Selçuk, Yeşiltaş & Kemal, Öztürk M. & Mesut, Tez, 2017. "Simulation of glioblastoma multiforme (GBM) tumor cells using ising model on the Creutz Cellular Automaton," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 901-907.
    3. Charalambos Loizides & Demetris Iacovides & Marios M Hadjiandreou & Gizem Rizki & Achilleas Achilleos & Katerina Strati & Georgios D Mitsis, 2015. "Model-Based Tumor Growth Dynamics and Therapy Response in a Mouse Model of De Novo Carcinogenesis," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-18, December.
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