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A cellular automata model of chemotherapy effects on tumour growth: targeting cancer and immune cells

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  • Fateme Pourhasanzade
  • S. H. Sabzpoushan

Abstract

The effects of therapy on avascular cancer development based on a stochastic cellular automata model are considered. Making the model more compatible with the biology of cancer, the following features are implemented: intrinsic resistance of cancerous cells along with drug-induced resistance, drug-sensitive cells, immune system. Results are reported for no treatment, discontinued treatment after only one cycle of chemotherapy, and periodic drug administration therapy modes. Growth fraction, necrotic fraction, and tumour volume are used as output parameters beside a 2-D graphical growth presentation. Periodic drug administration is more effective to inhibit the growth of tumours. The model has been validated by the verification of the simulation results using in vivo literature data. Considering immune cells makes the model more compatible with the biological realities. Beside targeting cancer cells, the model can also simulate the activation of the immune system to fight against cancer.Abbreviations CA: cellular automata; DSC: drug sensitive cell; DRC: drug resistant cell; GF: growth fraction; NF: necrotic fraction; ODE: ordinary differential equation; PDE: partial differential equation; SCAM: The proposed stochastic cellular automata model

Suggested Citation

  • Fateme Pourhasanzade & S. H. Sabzpoushan, 2019. "A cellular automata model of chemotherapy effects on tumour growth: targeting cancer and immune cells," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 25(1), pages 63-89, January.
    • Handle: RePEc:taf:nmcmxx:v:25:y:2019:i:1:p:63-89
    DOI: 10.1080/13873954.2019.1571515
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    Cited by:

    1. Moghari, Somaye & Ghorani, Maryam, 2022. "A symbiosis between cellular automata and dynamic weighted multigraph with application on virus spread modeling," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).

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