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The double-edged sword role of fibroblasts in the interaction with cancer cells; an agent-based modeling approach

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  • Zarifeh Heidary
  • Jafar Ghaisari
  • Shiva Moein
  • Shaghayegh Haghjooy Javanmard

Abstract

Fibroblasts as key components of tumor microenvironment show different features in the interaction with cancer cells. Although, Normal fibroblasts demonstrate anti-tumor effects, cancer associated fibroblasts are principal participant in tumor growth and invasion. The ambiguity of fibroblasts function can be regarded as two heads of its behavioral spectrum and can be subjected for mathematical modeling to identify their switching behavior. In this research, an agent-based model of mutual interactions between fibroblast and cancer cell was created. The proposed model is based on nonlinear differential equations which describes biochemical reactions of the main factors involved in fibroblasts and cancer cells communication. Also, most of the model parameters are estimated using hybrid unscented Kalman filter. The interactions between two cell types are illustrated by the dynamic modeling of TGFβ and LIF pathways as well as their crosstalk. Using analytical and computational approaches, reciprocal effects of cancer cells and fibroblasts are constructed and the role of signaling molecules in tumor progression or prevention are determined. Finally, the model is validated using a set of experimental data. The proposed dynamic modeling might be useful for designing more efficient therapies in cancer metastasis treatment and prevention.

Suggested Citation

  • Zarifeh Heidary & Jafar Ghaisari & Shiva Moein & Shaghayegh Haghjooy Javanmard, 2020. "The double-edged sword role of fibroblasts in the interaction with cancer cells; an agent-based modeling approach," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-14, May.
    • Handle: RePEc:plo:pone00:0232965
    DOI: 10.1371/journal.pone.0232965
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    References listed on IDEAS

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    1. Gabriele Lillacci & Mustafa Khammash, 2010. "Parameter Estimation and Model Selection in Computational Biology," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-17, March.
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