A multi-layered hybrid model for cancer cell invasion

Sadhukhan, Sounak; Mishra, Pramod Kumar

doi:10.1007/s11517-022-02514-2

Cited by 4 publications

(3 citation statements)

References 74 publications

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“…Models of tumor growth and metastatic spread are critical for understanding the key underlying biological processes and clinical evolution in patients. Mathematical models can be of different level of detail, computational or theoretical, spatial or non-spatial in nature, and can have several mechanisms explicit or implicitly embedded in them, including interaction with resources, biomechanical signals, cellular competition, mutation and migration (Chaplain, 2020; Franssen et al, 2021; Macnamara et al, 2020; Opasic et al, 2020; Sadhukhan & Mishra, 2022; Waclaw et al, 2015). While theoretical and analytical advances remain crucial in mathematical models of cancer, computational approaches that offer direct simulation platforms for efficient numerical exploration, focused study and hypothesis testing are also very much needed.…”

Section: Statement Of Needmentioning

confidence: 99%

MetaSpread: A cancer growth and metastatic spread simulation program in Python

Hernández-Inostroza,

Gjini

2024

Preprint

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We develop and provide MetaSpread, an open-source simulation package and interactive program in Python for tumor growth and metastatic spread, based on a mathematical model by Franssen et al. (2019 ). This paper proposed a hybrid modeling and computational framework where cellular growth and metastatic spread are described and simulated in a spatially explicit manner, accounting for stochastic individual cell dynamics and deterministic dynamics of abiotic factors. This model incorporates several key processes such as the growth and movement of epithelial and mesenchymal cells, the role of the extracellular matrix, diffusion, haptotaxis, circulation and survival of cancer cells in the vasculature, and seeding and growth in secondary sites. In the software that we develop, these growth and metastatic dynamics are programmed using MESA, a Python Package for Agent-based modeling (Masad & Kazil, 2015).

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Section: Statement Of Needmentioning

confidence: 99%

MetaSpread: A cancer growth and metastatic spread simulation program in Python

Hernández-Inostroza,

Gjini

2024

Preprint

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“…The diffusion of oxygen and nutrients was formulated by Fick's law. Sadhukhan and Mishra [55] enlarged this model to study cell heterogeneity produced by phenotypic changes during avascular tumor growth.…”

Section: Discrete and Hybrid Modelsmentioning

confidence: 99%

“…The diffusion of oxygen and nutrients was formulated by Fick’s law. Sadhukhan and Mishra [55] enlarged this model to study cell heterogeneity produced by phenotypic changes during avascular tumor growth. In both studies, the authors considered five possible phenotypes at cell scale: hypoxic, proliferative, apoptotic, necrotic and quiescent.…”

Section: Introductionmentioning

confidence: 99%

A simple agent-based hybrid model to simulate the biophysics of glioblastoma multiforme cells and the concomitant evolution of the oxygen field

Saucedo-Mora,

Sanz,

Montáns

et al. 2023

Preprint

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Background and objectives: Glioblastoma multiforme (GBM) is one of the most aggressive cancers of the central nervous system. It is characterized by a high mitotic activity and an infiltrative ability of the glioma cells, neovascularization and necrosis. GBM evolution entails the continuous interplay between heterogeneous cell populations, chemotaxis, and physical cues through different scales. In this work, an agent-based hybrid model is proposed to simulate the coupling of the multiscale biological events involved in the GBM invasion, specifically the individual and collective migration of GBM cells and the concurrent evolution of the oxygen field and phenotypic plasticity. An asset of the formulation is that it is conceptually and computationally simple but allows to reproduce the complexity and the progression of the GBM micro-environment at cell and tissue scales simultaneously. Methods: The migration is reproduced as the result of the interaction between every single cell and its micro-environment. The behavior of each individual cell is formulated through genotypic variables whereas the cell micro-environment is modeled in terms of the oxygen concentration and the cell density surrounding each cell. The collective behavior is formulated at a cellular scale through a flocking model. The phenotypic plasticity of the cells is induced by the micro-environment conditions, considering five phenotypes. Results: The model has been contrasted by benchmark problems and experimental tests showing the ability to reproduce different scenarios of glioma cell migration. In all cases, the individual and collective cell migration and the coupled evolution of both the oxygen field and phenotypic plasticity have been properly simulated. This simple formulation allows to mimic the formation of relevant hallmarks of glioblastoma multiforme, such as the necrotic cores, and to reproduce experimental evidences related to the mitotic activity in pseudopalisades. Conclusions: In the collective migration, the survival of the clusters prevails at the expense of cell mitosis, regardless of the size of the groups, which delays the formation of necrotic foci and reduces the rate of oxygen consumption.

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