A multi-scale agent-based model for avascular tumour growth

Sadhukhan, Sounak; Mishra, Pramod Kumar; Basu, Swapan; Mandal, J. K.

doi:10.1016/j.biosystems.2021.104450

Cited by 8 publications

(6 citation statements)

References 29 publications

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“…Neighboring cell pressure and a lack of available space are known to cause a decrease in tumour cell proliferation ( DiResta et al., 2005 ; Indana and Chaudhuri, 2021 ; Levayer, 2020 ). To account for this, previous agent-based models or individual based models have implemented force-balance equations to calculate neighboring cell pressures and determine the likelihood of proliferation ( Byrne and Drasdo, 2009 ; D’Antonio et al., 2013 ; Sadhukhan et al., 2021 ; Van Liedekerke et al., 2019 ). We chose to take advantage of PhysiCell’s inbuilt cell-cell pressure calculation to determine the mechanical pressure felt by an individual cell and then place a threshold on cell’s proliferating if under too much pressure.…”

Section: Methodsmentioning

confidence: 99%

Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy

Jenner

Smalley

Goldman³

et al. 2022

iScience

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Section: Methodsmentioning

confidence: 99%

Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy

Jenner

Smalley

Goldman³

et al. 2022

iScience

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“…The term mesoscale itself originated in meteorology as an intermediate scale between large-and small-scale systems. The nature of the tumour microenvironment can be studied at different scales at the same time; thus, many models are considered "multiscale" [110][111][112][113][114][115], as they consider, from molecules to cells to tissuelevel phenomena [116,117], how the extracellular matrix is altered [118,119], or an avascular tumour growth and cell model [120]. It is important to consider that any model should be able to reproduce data that have been observed through experiments [121] and, as such, models at different scales require validation at different scales as well [122].…”

Section: Model: Tomentioning

confidence: 99%

“…An interesting perspective to formulate models is to consider the cell as a basic unit, i.e., a virtual cell [124,125], with a set of rules for behaviour. The unit is sometimes called an "agent", with rules to proliferate, reproduce or transform depending on interactions with its external microenvironment [111] and probabilistic rules [126]. Different types of cells (tumour, immune or dendritic) constitute different agents [127].…”

Section: Model: Tomentioning

confidence: 99%

Modelling the Tumour Microenvironment, but What Exactly Do We Mean by “Model”?

Reyes-Aldasoro

2023

Cancers

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The Oxford English Dictionary includes 17 definitions for the word “model” as a noun and another 11 as a verb. Therefore, context is necessary to understand the meaning of the word model. For instance, “model railways” refer to replicas of railways and trains at a smaller scale and a “model student” refers to an exemplary individual. In some cases, a specific context, like cancer research, may not be sufficient to provide one specific meaning for model. Even if the context is narrowed, specifically, to research related to the tumour microenvironment, “model” can be understood in a wide variety of ways, from an animal model to a mathematical expression. This paper presents a review of different “models” of the tumour microenvironment, as grouped by different definitions of the word into four categories: model organisms, in vitro models, mathematical models and computational models. Then, the frequencies of different meanings of the word “model” related to the tumour microenvironment are measured from numbers of entries in the MEDLINE database of the United States National Library of Medicine at the National Institutes of Health. The frequencies of the main components of the microenvironment and the organ-related cancers modelled are also assessed quantitatively with specific keywords. Whilst animal models, particularly xenografts and mouse models, are the most commonly used “models”, the number of these entries has been slowly decreasing. Mathematical models, as well as prognostic and risk models, follow in frequency, and these have been growing in use.

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“…They modeled the biophysics characteristics of the cells through an individual force-based model whereas with the continuum part accounted for the diffusion of oxygen in the tissue. Recently, Sadhukhan et al [56] developed a forced-based hybrid model to study avascular tumor growth through protein/gen, cell and tissue scales. The diffusion of oxygen and nutrients was formulated by Fick’s law.…”

Section: Introductionmentioning

confidence: 99%

“…This is evidenced by the low rates of survival in the first five years since diagnosis (less than 5%) and the fact that the median survival of GBM patients is 6 to 16 months [26, 58], so some advances in biophysical modeling are needed. Such is the case of modeling and understanding the collective migration of cells [30, 23, 42], the biochemical and biophysics processes in the TME [56], the need to account for other phenotypes than the proliferative and migratory phenotypes in GBM or to analyze the consequences for invasion [3]. Furthermore, not many of the researches in this field have addressed the simulation of the coupling between the evolution conditions at tissue level and the evolution of the cell events so that more insight in this issue is needed [3].…”

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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A multi-scale agent-based model for avascular tumour growth

Cited by 8 publications

References 29 publications

Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy

Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy

Modelling the Tumour Microenvironment, but What Exactly Do We Mean by “Model”?

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

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