Identification of intrinsic in vitro cellular mechanisms for glioma invasion

Tektonidis, Marco; Hatzikirou, Haralampos; Chauvière, Arnaud; Simon, Matthias; Schaller, Karl; Deutsch, Andreas

doi:10.1016/j.jtbi.2011.07.012

Cited by 87 publications

(95 citation statements)

References 45 publications

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“…Recent work by Deutsch and co-workers attempt to infer the largescale behavior of LGCA for multicellular organization to better understand the large scale effect of the microscopic (cell scale) LGCA rules [99,100,102].…”

Section: Resultsmentioning

confidence: 99%

“…In the simulations of fluids, the velocity channels represented particle collisions. Deutsch and co-workers extended the framework to model growth and migration of cells in multi-cellular environments [46,69,99,100]. In our description we very closely follow the line of argument presented by Hatzikirou and Deutsch in [101], which we consider as an excellent presentation addressing the underlying concepts of LGCA for modeling of tissue organization and growth.…”

Section: Lattice Gas Cellular Automata (Type D)mentioning

confidence: 96%

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Simulating tissue mechanics with agent-based models: concepts, perspectives and some novel results

et al. 2015

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In this paper we present an overview of agentbased models that are used to simulate mechanical and physiological phenomena in cells and tissues, and we discuss underlying concepts, limitations, and future perspectives of these models. As the interest in cell and tissue mechanics increase, agent-based models are becoming more common the modeling community. We overview the physical aspects, complexity, shortcomings, and capabilities of the major agent-based model categories: lattice-based models (cellular automata, lattice gas cellular automata, cellular Potts models), off-lattice models (center-based models, deformable cell models, vertex models), and hybrid discrete-continuum models. In this way, we hope to assist future researchers in choosing a model for the phenomenon they want to model and understand. The article also contains some novel results.

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Section: Resultsmentioning

confidence: 99%

Section: Lattice Gas Cellular Automata (Type D)mentioning

confidence: 96%

Simulating tissue mechanics with agent-based models: concepts, perspectives and some novel results

et al. 2015

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“…Remark that our method for evaluating R core and R inv has already been validated in [22] for a particular data set and, for simplicity reason, we maintain the values of θ core and θ inv used in this previous study. Once the two radii are estimated, we derive the following quantities of interest: 1) the width of the invasive zone, defined as the difference between the core and invasive radii…”

Section: Simulation Procedures and Analysismentioning

confidence: 99%

“…Their model shows that the density-dependent "Go-or-Grow" mechanism can produce complex dynamics associated with tumor heterogeneity during invasion. In another recent study, Tektonidis et al [22] compared simulation results from a lattice-gas cellular automaton model to in vitro glioma invasion data. They identify the migration/proliferation dichotomy as a central mechanism in glioma invasion to explain the experimental features of the glioma's infiltrative zone.…”

Section: Introductionmentioning

confidence: 99%

“…We present a model that incorporates specific tumor cell dynamics and analyze in detail the interplay of proliferation and migration processes. We investigate the spatiotemporal dynamics of two cell populations with different migratory and proliferative abilities and model the "Go-or-Grow" mechanism as cells switching between mutually exclusive migratory and proliferative phenotypes, as proposed by the authors in [22]. The central aspect of our study is the characterization and the quantification of the invasive tumor front dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of the Migration/Proliferation Dichotomy and its Impact on Avascular Glioma Invasion

Böttger

Hatzikirou

Chauvière

et al. 2012

Math. Model. Nat. Phenom.

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Abstract. Gliomas are highly invasive brain tumors that exhibit high and spatially heterogeneous cell proliferation and motility rates. The interplay of proliferation and migration dynamics plays an important role in the invasion of these malignant tumors. We analyze the regulation of proliferation and migration processes with a lattice-gas cellular automaton (LGCA). We study and characterize the influence of the migration/proliferation dichotomy (also known as the "Go-or-Grow" mechanism) on avascular glioma invasion, in terms of invasion speed and width of the infiltration zone. We show that the invasive behavior of the (macroscopic) tumor colony is a highly complex phenomenon that cannot be extrapolated by the sole knowledge of the (microscopic) individual cell phenotype.

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An adaptive semi‐implicit finite element solver for brain cancer progression modeling

Tzirakis

Papanikas

Sakkalis

et al. 2023

Numer Methods Biomed Eng

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Glioblastoma is the most aggressive and infiltrative glioma, classified as Grade IV, with the poorest survival rate among patients. Accurate and rigorously tested mechanistic in silico modeling offers great value to understand and quantify the progression of primary brain tumors. This paper presents a continuum‐based finite element framework that is built on high performance computing, open‐source libraries to simulate glioblastoma progression. We adopt the established proliferation invasion hypoxia necrosis angiogenesis model in our framework to realize scalable simulations of cancer, and has demonstrated to produce accurate and efficient solutions in both two‐ and three‐dimensional brain models. The in silico solver can successfully implement arbitrary order discretization schemes and adaptive remeshing algorithms. A model sensitivity analysis is conducted to test the impact of vascular density, cancer cell invasiveness and aggressiveness, the phenotypic transition potential, including that of necrosis, and the effect of tumor‐induced angiogenesis in the evolution of glioblastoma. Additionally, individualized simulations of brain cancer progression are carried out using pertinent magnetic resonance imaging data, where the in silico model is used to investigate the complex dynamics of the disease. We conclude by arguing how the proposed framework can deliver patient‐specific simulations of cancer prognosis and how it could bridge clinical imaging with modeling.

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Identification of intrinsic in vitro cellular mechanisms for glioma invasion

Cited by 87 publications

References 45 publications

Simulating tissue mechanics with agent-based models: concepts, perspectives and some novel results

Simulating tissue mechanics with agent-based models: concepts, perspectives and some novel results

Investigation of the Migration/Proliferation Dichotomy and its Impact on Avascular Glioma Invasion

An adaptive semi‐implicit finite element solver for brain cancer progression modeling

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