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Sfakianakis, N.

Publications and source records attributed to Sfakianakis, N..

6 recordsLinked to original sources

Modelling phenotypic plasticity in cancer invasion and metastasis: from microscopic interactions to macroscopic dynamics

Cancer progression is driven by the interplay between cancer cell phenotypic variability regulated by EMT/MET, and cell-cell and cell-matrix interactions. Starting with an individual-based model, in which every cell is characterised by its position, velocity and a continuously varying epithelial-mesenchymal phenotype, we derive, via a kinetic description and a mean-field limit, two alternative macroscopic formulations: an Euler-like system that couples mass and momentum to the phenotypic variable, and a single advection-aggregation-diffusion equation (AADE) for the cancer cell density. Both macroscopic models retain the non-local adhesion-repulsion forces, haptotactic response to an evolving ECM, and phenotype-dependent transition dynamics driven by TGF-{beta}. Numerical experiments in two spatial dimensions indicate that the macroscopic equations reproduce key scenarios obtained at the individual scale. In particular, a microscopic-macroscopic comparison shows that the AADE density reproduces acurately both the spatial localisation and the phenotypic decomposition of the individual cell population. We also demonstrate that varying only the steepness of the TGF-{beta} switch function, changes the EMT response from an almost binary epithelial-mesenchymal (EM) separation to a partial EM phenotypes. This study provides a systematic bridge from stochastic, heterogeneous cell dynamics to continuum descriptions, for investigating phenotype driven tumour invasion and supporting the choice of macroscopic models in large-scale simulations and analytical studies.

cancer biology↗

Force-Dependent Cell-Cell Adhesion Dynamics in a Stochastic Regime for Cancer Invasion

Cell-cell adhesion is a key regulator of cancer invasion. In this work, we extend a pre-existing individual based cancer invasion model by introducing a stochastic representation of N-cadherin-mediated adhesion, where the lifetime of a cell-cell bond depends on the pulling force acting on the bond. Using experimental data, we derive expressions for the mean and standard deviation of N-cadherin bond lifetimes and fit them to Gamma distributions, enabling their treatment as force-dependent random variables. These distributions are then used to modify the diffusion coefficient of mesenchymal cancer cells. The model predicts reduced random motility with increasing adhesion and incorporates a dynamic transition between catch- and slip-bond behaviour. Along with this model for cell motility, we propose a preliminary physical framework, that can be used to model pattern formation as a result of the new adhesion mechanic.

cancer biology↗

Modelling cancer cell dormancy in mice: statistical distributions predict reactivation times and survival dynamics

In predicting metastatic potential and improving treatment outcomes in cancer research, it is crucial that we understand the dynamics of cancer cell dormancy and reactivation. In this paper we propose, study, and evaluate a cancer growth model that incorporates cell death, dormancy, reactivation, and proliferation in the secondary sites. Using experimental data on mice, we test various statistical distributions and identify models that represent the asymmetry and variability observed in dormancy durations. Notably, the estimated cancer cell death rate remained consistent across all tested distributions, supporting its biological relevance as a robust parameter for modelling dormancy survival dynamics. The most suitable among the distributions we studied, the most suitable ones exhibit heavy-tails and asymmetric skewness; this aligns with the prolonged and rare dormancy periods expected of cancer cells. Our findings stress the importance of selecting appropriate statistical models for dormancy, both in predicting cancer cell reactivation events, and in informing therapeutic strategies that focus dormancy-driven metastasis.

cancer biology↗

A genuinely hybrid, multiscale 3D cancer invasion and metastasis modelling framework

We introduce in this paper substantial enhancements to a previously proposed hybrid multiscale cancer invasion modelling framework to better reflect the biological reality and dynamics of cancer. These model updates contribute to a more accurate representation of cancer dynamics, they provide deeper insights and enhance our predictive capabilities. Key updates include the integration of porous medium-like diffusion for the evolution of Epithelial-like Cancer Cells and other essential cellular constituents of the system, more realistic modelling of Epithelial-Mesenchymal Transition and Mesenchymal-Epithelial Transition models with the inclusion of Transforming Growth Factor beta within the tumour microenvironment, and the introduction of Compound Poisson Process in the Stochastic Differential Equations that describe the migration behaviour of the Mesenchymal-like Cancer Cells. Another innovative feature of the model is its extension into a multi-organ metastatic framework. This framework connects various organs through a circulatory network, enabling the study of how cancer cells spread to secondary sites.

cancer biology↗

Stochastic differential equation modelling of cancer cell migration and tissue invasion

Invasion of the surrounding tissue is a key aspect of cancer growth and spread involving a coordinated effort between cell migration and matrix degradation, and has been the subject of mathematical modelling for almost 30 years. In this current paper we address a long-standing question in the field of cancer cell migration modelling. Namely, identify the migratory pattern and spread of individual cancer cells, or small clusters of cancer cells, when the macroscopic evolution of the cancer cell colony is dictated by a specific partial differential equation (PDE). We show that the usual heuristic understanding of the diffusion and advection terms of the PDE being one-to-one responsible for the random and biased motion of the solitary cancer cells, respectively, is not precise. On the contrary, we show that the drift term of the correct stochastic differential equation (SDE) scheme that dictates the individual cancer cell migration, should account also for the divergence of the diffusion of the PDE. We support our claims with a number of numerical experiments and computational simulations.

biophysics↗

A novel 3D atomistic-continuum cancer invasion model: In silico simulations of an in vitro organotypic invasion assay

We develop a three-dimensional genuinely hybrid atomistic-continuum model that describes the invasive growth dynamics of individual cancer cells in tissue. The framework explicitly accounts for phenotypic variation by distinguishing between cancer cells of an epithelial-like and a mesenchymal-like phenotype. It also describes mutations between these cell phenotypes in the form of epithelial-mesenchymal transition (EMT) and its reverse process mesenchymal-epithelial transition (MET). The model consists of a hybrid system of partial and stochastic differential equations that describe the evolution of epithelial-like and mesenchymal-like cancer cells, respectively, under the consideration of matrix-degrading enzyme concentrations and the extracellular matrix density. With the help of inverse parameter estimation and a sensitivity analysis, this three-dimensional model is then calibrated to an in vitro organotypic invasion assay experiment of oral squamous cell carcinoma cells.

cancer biology↗