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Gerisch, A.

Publications and source records attributed to Gerisch, A..

2 recordsLinked to original sources

Mathematical Modeling of Cancer Cell Invasion of Tissue: Development of an in silico Organotypic Assay

BackgroundOrganotypic assays are three-dimensional in vitro models widely used in cancer research to mimic the in vivo extracellular matrix (ECM) and to study cancer cell invasion by allowing investigation of critical interactions between tumor cells and their microenvironment. During invasion, cancer cells undergo genetic and epigenetic changes that disrupt cell-cell adhesion, enabling detachment from the primary tumor. Subsequently, invasive cells must (i) breach the basement membrane, a dense protein meshwork that restricts cell movement, and (ii) migrate through the surrounding ECM. This process relies on proteolytic enzyme secretion to degrade structural barriers, followed by cell-matrix adhesion-mediated migration. MethodsWe present an in silico model of cancer cell invasion in organotypic assays. The model is formulated as a system of partial differential equations capturing spatiotemporal dynamics, with nonlocal terms representing preferential adhesion-driven movement. Key variables in the model are cancer cells, proteolytic enzymes, and the ECM. ResultsComputational simulations demonstrate that modulation of cell-cell and cell-matrix adhesion parameters significantly influences tumor invasiveness. This result is consistent with experimental observations, demonstrating the models ability to accurately reflect in vitro behavior.

biophysics↗

A space-jump derivation for non-local models of cell-cell adhesion and non-local chemotaxis

Cellular adhesion provides one of the fundamental forms of biological interaction between cells and their surroundings, yet the continuum modelling of cellular adhesion has remained mathematically challenging. In 2006, Armstrong et al. proposed a mathematical model in the form of an integro-partial differential equation. Although successful in applications, a derivation from an underlying stochastic random walk has remained elusive. In this work we develop a framework by which non-local models can be derived from a space-jump process. We show how the notions of motility and a cell polarization vector can be naturally included. With this derivation we are able to include microscopic biological properties into the model. We show that particular choices yield the original Armstrong model, while others lead to more general models, including a doubly non-local adhesion model and non-local chemotaxis models. Finally, we use random walk simulations to confirm that the corresponding continuum model represents the mean field behaviour of the stochastic random walk.

systems biology↗