Mathematical Biology Tumor Growth

PhysiBoSS integrates multi-scale agent-based modelling with Boolean networks to study cell population dynamics and treatment responses in 2D and 3D environments.

This paper develops a multi-state model to analyze chemoresistance dynamics in breast cancer using experimental and mathematical modeling approaches.

The paper uses a computational model to identify tumor growth kinetic parameters as prognostic biomarkers for anti-angiogenic treatment outcomes.

Open source off-lattice agent-based C++ simulator PhysiCell, coupled to the BioFVM diffusion solver, simulates 3-D multicellular tissues with cost linear in cell number, reaching roughly 10^6 cells.

The paper models clonal dominance in tumor cells by showing that varying division rates, not stem cell properties or random growth, lead to clonal dominance in vitro.

This study uses computational modeling to show that tumor growth kinetics influence the response to anti-VEGF treatment, with nonlinear dependence on VEGF receptor expression.

The paper studies radially-symmetric anisotropic diffusion to model aggregation phenomena in biological systems without relying on chemotaxis or adhesion.

This paper derives non-local models of cell-cell adhesion and chemotaxis from a space-jump process and validates them with random walk simulations.

This review discusses mathematical and computational models addressing challenges in cancer immunotherapy, including tumor classification, treatment scheduling, and combination therapy design.