Mathematical Biology Tumor Growth

The paper presents a mathematical model to explore the relationship between tumor genetic instability and immune response, showing that increased mutational load and immune migration can lead to cancer clearance.

The paper introduces a geostatistical approach to visualize tumor heterogeneity and spatial patterns in the tumor microenvironment using agent-based models of ecological interactions.

The paper presents a multiscale mathematical model of avascular tumor growth in pancreatic cancer, simulating metabolic reprogramming and its effects on tumor dynamics.

This paper develops optimal chemotherapy strategies for AML by incorporating immune response into a stem cell model and applying optimal control theory.

This study uses ODE models to analyze immune-leukemia interactions in CML treatment, showing that qualitative criteria outperform quantitative ones in identifying biologically relevant models.

This study develops a multiscale model to simulate vascular tumor growth and drug response, revealing angiogenesis-induced drug resistance and predicting a synergistic drug combination targeting EGFR and VEGFR pathways.

This paper develops a mathematical model of tumour-immune interaction with distributed delays to study the impact of tumour-immune dynamics on disease progression and response to viral therapy.

ya||a is a GPU-accelerated agent-based model for morphogenesis that simulates mesenchyme and epithelium more efficiently than previous models.

The paper develops a Bayesian framework to quantify uncertainty in a glioblastoma treatment response metric, showing robustness across a range of cutoff values for predicting time to progression.

The paper uses computational models to study how macrophage polarization and cancer cell plasticity interact in the tumor microenvironment, showing that these interactions can lead to multiple stable states, including cancer cell extinction.

The paper presents a framework combining agent-based simulation and active learning to discover optimal cancer regression regions in immunotherapy models.

The paper develops a free boundary model to describe epithelial dynamics by deriving a continuum-limit PDE that accurately predicts cell-based model behavior with a mass-conserving boundary condition.

This paper introduces a high-throughput framework integrating PhysiCell and EMEWS to test cancer hypotheses through mechanistic simulations and optimize therapeutic strategies.

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.