Mathematical Biology Tumor Growth

This study enhances a personalized cancer treatment strategy by incorporating both irreversible and reversible drug resistance mechanisms, showing improved patient outcomes compared to current approaches.

This study uses a computational model to show how tumor-associated collagen signatures influence immune escape and survival in breast cancer.

This paper develops a stochastic model to assess the identifiability of phenotypic adaptation parameters from low-cell-count experiments and shows that cell-to-cell heterogeneity is non-identifiable from population-level data.

Start&Stop enables interactive simulation control in PhysiCell and PhysiBoSS 2.0 by allowing parameter adjustments during simulations.

This study models tumor growth and macrophage dynamics in a multiphase system, showing how macrophage heterogeneity affects tumor progression and proposing an immunotherapeutic strategy.

This study develops a spatiotemporal model to simulate CAR T-cell therapy in solid tumors, showing that local administration is most effective against slow-growing, highly diffusive tumors with low cell density.

The paper optimizes vaccine and anti-PD1 combination therapy for melanoma by using a mathematical model and optimization algorithm to determine the best treatment protocol.

This paper introduces a stochastic modelling framework for cancer progression that integrates tumour growth, metastasis, and survival, using clinical data to analyze patient trajectories.

This study uses mathematical modeling to show that optimal cancer treatment involves intermittent dosing with lower drug concentrations and recovery periods, potentially improving efficacy over continuous regimens.

This study develops mathematical models of contrasting Notch signaling pathways in multiple cancers to identify optimal targeted therapies that vary by cancer stage.

The paper develops a mathematical model of glioblastoma growth and immune interactions, showing that targeting TAMs to enhance their antitumoral properties could significantly improve treatment outcomes.

The paper quantifies phenotypic heterogeneity in glioblastoma cells' response to temozolomide, showing non-genetic factors drive differential drug response.

The paper compares in vitro endothelial cell network formation with three computational models to validate which model best captures the dynamics of network remodelling.

This study uses a multiscale mathematical model to explore how EMT influences ovarian cancer progression in two cell lines, finding that EMT and cell cycle parameters significantly affect tumor dynamics.

This paper develops a computational model to study how protrusion force and cell-cell adhesion influence collective migration modes in microtumors under hypoxia.

This paper develops a quantitative cancer-immunity cycle model to predict disease progression and survival in advanced metastatic colorectal cancer by integrating clinical data and virtual patient technology.

This study uses a hybrid computational model to show that increased ECM stiffness inhibits invasion in aggressive cancer cells but not in non-invasive ones.

This study develops mathematical models to explore how chemotherapy and immune checkpoint blockade (ICB) combination therapy affects heterogeneous tumors, revealing chemotherapy's role in reducing antigenic heterogeneity and enhancing ICB efficacy through immune recovery.

This paper introduces a stochastic model to study cancer immune escape dynamics, showing how immunomodulation in the tumor microenvironment affects disease recurrence timing in bladder and breast cancer.

This study uses a mathematical model to show that ploidy and resource availability in the tumor microenvironment influence glioblastoma initiation and recurrence.

This study uses mathematical models to show that spatial factors influence drug response and competition between drug-sensitive and drug-resistant cancer cells.

This study uses a continuum-level model to show that combining radiation with anti-VEGF and chemotherapy improves tumor suppression in growing vascular tumors.

This study shows that explicitly modeling immune cell-cancer cell conjugates in mathematical models changes the time scale and outcomes of tumor control dynamics.

This paper develops a mathematical model of oncolytic virus therapy in zebrafish to capture tumor dynamics and inform future cancer treatments.

This paper uses mathematical modeling to show that macrophage-driven protection against chemotherapy arises from spatially structured protective niches, not just biochemical degradation.

The paper develops a mathematical model to study CTC adhesion in a microfluidic system, revealing the impact of fluid velocity and proteins on cell behavior.

The paper develops a multiscale mathematical model of immune surveillance in micrometastases to simulate patient trajectories and identify key parameters affecting immunosurveillance outcomes.

This paper compares continuous and intermittent adaptive chemotherapy dosing using mathematical models, finding continuous adaptive therapy more robust and effective in delaying tumor growth.

The paper models how cancer-associated fibroblasts in lung tumors create barriers that exclude T-cells, affecting tumor growth and immune response outcomes.

This paper introduces a stochastic agent-based model to study how cancer cell dormancy affects therapy success, showing that even small dormant cell populations can lead to treatment failure.