Mathematical Biology Tumor Growth

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 paper develops mathematical biomarkers to predict treatment outcomes for adaptive therapy in cancer, showing personalized strategies outperform standard-of-care protocols.

This study models cancer cell dormancy in mice using statistical distributions to predict reactivation times and survival dynamics, finding heavy-tailed and asymmetric distributions best fit dormancy patterns.

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 study uses a Lotka-Volterra model to analyze tumor-host dynamics under constant and periodic perturbations, showing that periodic treatment can control aggressive tumors by shifting the system to a non-chaotic attractor.

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

This paper uses mathematical models to compare cancer-targeting and stroma-targeting ADCs, finding that stroma-targeting may be more effective in cases of heterogeneous antigen expression.

This study uses mathematical modeling to show that strong negative feedback leads to stable tissue growth, while weak feedback results in unlimited growth.

This study simulates how radiotherapy affects cancer stem cell evolution, showing increased resistance, proliferation, and CSC proportion, and highlights the importance of CSC-targeted treatment.

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

The paper develops a mathematical model to predict pancreatic cancer response to various therapies in mice, showing high accuracy in fitting and predicting tumor dynamics.

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.

This paper introduces a Bayesian framework to predict tumour evolution and drug resistance using longitudinal cancer data across three cancer types.

This study uses an agent-based model to show that increased chemoattractant secretion and reduced cell-cell adhesion promote cancer cell transition from spheroid to network phenotypes.

The paper uses simplified models to show that loss of cell-cell cohesion in LCIS cells leads to lobular localization, linking cohesion to spatial patterns in breast cancer.

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.

This study develops a mathematical model to simulate cancer cell responses to cyclic hypoxia and shows varying cell responses based on oxygen fluctuation dynamics.

This study uses an ODE model to show that neutrophil depletion enhances oncolytic vaccinia virus efficacy in melanoma by reducing viral clearance.

PhysiMeSS extends PhysiCell to model extracellular matrix fibers and their interactions, enabling more accurate simulations of cancer metastasis.

This paper develops a model integrating tumor-immune dynamics to predict treatment-free remission in CML patients using time-course data on NK cells and tumor load.

The paper develops a multiscale mathematical model of pancreatic adenocarcinoma evolution, showing that inflammatory processes and the RAS oncogene drive cancer progression from PanIN 1 to PanIN 2.

This study uses computational modeling to explore how spatial interactions between immune cells and collagen fibers influence tumor growth and immune infiltration.

This paper develops a systems-theoretic formalism to analyze tumor microenvironment (TME) dynamics and predict immune checkpoint inhibitor (ICI) response by identifying attractor transitions and subtype-specific therapies.

This paper optimizes combination immunotherapy regimens for glioblastoma using a virtual murine cohort and finds personalized treatment improves survival.