Mathematical Biology Tumor Growth

The paper introduces a mathematical model of the cancer-immunity cycle to optimize chemotherapy and immunotherapy dosing schedules by synchronizing them with the cycle's period, showing that optimized timing can improve efficacy with lower doses.

This study uses a lattice-gas cellular automaton model to show inhibitory feedback from differentiated cells affects stem cell invasive potential and local fluctuations create slow-moving unstable fronts.

The paper develops a model for non-symmetrical multilayer cell growth on a solid plate, using ordinary differential equations to describe variable contact inhibition and colony formation kinetics.

This paper presents a community-driven benchmarking initiative for cell-based multiscale models, highlighting variations in tool implementations and advocating for standardized benchmarks and interoperability.

This paper develops deterministic and stochastic models to analyze the design and stability of adaptive therapy cycles for cancer treatment.

This paper uses spatial statistics from single time-point images to classify game-theoretic interactions between drug-sensitive and resistant cancer cells using an agent-based model.

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 develops a 3D in-silico model to investigate how omental adipocytes influence ovarian cancer progression by simulating the effects of adipose media concentration, treatment dosage, and initial tumour size.

The paper identifies optimal radiotherapy schedules to reduce metastasis by combining mechanistic modeling and machine learning with in silico clinical trials.

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

This study uses a spatial agent-based model to show that persister cells reduce treatment efficacy in adaptive cancer therapy by providing a reservoir for genetic resistance.

The paper uses a multiscale model to show that mesenchymally transitioned tumor niches outcompete parental niches under specific adhesion and ECM conditions.

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 paper uses Q-learning in a Markov tumor model to optimize chemotherapy dose scheduling, identifying evolutionary paths, robustness, and simplified policies for balanced tumor cell coexistence.

This study uses a mathematical model to explore combination CAR T-cell treatments for glioblastoma, showing that early intervention and spatially targeted delivery are effective strategies.

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 an in silico model of cancer cell invasion in organotypic assays, showing that adhesion parameters affect tumor invasiveness.

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 paper develops an agent-based model to simulate radiotherapy effects on Stage III sigmoid colon cancer, revealing tumor volume reduction, resistant subclone emergence, and immune infiltration variability.

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.