Mathematical Biology Tumor Growth

This paper applies optimal control theory to design combination therapies for acute myeloid leukaemia by analyzing control dynamics and pay-off functions.

The paper develops a mathematical model to explain immune recruitment and drug resistance in metronomic chemotherapy using a mouse glioma model and validates it across different drug regimens.

GLUECK, a neural network model, learns cancer growth curves from clinical datasets without prior knowledge, outperforming traditional tumor growth models.

The paper introduces a Machine Learning model called PRINCESS to predict individual breast cancer evolution to surgical size, showing superior accuracy compared to existing models.

CHIMERA combines mechanistic models and machine learning to optimize chemotherapy and surgery sequencing in breast cancer by learning tumor growth and drug pharmacokinetics.

The paper compares IC50 values in monolayer and spheroid cultures to identify conditions affecting drug efficacy and toxicity.

The paper uses an agent-based model to show that glioma cell morphology and density influence self-organization patterns like flocks and streams, with higher density reducing flocks and increasing streams, linking these patterns to tumor malignancy.

This study uses a hybrid agent-based model to analyze how drug resistance develops in tumor cells through spatio-temporal lineage tracking and identifies adaptation mechanisms in microenvironmental niches.

The paper investigates how nutrient limitations affect invasion in cancer populations, showing that phenotypic imbalance accelerates invasion while balance decelerates it.

The paper develops a mathematical model of early angiogenesis to explore the role of Notch signaling and mechanical cues in vessel branching, anastomosis, and vascular dynamics.

The paper develops a personalized tumor growth model using multiscale simulation and Bayesian methods, showing promising predictions with low error margins on A549 cell line data.

This paper introduces a machine learning model that learns tumor growth patterns and volume prediction from breast cancer data without supervision, outperforming existing models on clinical datasets.

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 uses mathematical models and computational optimization to design effective cancer treatment protocols that leverage hypoxia in pancreatic and melanoma cancers.

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 applies optimal control theory to design personalized cancer therapies, showing that individual pharmacokinetic differences significantly impact treatment outcomes.

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

The paper develops a multi-scale framework integrating cancer pathways to predict patient-specific treatment responses in prostate, lung, and kidney cancers.

The paper develops microPKPD models to assess patient-specific drug efficacy by simulating drug delivery challenges in tumor microenvironments.

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.

The paper presents a stochastic spatial model to simulate tumor growth with two cell phenotypes, showing it can reproduce Gompertz and bi-phasic growth dynamics.

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.

The paper introduces a novel averaging method for random ordinary differential equations and applies it to study how intratumoral heterogeneity affects tumor growth dynamics.

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

The paper models fractional killing in cancer cells as a stochastic process, showing that dynamic variability in apoptotic thresholds can enhance cellular killing by reducing the mean first-passage time for p53 to reach the threshold level.

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.

This paper introduces a modelling framework that links genotype, signalling networks, and cell behaviour in a 3D microenvironment to predict single- and multi-cellular dynamics.