Mathematical Biology Tumor Growth

Assessing the Operational Feasibility of Evolutionary Therapy in Metastatic Non-Small Cell Lung Cancer

This study evaluates the clinical feasibility of evolutionary therapy for NSCLC by simulating its performance under real-world constraints like measurement error and appointment delays.

Linking clinical border morphology to cell-scale growth mechanisms in melanoma: a segmentation-to-simulation alignment study

Across 1,581 ISIC 2018 lesions, melanoma borders differ from nevi mainly by coarse lobulation rather than fractal dimension; of six simulated mechanisms, only an imposed long-range growth-rate field reproduces this.

Modeling a Continuum of Drug-induced Persistence during Targeted Cancer Therapy

The paper develops a framework to model drug-induced persistence in cancer therapy, identifying optimal drug concentrations and schedules to reduce persistence-based resistance.

Functional heterogeneity, tumor vascularization, and the nonlinear dynamics of metastatic cancer treated by oncolytic viral therapy

This paper models the dynamics of oncolytic viral therapy in primary and metastatic tumors, showing that immune modulation can paradoxically promote distant metastasis.

Persister cells and their effects on cancer stabilization therapy

This study develops an evolutionary game theory model to analyze how persister cells influence cancer dynamics under treatment, showing they can stabilize or destabilize tumors based on transition rates and competition.

SHAPPEing agent-based models to spatial data: Sequential Hooke's law Algorithm for Point Pattern reconstruction with Environmental constraints

SHAPPE reconstructs spatial point patterns from environmental constraints, improving ABM initialization and reducing cell interaction errors in glioblastoma samples.

A mathematical model of tumor growth driven by oxygen lactate exosome feedback

This paper develops a mathematical model to study how oxygen, lactate, and exosome feedback mechanisms influence tumor growth dynamics under different hypoxic conditions.

Crowding governs resistance confinement and guides the improvement of adaptive therapy in a 2D melanoma model system

This study shows how mechanical crowding in 2D melanoma models influences resistance expansion and therapy failure, guiding adaptive therapy improvement through physical and computational models.

Reinforcement Failing guides the discovery of emergent spatial dynamics in adaptive tumor therapy

This paper introduces Reinforcement Failing, an AI framework that identifies emergent spatial dynamics in adaptive tumor therapy by refining training environments.

Impact of oxygen fluctuation and consumption on the dynamics of a reduced go-or-grow model for tumor cell invasion

This study shows that rapid oxygen fluctuations alter tumor growth dynamics in a go-or-grow model, with the full model outperforming a reduced version under these conditions.

A Comparison of Mechanisms Driving Lesion Outcomes during Lung Tumor and Tuberculosis Granuloma Formation

This paper compares the mechanisms driving lesion outcomes in lung tumors and tuberculosis granulomas using agent-based models to study immune-tumor interactions and treatment responses.

Cancer Evolvability Determines Therapy Outcomes

This paper develops an evolutionary model to show how cancer evolvability affects therapy outcomes, finding that targeted therapy is better for low-evolvability cancers and chemotherapy for high-evolvability ones, with a combined approach effective against facultative evolvability.

Fractional Order Temporal Dynamics of Tumor Immune Chemotherapy Interactions: A Reproducible Dimensionless Analysis and a Pharmacokinetic Caveat

The paper analyzes fractional order temporal dynamics in tumor-immune chemotherapy models, showing equilibrium independence from fractional order and correcting misconceptions about drug concentration behavior.

The trade-off between parsimony and model complexity for understanding biomedical mechanisms from mathematical models

The paper explores the balance between model simplicity and complexity in mathematical models of ovarian cancer growth and treatment response, showing that statistical metrics alone are insufficient for capturing biological mechanisms.

Cooperativity through Allee Effects drives growth of diffuse midline gliomas impacting the optimal scheduling of therapeutic interventions

The paper models subclonal interactions in diffuse midline gliomas using a modified Lotka-Volterra model to show how Allee effects influence therapeutic outcomes and treatment scheduling.

Predictive Modeling of Cancer Cell Growth Kinetics with Machine Learning

This study uses Random Forest regression to predict cancer cell growth kinetics, showing higher accuracy than logistic and Gompertz models at high seeding densities.

Explaining and predicting pseudoprogression in immunotherapythrough joint modeling of immune infiltration and ctDNA dynamics

This paper develops a mathematical model of tumor-immune dynamics under ICI therapy to predict pseudoprogression and trains a classifier using tumor and ctDNA data to improve progression classification.

Noninvasive tomography of tumor progression via dynamical changes of T-cells in surrounding microenvironment

This paper develops a framework to noninvasively assess tumor progression by analyzing T-cell distribution changes in the microenvironment.

Communication through autoattractants can enhance and limit collective migration of immune cells

This study shows autoattractants enhance immune cell migration but can also cause aggregation, with optimal communication balancing cell coordination and movement efficiency.

How optimal control of cellular cost shapes population-level tumor growth dynamics

This paper models tumor growth dynamics using a controlled birth-death process to show how cost structures and constraints influence population-level regulation of proliferation and apoptosis.

Evolutionary rescue model informs strategies for driving cancer cell populations to extinction

This paper develops an evolutionary model to evaluate extinction therapy strategies for cancer, showing it outperforms combination therapy when cross-resistance exists.

A mechanistic digital twin model for epigenetic therapy optimization in triple-negative breast cancer

This paper develops a digital twin model to optimize epigenetic therapies in triple-negative breast cancer by simulating treatment responses and identifying effective drug combinations.

Clusters, Fingers, and Singles: A Mechanical Landscape of Tumor Invasion

The paper presents a computational model of tumor invasion, identifying four invasion phenotypes and showing that multimodal invasion is most common under specific mechanical conditions.

Testable Clinical Signatures for Go-or-Grow Dichotomy of Gliomas along Anisotropic White-matter Tracts

The paper derives a formula for tumor expansion speed based on cell movement and growth rates and validates it along white-matter tracts using diffusion tensor imaging.

Robust and Quality-of-Life-Aware Treatment Protocols in NSCLC using Deep Reinforcement Learning

This study uses deep reinforcement learning to design treatment protocols for NSCLC that improve robustness, quality of life, and time to progression compared to standard adaptive therapy and maximum tolerable dose approaches.

INFORME: coupling information-theoretic experimental design with nonlinear mixed-effects modeling for efficient observation scheduling

INFORME uses information-theoretic design with mixed-effects modeling to adaptively schedule measurements, reducing the number of scans needed for patient-specific treatment response prediction.

An agent-based 3D model of non-genetic adaptation in cancer tissues under electrical, mechanical, and hypoxic stress

This paper develops an agent-based 3D tumor model to study non-genetic adaptation under electrical, mechanical, and hypoxic stress, showing how these factors shape phenotypic heterogeneity and adaptive memory.

Critical Fragility Emerges from Chromosomal Instability in Cancer

This study develops a computational framework to model genomic instability in tumors, showing it can both drive evolution and lead to population collapse, with implications for therapy.

Initial tumor composition shapes resistance evolution and treatment outcomes in non-small cell lung cancer

This study shows that initial tumor composition influences resistance evolution and treatment outcomes in NSCLC, with resistance fitness effects varying based on initial resistant cell proportion.

Spatial agent-based modeling and interpretable machine learning identify determinants of combination-therapy response in HER2-heterogeneous breast cancer

The paper develops a spatial agent-based model and interpretable machine learning to analyze how HER2 heterogeneity and combination therapy affect breast cancer treatment response.