Mathematical Biology Tumor Growth

Personalized therapy design for liquid tumors via optimal control theory

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

Optimal control of acute myeloid leukaemia

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

Heterogeneous multi-scale framework for cancer systems models and clinical applications

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

Assessment of patient-specific efficacy of chemo- and targeted-therapies: a micropharmacology approach

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

To cure or not to cure: consequences of immunological interactions in CML treatment

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.

Multi-scale modeling reveals angiogenesis-induced drug resistance in brain tumor and predicts a synergistic drug combination targeting EGFR and VEGFR pathways

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.

A stochastic spatial model for heterogeneity in cancergrowth

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.

A mathematical model of viral oncology as a instigator of immuno-oncology

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.

A novel averaging principle provides insights in the impact of intratumoral heterogeneity on tumor progression

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: GPU-powered Spheroid Models for Mesenchyme and Epithelium

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

Dynamic variability in apoptotic threshold as a strategy for combating fractional killing

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.

Quantifying Uncertainty and Robustness in a Biomathematical Model Based Patient-Specific Response Metric for Glioblastoma

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.

Computational modeling of the crosstalk between macrophage polarization and tumor cell plasticity in the tumor microenvironment

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.

Modelling genotypes in their physical microenvironment to predict single- and multi-cellular behaviour

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.

Learning-accelerated Discovery of Immune-Tumour Interactions

The paper presents a framework combining agent-based simulation and active learning to discover optimal cancer regression regions in immunotherapy models.

A free boundary model of epithelial dynamics

The paper develops a free boundary model to describe epithelial dynamics by deriving a continuum-limit PDE that accurately predicts cell-based model behavior with a mass-conserving boundary condition.

High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow

This paper introduces a high-throughput framework integrating PhysiCell and EMEWS to test cancer hypotheses through mechanistic simulations and optimize therapeutic strategies.

PhysiBoSS: a multi-scale agent based modelling framework integrating physical dimension and cell signalling

PhysiBoSS integrates multi-scale agent-based modelling with Boolean networks to study cell population dynamics and treatment responses in 2D and 3D environments.

A multi-state model of chemoresistance to characterize phenotypic dynamics in breast cancer

This paper develops a multi-state model to analyze chemoresistance dynamics in breast cancer using experimental and mathematical modeling approaches.

In silico mouse study identifies tumor growth kinetics as biomarkers for the outcome of anti-angiogenic treatment

The paper uses a computational model to identify tumor growth kinetic parameters as prognostic biomarkers for anti-angiogenic treatment outcomes.

Quantitative agent-based modeling reveals mechanical stress response of growing tumor spheroids is predictable over various growth conditions and cell lines

The paper develops an agent-based model to predict tumor spheroid growth under mechanical stress across different conditions and cell lines.

PhysiCell: an Open Source Physics-Based Cell Simulator for 3-D Multicellular Systems

Open source off-lattice agent-based C++ simulator PhysiCell, coupled to the BioFVM diffusion solver, simulates 3-D multicellular tissues with cost linear in cell number, reaching roughly 10^6 cells.

Heritable tumor cell division rate heterogeneity induces clonal dominance

The paper models clonal dominance in tumor cells by showing that varying division rates, not stem cell properties or random growth, lead to clonal dominance in vitro.

Days Gained: A Simulation-Based, Response Metric in the Assessment of Glioblastoma

This paper introduces a simulation-based metric, Days Gained, to assess glioblastoma response to therapy using MRI-derived growth models.

Mechanistic Modeling Quantifies The Influence Of Tumor Growth Kinetics On The Response To Anti-Angiogenic Treatment

This study uses computational modeling to show that tumor growth kinetics influence the response to anti-VEGF treatment, with nonlinear dependence on VEGF receptor expression.

Hybrid approach for parameter estimation in agent-based models

The paper introduces a hybrid method to estimate parameters in agent-based models using glioblastoma data to improve model calibration.

Aggregation of biological particles under radial directional guidance.

The paper studies radially-symmetric anisotropic diffusion to model aggregation phenomena in biological systems without relying on chemotaxis or adhesion.

A space-jump derivation for non-local models of cell-cell adhesion and non-local chemotaxis

This paper derives non-local models of cell-cell adhesion and chemotaxis from a space-jump process and validates them with random walk simulations.

Addressing current challenges in cancer immunotherapy with mathematical and computational modeling

This review discusses mathematical and computational models addressing challenges in cancer immunotherapy, including tumor classification, treatment scheduling, and combination therapy design.

A hierarchical Bayesian framework for understanding the spatiotemporal dynamics of the intestinal epithelium

The paper develops a hierarchical Bayesian framework to model spatiotemporal dynamics of intestinal epithelium proliferation and migration under different conditions, concluding a proliferation-driven model is sufficient for most time-scales.