Mathematical Biology Tumor Growth

The paper develops a mathematical model to study how tumor heterogeneity affects the response to combination therapy in hormone receptor-positive breast cancer, finding that pharmacokinetics and pharmacodynamics of fulvestrant drive treatment outcomes.

The paper challenges age-structured and first order transition models of cell proliferation by testing their fit to time course data from cancer cell line experiments.

This paper develops mathematical models to optimize cancer treatment protocols by analyzing genetic and epigenetic resistance, showing that an optimal protocol can achieve curative treatment in xenograft experiments.

The paper constructs a mathematical model to predict tumor control patterns based on CTL killing mechanisms and evaluates therapeutic outcomes under immune checkpoint blockade.

This paper develops a tumor-immune model for glioblastoma, identifies key parameters for treatment, and suggests combining immune checkpoint inhibitors with MDSC-targeting therapies.

This paper introduces an in silico model to simulate tumor growth and invasiveness, revealing that growth depends on cell replication speed and adhesion, while invasiveness is driven by chemotaxis.

The paper develops a new model for glioma invasion driven by microtubes, showing it can predict tumour expansion and relate to existing model classes.

This paper develops a mathematical model to simulate interclonal interactions in DLBCL, showing cooperative growth and the impact of therapy on tumor dynamics.

This study evaluates classical, fractional, and multiscale logistic models of tumour growth using Bayesian inference and uncertainty quantification to assess their fit under different treatment conditions.

PhysiBoSS 2.0 integrates stochastic Boolean and agent-based models for multiscale cancer simulations, improving modularity and functionality.

This paper compares parameter estimation in a computational model of ovarian cancer using 2D and 3D experimental data to assess the impact of experimental settings on model calibration and validation.

The paper introduces four empirical methods to validate time-to-event models in EGFR+ lung adenocarcinoma, considering uncertainty and variability, and shows their context-dependent effectiveness.

The paper presents analytical differential equations to model cell growth in 2D and 3D cultures, accounting for contact inhibition and other factors like geometry and nutrient depletion.

The paper develops a mathematical model to study cytokine storm risks in immune agonist therapies and identifies optimal dosing strategies to prevent storms while maintaining treatment efficacy.

The paper presents a mathematical framework to model cellular adaptation through internal variables, demonstrating its application to glioblastoma hypoxia adaptation.

Gell is a GPU-based hybrid simulator for large-scale multicellular systems that achieves 150X speedup and 10X lower memory use compared to CPU-based methods.

This study models the immune response in spatially fractionated radiotherapy, showing how heterogeneous dose distributions affect tumor cell killing through immune mechanisms.

The paper investigates how collective cell migration is robust to different integration strategies of guidance stimuli using an individual-based model.

This study develops an agent-based model of prostate cancer progression, showing that stochastic interactions and systematic factors influence tumor development and clinical outcomes.

The paper uses Bayesian multimodel inference to analyze tumor growth mechanisms in SCLC, finding that high lineage plasticity, not rare stem-like populations, drives tumor evolution.

This paper develops a statistical framework to estimate cell proliferation and phenotypic switching rates in cancer using cell line experiment data and applies it to a public dataset.

The paper presents a multiscale model of cancer cell invasion using PhysiBoSS, integrating spatial and intracellular processes to study migration modes and validate against experimental data.

The paper predicts cancer cell responses to drug combinations using a temporal cell state network model based on single drug dose responses and cell cycle transitions.

This study develops a mathematical model to simulate drug responses in ER+ breast cancer, identifying synergistic combinations and optimizing alternating therapies to reduce resistance and drug dose.

This paper introduces a new spatial statistic, the weighted pair correlation function (wPCF), to analyze spatial and phenotypic heterogeneity in agent-based models and multiplex imaging data.

This study develops a mathematical model to analyze MDSC dynamics in metastatic niches, showing that MDSC delays and suppression capabilities significantly impact metastasis outcomes.

The paper introduces a global method to simulate agent-based models of biological tissue with phenotype switching, reducing computational time while preserving key dynamics.

This study calibrates a multiscale tumor angiogenesis model with time-resolved microscopy data to predict vascular sprouting and assess model accuracy.

The paper integrates transcriptomics data into agent-based models of cancer to identify therapy resistance scenarios.

This paper proposes a treatment strategy that reduces long-term tumor burden by decreasing tumor adaptation rates through differential selective treatment.