Mathematical Biology Tumor Growth

Qualitative Optimization of Oncolytic Virotherapy and Immune Therapy Combination Treatments

This study uses mathematical modeling to explore strategies for improving oncolytic virotherapy and immune therapy combination treatments, highlighting the importance of timing and suggesting promising treatment scenarios.

ViVo: A temporal modeling framework that boosts statistical power and minimizes animal usage

ViVo improves statistical power and reduces animal use in tumor studies by modeling tumor growth rates and treatment effects across time.

A Mechanistic Model of Oncolytic Virus-CAR T Therapy Identifies Memory-Dependent Control of Solid Tumors

This paper develops a mechanistic model of oncolytic virus-CAR T therapy, showing that OV-induced CAR T-cell memory is critical for long-term control of solid tumors.

Abnormal vasculature reduces overlap between drugs and oxygen in a tumour computational model: implications for therapeutic efficacy

This study uses a computational model to show that in tumours, oxygen and drug concentration overlap is reduced compared to healthy tissue, affecting drug efficacy.

Abnormal bioelectricity during unperturbed cancer growth. Part-I: electrical biopotentials, surface charge density, volumetric charge density, phase-field and Turing approach.

This paper simulates the electrical and mechanical coevolution of unperturbed solid tumors using spherical harmonics, phase-field, and Turing approaches, revealing how charge densities and patterns relate to tumor growth and metastasis.

Astrocyte reprogramming drives tumor progression and chemotherapy resistance in agent-based models of breast cancer brain metastases

Dynamic optimization of chemo-immunotherapy sequencing reveals phenotype-dependent regimens across tumor immune microenvironments

This paper develops a dynamic optimization framework to design chemo-immunotherapy regimens tailored to tumor immune microenvironment phenotypes.

A memory-driven model of phenotypic adaptation for anticipating the emergence of enzalutamide resistance in prostate cancer

This paper develops a memory-driven model to predict enzalutamide resistance in prostate cancer by integrating reinforcement learning and clinical data, outperforming traditional models in capturing treatment dynamics and patient responses.

Impacts of competition and phenotypic plasticity on the viability of adaptive therapy

This paper develops a deterministic model to study how competition and phenotypic plasticity affect adaptive therapy outcomes in cancer, showing that phenotypic transitions can complicate therapy and that asymmetric competition benefits treatment.

Optimizing T-cell-mediated cancer killing: An agent-based model

This study uses an agent-based model to simulate T-cell interactions with tumors and identifies parameter optimizations that enhance tumor clearance.

Cancer Interception During Treatment: Using Growth Kinetics to Create a Continuous Variable for Assessing Disease Response

The paper applies mathematical tumor growth models to clinical data to estimate growth rates and assess treatment response, finding strong correlations between growth rates and overall survival.

A simple growth model for tumor spheroids

The paper presents a simple biophysically motivated growth model for tumor spheroids that integrates energy conservation, oxygen diffusion, and necrosis formation, accurately reproducing growth kinetics across multiple phases.

A perfusion-based model to explain how paclitaxel achieves tumour-selective killing

This paper develops a mathematical model to explain how paclitaxel selectively kills tumour cells by exploiting differences in perfusion and drug retention between tumour and healthy tissues.

A mathematical model for cancer dynamics with treatment and saboteur bacteria

The paper develops a mathematical model to analyze cancer dynamics with treatment and saboteur bacteria, showing how antibiotics can mitigate bacterial sabotage of chemotherapy.

The impact of normal tissue density on tumor growth and evolution in a 3D whole-tumor model of lung cancer

This study uses a 3D computational model to show that tumor growth rates depend on tissue density and metabolic strategies like glycolysis or angiogenesis in lung cancer.

Drug-loaded nanoparticles for cancer therapy: a high-throughput multicellular agent-based modeling study

This study develops a multicellular agent-based model to simulate nanoparticle interactions in cancer therapy and evaluates how pharmacokinetic parameters and therapeutic strategies affect tumor dynamics.

PhysiGym: bridging the gap between the Gymnasium reinforcement learning application interface and the PhysiCell agent-based model software

PhysiGym integrates PhysiCell with Gymnasium to enable reinforcement learning control of in silico biological processes, demonstrating tumor elimination in a case study.

Modeling Bistable Dynamics Arising from Macrophage-Tumor Interactions in the Tumor Microenvironment

This study develops a mathematical model to analyze bistable tumor dynamics influenced by macrophage polarization and links findings to potential treatment strategies.

CAR T-cell and oncolytic virus dynamics and determinants of combination therapy success for glioblastoma

This study uses a mathematical model to analyze CAR T-cell and oncolytic virus dynamics in glioblastoma, identifying virus bursting size as a key determinant of combination therapy efficacy.

Evolutionary antifragile therapy

This paper develops and validates a treatment strategy called evolutionary antifragile therapy, which uses treatment fluctuations to limit tumor resistance evolution.

Mathematical Modeling and Simulation of Tumor-Induced Angiogenesis in Retinal Hemangioblastoma

This study uses a mathematical model combined with OCTA images to simulate tumor-induced angiogenesis in retinal hemangioblastoma, revealing critical insights into the timing and dynamics of vascular development.

Proliferation to suppress neoplasia: a general model and a first test in the moon jelly

The paper develops a dynamical model showing that increased cell proliferation can suppress neoplasia through high-flux proofreading, supported by experimental tests in moon jellies.

Mechanistic modeling of cell viability assays with in silico lineage tracing

The paper develops an algorithm to simulate and compare mechanistic models of cell viability assays with experimental data to understand drug response variability.

Mathematical model of tumor-macrophage dynamics in glioma to advance myeloid-targeted therapies

The paper develops a mathematical model of glioma growth incorporating tumor-associated macrophages to evaluate myeloid-targeted therapies and their combination with radiation.

Self-generated chemotaxis of mixed cell populations

This study introduces a theoretical framework for self-organized chemotaxis in heterogeneous cell populations and validates it with in vitro experiments on immune cell co-migration.

Predicting Organoid Morphology Through a Phase Field Model: Insights into Cell Division and Lumenal Pressure

This paper uses a phase field model to predict organoid morphology by simulating cell division and lumenal pressure, identifying key parameters that determine shape and structure.

Beyond RECIST: mathematical modeling and Bayesian inference reveal the importance of immune suppressive parameters in metastatic breast cancer

The paper develops mathematical models and Bayesian inference methods to analyze tumor-immune dynamics in metastatic breast cancer, revealing immune suppressive parameters as key predictors of response to immunotherapy.

Optimal control of immune checkpoint inhibitor therapy in a heart-tumour model

This paper develops a mathematical model of heart-tumour interactions under immune checkpoint inhibitor therapy and uses optimal control to design dosing schedules that balance therapeutic and toxic effects.

Gaming the cancer-immunity cycle by synchronizing the dose schedules

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.

On the onset of multicellular invasive behavior in hierarchical lineage: the role of inhibitory feedback and local fluctuations

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.