Mathematical Biology Tumor Growth

BIO-LGCA: a cellular automaton modelling class for analysing collective cell migration

The paper introduces BIO-LGCA, a cellular automaton model for collective cell migration, and demonstrates its ability to predict cluster formation and cancer cell invasion plasticity.

Computational modeling of cell signaling and mutations in pancreatic cancer

The paper develops a computational model of pancreatic cancer signaling and mutations to identify potential inhibitor combinations for therapy.

Optimal Chemotherapy Scheduling for Non-Genetic Drug Resistance

This paper models transient drug resistance in tumors and finds continuous therapy is optimal under estimated parameters, but alternating schedules can be optimal under different conditions.

TISON: a next-generation multi-scale modeling theatre for in silico systems oncology

TISON is a web-based platform for multi-scale cancer modeling that integrates biomolecular data and patient-specific information to support drug target discovery and personalized therapeutics.

Standing Variations Modeling Captures Inter-Individual Heterogeneity in a Deterministic Model of Prostate Cancer Response to Combination Therapy

The paper models prostate cancer response to sipuleucel-T and androgen depletion using a deterministic model with standing variations to capture inter-individual heterogeneity, predicting an optimal combination therapy.

Repulsive expansion dynamics in colony growth and patterning

The paper develops a framework to simulate growth and gene expression dynamics in radially symmetric colonies using repulsive-expansion kinetics.

A rigid body framework for multi-cellular modelling

This paper introduces a rigid body framework for multi-cellular modelling using one-dimensional edges to represent cells, demonstrating its application in replicating established results and solving limitations of current models.

A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors

This paper introduces a stochastic mesoscopic simulation framework to model tumor heterogeneity and evolutionary dynamics while maintaining low computational costs.

Impact of tumor-parenchyma biomechanics on liver metastatic progression: a multi-model approach

This study uses multi-model simulations to show how liver parenchyma biomechanics influence metastatic tumor growth and dormancy.

Reconstructing tumor trajectories during therapy through integration of multiple measurement modalities.

The paper reconstructs tumor size trajectories during therapy by integrating multiple measurement modalities to identify distinct patterns of tumor response and resistance.

A multiscale model of complex endothelial cell dynamics in early angiogenesis

This paper introduces a multiscale model of angiogenesis that reproduces vascular network features through cell rearrangements and uses a mixing measure to quantify cell mixing as a potential biomarker for pathological angiogenesis.

Multi-scale modeling of macrophage - T cell interactions within the tumor microenvironment and impacts of macrophage-based immunotherapies

This study develops an agent-based model to evaluate macrophage-based immunotherapies in the tumor microenvironment, finding that reeducation is the most effective strategy.

PERFECTO: Prediction of Extended Response and Growth Functions for Estimating Chemotherapy Outcomes in Breast Cancer

PERFECTO predicts tumor growth and chemotherapy response using clinical data to improve personalized breast cancer treatment planning.

Time-dependent synergy for multi-agent anticancer therapy

The paper introduces second-order sensitivity analysis to detect time-dependent synergy in multi-agent anticancer therapy using combined radio-chemotherapy models.

Designing combination therapies using multiple optimal controls

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

Delicate balances in cancer chemotherapy: modeling immune recruitment and emergence of systemic drug resistance

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.

Growth pattern Learning for Unsupervised Extraction of Cancer Kinetics

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

Prediction of Individual Breast Cancer Evolution to Surgical Size

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.

Combining Mechanistic Models and Machine Learning for Personalized Chemotherapy and Surgery Sequencing in Breast Cancer

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

Comparison of drug inhibitory effects (IC50) in monolayer and spheroid cultures

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

Self-organization in brain tumors: how cell morphology and cell density influence glioma pattern formation

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.

Drug-induced resistance in micrometastases: analysis of spatio-temporal cell lineages

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.

Invasion of homogeneous and polyploid populations in nutrient-limiting environments

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

Notch signaling and taxis mechanims regulate early stage angiogenesis: A mathematical and computational model

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.

Personalized Tumor Growth Prediction with Multiscale Tumor Modeling

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.

Tumor Characterization using Unsupervised Learning of Mathematical Relations within Breast Cancer 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.

Genetic instability as a driver for immune surveillance

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.

Geostatistical visualization of ecological interactions in tumors

The paper introduces a geostatistical approach to visualize tumor heterogeneity and spatial patterns in the tumor microenvironment using agent-based models of ecological interactions.

Hypoxia in cancer chemo- and immunotherapy: foe or friend?

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

Metabolic reprogramming dynamics in tumor spheroids: Insights from a multicellular, multiscale model

The paper presents a multiscale mathematical model of avascular tumor growth in pancreatic cancer, simulating metabolic reprogramming and its effects on tumor dynamics.