Mathematical Biology Tumor Growth

This paper develops a mathematical model and optimization algorithm to determine optimal radiation therapy schedules for heterogeneous tumours, finding that the ratio of radiosensitivities between cell types is key to predicting optimal dose distribution.

This study fits the Kuznetsov model to 1472 cancer patients to predict tumor growth and shows it can extrapolate future dynamics.

The paper demonstrates that adaptive model predictive control can steer cancer cell signaling dynamics to mimic wild-type cells and support combination therapy design.

This paper uses a Boolean model to study the impact of mutations in pancreatic cancer and identifies potential drug targets based on simulation and clinical data.

This paper uses agent-based models to analyze how metastatic tumor dynamics inform adaptive therapy schedules and identifies how tumor heterogeneity affects treatment outcomes.

The paper describes a calibration strategy for multiscale mechanistic models using heterogeneous data, demonstrating its application to a lung adenocarcinoma model.

This paper develops a computational model to predict outcomes of different therapeutic strategies in melanoma by analyzing single-cell mRNA sequencing data and simulating tumor heterogeneity and drug resistance.

The paper develops an HPC-optimized workflow to explore treatment strategies for tumor growth using a multi-scale model, finding that spatial geometry and population variability affect treatment effectiveness.

This study uses agent-based modeling to show that anti-angiogenic therapy creates hypoxic and acidic conditions that increase tumor cell survival and invasiveness, but combining it with chemotherapy improves treatment response.

The paper develops a deep learning model to predict tumor microenvironment changes and chemotherapy response, achieving high accuracy in simulated tumor growth and drug delivery.

This paper introduces a machine learning system that discovers mathematical and physical relations in oncology data, revealing human-understandable insights into cancer dynamics and treatment responses.

The paper develops and validates agent-based models to predict multicell patterns driven by signaling-induced differential adhesion and uses machine learning to map parameters to patterns.

BaM3 improves personalized tumor growth predictions by combining mechanistic modeling and machine learning, showing better performance in simulated and real patient data.

This paper links radiologic image data to cell-scale simulations to explain oxygen fluctuations in tumors, showing vascular changes, not cellular absorption, drive these fluctuations.

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.

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

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 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.

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.

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

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.

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

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

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

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.

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

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

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

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.