Mathematical Biology Tumor Growth

In Silico Investigations of Adaptive Therapy Using a Single Cytotoxic or a Single Cytostatic Drug

This paper investigates adaptive therapy with single cytotoxic or cytostatic drugs, finding it effective under specific conditions and dosing strategies.

In Silico Investigations of Adaptive Therapy Using Two Cytotoxic or Two Cytostatic Drugs

This paper evaluates adaptive therapy strategies using two cytotoxic or two cytostatic drugs, finding that dose modulation protocols perform better with cytotoxic drugs while ping-pong protocols are more effective with cytostatic drugs under specific conditions.

Predicting Anti-Cancer Drug Combination Responses with a Temporal Cell State Network Model

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.

Modeling Breast Cancer Proliferation, Drug Synergies, and Alternating Therapies

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.

PhenoCellPy: A Python package for biological cell behavior modeling

PhenoCellPy provides a Python package to model cell behaviors and states, enabling integration with other modeling frameworks.

Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions

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.

Myeloid-derived suppressor cell dynamics control outcomes in the metastatic niche

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

Phenotype switching in a global method for agent-based models of biological tissue

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

Pharmacodynamic model of PARP1 inhibition and global sensitivity analyses can lead to cancer biomarker discovery

The paper develops an ODE model of PARP1 inhibition, identifies key parameters through sensitivity analysis, and discovers GSPT2 as a biomarker linked to cancer patient outcomes.

Towards integration of time-resolved confocal microscopy of a 3D in vitro microfluidic platform with a hybrid multiscale model of tumor angiogenesis

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

Fitting agent-based models to tumor images using representation learning

This paper introduces a method to estimate ABM parameters by quantitatively comparing tumor images and ABM simulations using neural networks.

Integration of transcriptomics data into agent-based models of solid tumor metastasis

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

Optimal drug treatment for reducing long-term drug resistance

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

PhysiPKPD: A pharmacokinetics and pharmacodynamics module for PhysiCell

PhysiPKPD integrates pharmacokinetics and pharmacodynamics into PhysiCell models to enhance in silico simulations of molecular therapies.

Quantifying the Morphology and Mechanisms of Cancer Progression in 3D in-vitro environments: Integrating Experiments and Multiscale Models

This study integrates experimental and computational approaches to quantify cancer morphology and mechanisms in 3D cultures using a multiscale model and spatial analysis.

Therapy Induced Tumor Senescence Model

The paper proposes a simplified model to explain how senescent tumor cells are cleared by NK cells and how a two-punch therapy could regulate tumors through immune-tumor interactions.

Temporal Optimization of Radiation Therapy to Heterogeneous Tumour Populations and Cancer Stem Cells

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.

Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans?

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

MODEL PREDICTIVE CONTROL OF CANCER CELLULAR DYNAMICS: A NEW STRATEGY FOR THERAPY DESIGN

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

Uncovering potential interventions for pancreatic cancer patients via mathematical modeling

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.

The sum and the parts: dynamics of multiple and individual metastases during adaptive therapy

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

Integration of heterogeneous biological data in multiscale mechanistic model calibration: application to lung adenocarcinoma

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

Computational Model of Heterogeneity in Melanoma: Designing Therapies and Predicting Outcomes

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.

Optimizing dosage-specific treatments in a multi-scale model of a tumor growth

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.

Multiscale modeling of tumor adaption and invasion following anti-angiogenic therapy

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.

Chemotherapy response prediction with diffuser elapser network

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.

Data-driven Discovery of Mathematical and Physical Relations in Oncology Data using Human-understandable Machine Learning

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.

A computational modeling approach for predicting multicell patterns based on signaling-induced differential adhesion

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.

Bayesian combination of mechanistic modeling and machine learning (BaM3): improving personalized tumor growth predictions

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

Bridging cell-scale simulations and radiologic images to explain short-time intratumoral oxygen fluctuations

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.