Jason Thomas George

This paper develops a biophysical computational framework to identify antigen escape variants and compensatory TCR sequences by integrating mutagenesis data and structural modeling.

This paper models tumor growth dynamics using a controlled birth-death process to show how cost structures and constraints influence population-level regulation of proliferation and apoptosis.

The paper develops a biophysical model to predict T cell receptor specificity for viral and tumor antigens using structural data and sequence-based clustering.

This paper develops a coupled SDE-ODE model to infer tumor burden from biomarker signals by linking tumor-immune dynamics to ctDNA release.

The paper shows hypoxic memory in cancer cells affects tumor invasion dynamics through time-dependent changes in cell state and spatial oxygen dynamics.

This study models regulatory networks and spatial dynamics in ER+ breast cancer to show how resistance and immune escape co-occur and how combination therapies can counteract these mechanisms.

This paper develops a PDE framework to model hypoxic memory's impact on tumor invasion under cyclic hypoxia, showing enhanced invasion without reduced tumor volume.

The paper shows that interconnected axes of phenotypic plasticity in breast cancer drive coordinated cellular behavior and influence clinical outcomes through mutual inhibition between two teams of plasticity-related nodes.

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.

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.

This study uses a computational model to show how tumor-associated collagen signatures influence immune escape and survival in breast cancer.

This study develops a model to analyze how cellular memory and adaptation cost affect survival in fluctuating environments, finding that increased adaptation cost can improve growth by reducing mismatches between cell state and environment.

This paper develops a computational model of cancer metabolism integrating catabolic and anabolic pathways, identifying four metabolic phenotypes and their association with patient survival.

This paper introduces a stochastic model to study cancer immune escape dynamics, showing how immunomodulation in the tumor microenvironment affects disease recurrence timing in bladder and breast cancer.

This study shows that feature selection improves peptide binding predictions for TCRs by optimizing model complexity and performance using physicochemical properties and machine learning.

This study improves T cell-antigen specificity prediction by integrating kinetic data into affinity-driven models, enhancing predictions for low-affinity TCR-pMHC interactions.

The paper models the dynamics of E-M heterogeneity in breast cancer cells, finding that cell-state transitions, not growth differences, are essential for explaining observed population dynamics.

The paper compares three transcriptomic metrics to quantify epithelial-mesenchymal plasticity across cancer and non-cancer contexts, finding high concordance and applicability beyond cancer.

The paper uses computational models to study how macrophage polarization and cancer cell plasticity interact in the tumor microenvironment, showing that these interactions can lead to multiple stable states, including cancer cell extinction.

The paper uses computational methods to classify gene expression profiles into epithelial, hybrid E/M, and mesenchymal categories based on EMT dynamics and scoring metrics.