John S. Lowengrub

This study identifies two cell-of-origin-driven subtypes of BRCA1-associated TNBC, showing that tumor subtype depends on the cell type initiating the cancer.

This paper introduces a fast, energy-stable numerical scheme for simulating vesicle deformation under osmotic pressure, achieving significant computational speedup compared to classical methods.

The paper introduces BiLO, a neural network method for solving PDE inverse problems by formulating them as bilevel optimization problems, showing it enforces PDE constraints and handles noisy data effectively.

The paper develops and applies Cahn-Hilliard solvers to model biomolecular condensate dynamics, showing they match experimental behaviors of chromosomal passenger complex condensation.

tauFisher predicts circadian time from bulk and single-cell transcriptomic data by analyzing rhythmic gene correlations, showing strong performance across multiple tissues and experimental settings.

This paper develops a nonlinear mathematical model of CML hematopoiesis to predict TKI therapy responses and identifies strategies to improve treatment outcomes.

The paper uses spatially explicit models to show that feedback regulation in cell lineages requires spatial feedforward loops to maintain tissue homeostasis.

The paper analyzes feedback regulation in bladder cancer to identify mechanisms causing cancer stem cell enrichment and chemoresistance.

This study uses mathematical models to analyze how feedback loops influence cancer stem cell fractions in tumors and healthy tissues, identifying conditions that promote or reduce stem cell enrichment.