Feilun Wu

The paper demonstrates a method to revive dead microbes by transplanting synthetic genomes, eliminating the need for antibiotic resistance markers in the process.

This paper develops a foundation model for microbial growth dynamics using self-supervised learning on diverse datasets, enabling accurate reconstruction and prediction of growth patterns and antibiotic responses.

The paper introduces a framework using neural networks to accelerate computational simulations of biological models by emulating their behavior across larger parametric spaces.

The paper develops a general rule for predicting mutualistic system outcomes and introduces a calibration method to apply it without full mechanistic understanding.