Jia Grace Lu

This paper introduces a simulation-conditioned generative framework that combines mechanistic models with image generation to create biologically realistic patterns, enabling inference from experimental data.

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.

This study shows how transposon-plasmid coevolution enables Escherichia coli to dynamically respond to antibiotics through population-level feedback control.

The paper develops a new computational method to analyze plasmid copy numbers across microbial genomes and identifies three scaling laws relating plasmid length, gene content, and metabolic function.

The paper integrates mechanistic modeling with machine learning to accelerate the discovery of design rules for pattern formation in living cells.

The paper demonstrates a method using self-organized patterns and machine learning to encode and decode information, showing scalability by mapping the English keyboard.