Emrah Şimşek

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.

The paper shows keystone engineering enables microbial range expansion by antibiotic-degrading bacteria, revealing spatial effects in microbial communities.

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 shows that microbial community dynamics can be represented by low-dimensional latent variables, with the critical dimension scaling linearly with observables.

The paper shows transposon-plasmid nesting allows microbes to rapidly respond to environmental changes by combining genetic mobility with copy number amplification.