Nicolas E. G. Buchler

This paper develops a deep learning framework to image and analyze cell division and growth across the entire sexual life cycle of Saccharomyces cerevisiae.

This paper shows that gene expression noise accelerates the evolution of a biological oscillator by revealing cryptic dynamic phenotypes that guide mutation towards robust oscillation.

This paper introduces an accelerated Bayesian method for inferring gene expression models from single-cell transcript data, showing improved model selection and parameter estimation.

The paper develops BayFish, a Bayesian inference method to estimate kinetic parameters of gene expression from smFISH data, showing increased promoter transition rates in response to stimuli.

The paper uses computer simulations to show that epigenetic switching helps organisms adapt quickly in fast fluctuating environments, while genetic adaptation is favored in slower fluctuations.