Our paper led by Xiaojun on Data-driven model discovery and model selection for noisy biological systems has been published PLOS Comp Biol. Mathematical models wielded skillfully can offer great insight into biological systems. The process of constructing models, however, is typically manual and labor-intensive. Data-driven model discovery provides an exciting alternative but dealing appropriately with the typical level of biological noise we observe in data is a challenge. Here we presents model discovery and model selection methods to infer models and evaluate the current limits of model discovery from noisy data.