Mattia G. Gollub

ENKIE predicts KM and kcat enzyme kinetic parameters and their uncertainties using Bayesian Multilevel Models, achieving performance comparable to deep learning methods.

The paper introduces manufacturing-aware generative models to enable efficient biological sequence design and synthesis at petascale, reducing costs by a trillion-fold.

The paper introduces a probabilistic framework for analyzing metabolic networks by integrating thermodynamics and flux constraints, revealing new insights into E. coli metabolism.