Before revision:  Here, we develop a new algorithm that extends existing approaches for de novo objective generation and solve it using the alternating direction method of multipliers (ADMM) . We demonstrate our approach on a genome-scale model and show that it identifies de novo objectives from measured fluxes with tunable sparsity .
After revision:  Here, we develop an approach to estimate constraint reactions from data that can scale to realistically large metabolic models. Previous tools have been used on problems having less than 70 biochemical reactions and 60 metabolites, which limits real-life-size applications. We perform extensive experiments using 70 large-scale metabolic network models for URLanisms (including bacteria, yeasts, and mammals) and show that it identifies de novo objectives from measured fluxes with tunable sparsity .