Before revision: Cellular metabolism is predicted accurately at the genome-scale using constraint based modeling. Such predictions typically rely on optimizing an assumed cellular objective function, which takes the form of a stoichiometrically-determined reaction such as biomass synthesis, ATP yield, or reactive oxygen species formation. While these objective functions are typically constructed by hand, several algorithms have been developed to estimate them from data . Generally, two approaches for data-driven objective estimation exist: estimating objective weights for existing reactions, and de novo generation of a new objective reaction .
After revision: Optimization models have been used to predict cellular behavior for over 25 years. The constraints in these models are reconstructed from genome annotations, measured macro-molecular composition of cells, and measuring the cell's growth rate and metabolism in different conditions. The cellular goal (the optimization problem the cell is trying to solve) can be challenging to define experimentally for URLanisms, including human or mammalian cells, which have complex metabolic capabilities and are not well understood. Existing approaches to learning goals from data include (a) estimating a linear objective function, or (b) estimating linear constraints that model complex biochemical reactions and constrain the cell's operation .