ABSTRACT
Constraint-based modelling is focused on metabolic networks and is most useful to predict the growth rate of a cell, under specific environmental conditions, such as given glucose uptake rate, or following perturbations, such as the knock-out of certain genes from an organism. More specifically, the key goal of constraint-based methods is to predict the ‘flux distribution’ or flux configuration of the cell, that is, the fluxes, or velocities of each and every reaction in the metabolic network. Constraints arise out of the spatial organisation of the cell, notably the crowding of an extraordinary variety of molecules including macromolecules such as DNA, which could not exist within the cell unless tightly packed. The defining mathematical object, for constraint-based models is the stoichiometric matrix, which captures the entire metabolic network of a cell in a mathematical form. The biomass objective function has been heavily used in constraint-based modelling since the late 1990s.
