Figure 1.

Principles of constraint-based modeling. A three-dimensional flux space for a given metabolic network is depicted here. Without any constraints the fluxes can take on any real value. After application of stoichiometric, thermodynamic and enzyme capacity constraints, the possible solutions are confined to a region in the total flux space, termed the allowable solution space. Any point outside of this space violates one or more of the applied constraints. Linear optimization can then be applied to identify a solution in the allowable solution space that maximizes or minimizes a defined objective, for example ATP or biomass production [3-6].

Reed et al. Genome Biology 2003 4:R54   doi:10.1186/gb-2003-4-9-r54