Figure 3. Flux balance analysis (FBA).

(a) In FBA, a cellular objective (e.g., biomass production) is optimized. This provides the predicted flux for each reaction in the network. (b) FBA solutions are typically not unique, i.e., there are alternate optimal solutions that use different pathways to achieve the same objective value (e.g., growth rate). (c) Additional constraints can be applied to reduce the solution space size, and may remove competing optimal solutions, or (d) change the optimal solution. If the optimal solution is moved, then the choice of the new optimal solution may depend on the solver and/or algorithm, as shown for the MOMA⁵⁰ method. (e) The addition of constraints can enhance predictions. For example, when constraints on molecular crowding are added, the model-predicted order of substrate metabolism is consistent with experimental observation. Panel e reproduced from⁵⁷, Copyright 2007, National Academy of Sciences, USA. NTPs, nucleotide triphosphates; AAs, amino acids; FVA, flux variability analysis; v, reaction flux; μ_max, predicted maximum growth rate.