| 13C-MFA | COBRA |
---|---|---|
Network size | Small-scale (typically central metabolism) Difficult to determine network model boundaries Experimentally and computationally hard to extend for larger networks | Genome-scale Enables finding activity of non-canonical metabolic pathways Potential false prediction of non-canonical metabolic activities due to the inclusion of reactions with weak biochemical evidence in the network model |
Typical experimental inputs | Biomass composition, growth rate, and metabolite uptake and secretion rates | |
Computational requirements | Isotope tracing measurements; potentially absolute metabolite concentrations | A variety of ‘omics’ datasets Requires simplifying assumptions for integrative analysis |
Mostly hard non-convex optimization problems solved heuristically | Mostly computationally tractable optimizations (linear or quadratic programming) | |
Determining a unique flux solution | Typically possible Assessing uncertainty with confidence intervals | Requires simplifying optimizations (e.g., maximal growth rate) |
Compartmentalization | Partially addressed with specific tracers, compartment-specific markers, cell fractionation | Addressed via simplifying optimization assumptions |
Applicability | Inferring fluxes in a specific condition | |
– | Predict flux adaptation following chemical/genetic alterations |