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Table 1 A comparison between 13C-MFA and COBRA

From: Studying metabolic flux adaptations in cancer through integrated experimental-computational approaches

Network size Small-scale (typically central metabolism)
Difficult to determine network model boundaries
Experimentally and computationally hard to extend for larger networks
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