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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

 

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