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 |