Metabolic network reduction based on Extreme Pathway sets

The use of metabolic networks is extremely valuable for design and optimisation of bioprocesses as they provide great insight into cellular metabolism. Within bioprocess optimisation, they have enabled better (economic) objective performance through more accurate network-based models. However, one of the drawbacks of using metabolic networks is their underdeterminacy, leading to non-unique flux distributions. Flux Balance Analysis (FBA) reduces this issue by making assumptions on the behaviour of the cell. However, for metabolic networks of higher complexity, can still struggle with underdeterminacy. Metabolic network reduction can remove or greatly reduce this effect but can be difficult, especially when data is limited. Structural analysis of the metabolic network through Elementary Flux Modes (EFM) or Extreme Pathways (EP) can help locate the relevant information within the network. This work presents a metabolic network reduction approach based on the EPs that best explain a small set of available measurements. Many of the reactions will not be active during the process and a significantly smaller network can therefore be constructed. A case study of oxygen-limited Escherichia coli is presented which showcases this approach, enabling accurate prediction of the process with much smaller network-based models. This leads to much lower complexity bioprocess models while keeping the necessary information on cellular metabolism for the given process.