Data-driven Metabolic Network Reduction for Multiple Modes Considering Uncertain Measurements

Dynamic models of biotechnological processes form the basis of process optimization, control, and estimation. Metabolic network models are often at the core of such models. Since metabolic network models can be very large, and consequently computationally expensive, model reduction techniques can be applied. The derivation of a suitable reduced metabolic network that captures the essential metabolism is still a challenging problem. State-of-the-art network reduction algorithms utilize a priori defined phenotypes that reflect the expected behavior of the biological system. However, most bioprocesses undergo changes in the metabolism, hence, a switch in the cellular phenotype. If these phenotypes are unknown a priori, the reduced network fails to represent all observed metabolic behaviors. Contrary to these approaches, we propose a method that reduces genome-scale metabolic networks models using data from real experiments instead of relying on predefined phenotypes. Doing so, we circumvent the use of a priori information and guarantee that the network is capable to describe all observed phenotypes and can be reliably used for estimation, prediction, and optimization.