Model reduction of genome-scale metabolic models as a basis for targeted kinetic models

Constraint-based, genome-scale metabolic models are an essential tool to guide metabolic engineering. However, they lack the detail and time dimension that kinetic models with enzyme dynamics offer. Model reduction can be used to bridge the gap between the two methods and allow for the integration of kinetic models into the Design Built-Test-Learn cycle. Here we show that these reduced size models can be representative of the dynamics of the original model and demonstrate the automated generation and parameterisation of such models. Using these minimal models of metabolism could allow for further exploration of dynamic responses in metabolic networks.

Automated summary

Constraint-based, genome-scale metabolic models are crucial for guiding metabolic engineering, but lack the time dimension and enzyme dynamics. Model reduction can bridge the gap between these models and kinetic models, allowing integration into the Design Built-Test-Learn cycle. These reduced size models can represent the dynamics of the original model and enable further exploration of dynamic responses in metabolic networks.