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