Bilevel Modelling of Metabolic Networks for Computational Strain Design

Bilevel modelling has been widely applied for the identification of genetic perturbations in metabolic engineering. However, most current approaches are based on a biased assumption that mutant strains always grow optimally. In addition, they are developed based on production rates, which may not meet yield requirements imposed on a production strain. This paper propose to design strains via multiobjective bilevel models that account for the tradeoff between cell growth and metabolic adjustments from the wild type strain. The proposed modelling frameworks can be used to identify design strategies that maximise rates and/or yields of target products, termed rate-based and yield-based modelling, respectively. We demonstrate, through in silico production of important chemicals in Escherichia coli, that our modelling approaches can generate growth-coupled designs in terms of rate and/or yield, and yield-based modelling identifies design strategies consistent with existing experimental studies as well as suggesting novel designs, thereby holding great promise for selecting targets for high-performance strain design. An important finding from this work that a growth rate coupled design is not necessarily growth yield coupled and vice versa suggests that growth-coupled designs should be analysed in both rate and yield spaces to determine their theoretical feasibility.