An extended and generalized framework for the calculation of metabolic intervention strategies based on minimal cut sets

Abstract

The concept of minimal cut sets (MCS) provides a flexible framework for analyzing properties of metabolic networks and for computing metabolic intervention strategies. In particular, it has been used to support the targeted design of microbial strains for bio-based production processes. Herein we present a number of major extensions that generalize the existing MCS approach and broaden its scope for applications in metabolic engineering. We first introduce a modified approach to integrate gene-protein-reaction associations (GPR) in the metabolic network structure for the computation of gene-based intervention strategies. In particular, we present a set of novel compression rules for GPR associations, which effectively speedup the computation of gene-based MCS by a factor of up to one order of magnitude. These rules are not specific for MCS and as well applicable to other computational strain design methods. Second, we enhance the MCS framework by allowing the definition of multiple target (undesired) and multiple protected (desired) regions. This enables precise tailoring of the metabolic solution space of the designed strain with unlimited flexibility. Together with further generalizations such as individual cost factors for each intervention, direct combinations of reaction/gene deletions and additions as well as the possibility to search for substrate co-feeding strategies, the scope of the MCS framework could be broadly extended. We demonstrate the applicability and performance benefits of the described developments by computing (gene-based) Escherichia coli strain designs for the bio-based production of 2,3-butanediol, a chemical, that has recently received much attention in the field of metabolic engineering. With our extended framework, we could identify promising strain designs that were formerly unpredictable, including those based on substrate co-feeding.

Author summary

The targeted modification of metabolic networks, e.g. for designing microbial cell factories or to combat cancer cells, is often supported by computational methods. The framework of Minimal Cut Sets (MCS) uses a constraint-based approach to determine a minimum set of reaction deletions in a metabolic network that enforce desired phenotypes according to user-defined specifications. In this work we generalize the MCS approach by introducing several new features making it suitable for a broader range of applications. Among other extensions, the new features support (1) the combination of multiple strain design specifications at once and thus more precise metabolic network tailoring, (2) the optional addition of alternative substrates or of heterologous reactions in combination with reaction deletions, and (3) an improved direct computation of gene-based intervention strategies also exploiting new compression rules for gene-reaction-enzyme relationships. We use the example of designing E. coli strains with different specifications for growth-coupled production of 2,3-butanediol to demonstrate the functional and performance benefits of our methodological enhancements.