Determination of growth-coupling strategies and their underlying principles

Tobias B Alter,Birgitta E Ebert

doi:10.1186/s12859-019-2946-7

Tobias B Alter, Birgitta E Ebert

Open Access

https://doi.org/10.1186/s12859-019-2946-7

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Abstract

BackgroundMetabolic coupling of product synthesis and microbial growth is a prominent approach for maximizing production performance. Growth-coupling (GC) also helps stabilizing target production and allows the selection of superior production strains by adaptive laboratory evolution. To support the implementation of growth-coupling strain designs, we seek to identify biologically relevant, metabolic principles that enforce strong growth-coupling on the basis of reaction knockouts.ResultsWe adapted an established bilevel programming framework to maximize the minimally guaranteed production rate at a fixed, medium growth rate. Using this revised formulation, we identified various GC intervention strategies for metabolites of the central carbon metabolism, which were examined for GC generating principles under diverse conditions. Curtailing the metabolism to render product formation an essential carbon drain was identified as one major strategy generating strong coupling of metabolic activity and target synthesis. Impeding the balancing of cofactors and protons in the absence of target production was the underlying principle of all other strategies and further increased the GC strength of the aforementioned strategies.ConclusionMaximizing the minimally guaranteed production rate at a medium growth rate is an attractive principle for the identification of strain designs that couple growth to target metabolite production. Moreover, it allows for controlling the inevitable compromise between growth coupling strength and the retaining of microbial viability. With regard to the corresponding metabolic principles, generating a dependency between the supply of global metabolic cofactors and product synthesis appears to be advantageous in enforcing strong GC for any metabolite. Deriving such strategies manually, is a hard task, due to which we suggest incorporating computational metabolic network analyses in metabolic engineering projects seeking to determine GC strain designs.

Highlights

Metabolic coupling of product synthesis and microbial growth is a prominent approach for maximizing production performance
While the GC classification into Weak growth-coupling (wGC), Holistic growth-coupling (hGC), and Strong growth-coupling (sGC) provides a qualitative notion of the GC strength, the position of the lower production rate boundary can be interpreted as a quantitative measure: the higher the boundary in terms of positive rate values, the stronger the GC
This principle was implemented in a bi-level optimization algorithm, gcOpt, which maximizes the minimum production rate of a target compound at a fixed, medium growth rate μfix using appropriate reaction deletions (Fig. 2)

Summary

Introduction

Metabolic coupling of product synthesis and microbial growth is a prominent approach for maximizing production performance. Various computational algorithms exploiting the rich information content of stoichiometric metabolic models have been developed to provide reaction deletion strategies leading to GC. These approaches are generally grouped into Flux Balance Analysis (FBA) and Elementary Modes Analysis (EMA) based methods. The main disadvantage of EMA-based compared to FBA-based methods is the computationally expensive necessity to enumerate all EMs, limiting the application of EMA to small or mid-scale metabolic networks This has been overcome by introducing MCSEnumerator, an algorithm that sequentially enumerates the smallest MCSs and significantly reduces the computational costs [22]. To effectively gain from the advantages of different methods in terms of a biologically robust strain design, combinations and adaptions of the mentioned algorithms have been reported [12, 23, 24]

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