Constraint-based metabolic control analysis for rational strain engineering

Sophia Tsouka,Meric Ataman,Tuure Hameri,Ljubisa Miskovic,Vassily Hatzimanikatis

doi:10.1016/j.ymben.2021.03.003

Sophia Tsouka, Meric Ataman + Show 3 more

Open Access

https://doi.org/10.1016/j.ymben.2021.03.003

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Abstract

The advancements in genome editing techniques over the past years have rekindled interest in rational metabolic engineering strategies. While Metabolic Control Analysis (MCA) is a well-established method for quantifying the effects of metabolic engineering interventions on flows in metabolic networks and metabolite concentrations, it does not consider the physiological limitations of the cellular environment and metabolic engineering design constraints. We report here a constraint-based framework, Network Response Analysis (NRA), for rational genetic strain design. NRA is cast as a Mixed-Integer Linear Programming problem that integrates MCA, Thermodynamically-based Flux Analysis (TFA), biologically relevant constraints, as well as genome editing restrictions into a comprehensive platform for identifying metabolic engineering targets. We show that the NRA formulation and its core constraints are equivalent to the ones of Flux Balance Analysis (FBA) and TFA, which allows it to be used for a wide range of optimization criteria and with various physiological constraints. We also show how the parametrization and introduction of biological constraints enhance the NRA formulation compared to the classical MCA approach, and we demonstrate its features and its ability to generate multiple alternative optimal strategies given several user-defined boundaries and objectives. In summary, NRA is a sophisticated alternative to classical MCA for rational metabolic engineering that accommodates the incorporation of physiological data at metabolic flux, metabolite concentration, and enzyme expression levels.

Highlights

Recent improvements in genome editing techniques have paved the way for more sophisticated and performant metabolic engineering de signs for achieving desired physiological states of host organisms
The strain design should likewise consider that enzyme expression levels cannot increase beyond the currently reported experimentally achiev able levels, and it cannot allow an infinite increase of reaction fluxes in the network
Network Response Analysis (NRA) allows to metabolic engineers to simultaneously consider the desired yield and specific productivity of target chemicals, which is a ubiquitous design consideration in metabolic engineering

Summary

Introduction

Recent improvements in genome editing techniques have paved the way for more sophisticated and performant metabolic engineering de signs for achieving desired physiological states of host organisms. Two approaches for reaching the targeted states exist: (i) integrating heter ologous pathways to disruptively overcome native control patterns, and (ii) modifying the endogenous regulatory architecture by removal of the existing control loops (Bailey, 1991). The former method can be rather arduous because it requires testing if the integration of DNA fragments into the original genome sequence perturbs cellular regulation in the desired fashion. Strain design requires the identification and engineering of pathways toward the production of desired com pounds (Hadadi and Hatzimanikatis, 2015), and mathematical models can provide an invaluable insight in the process of selection of deletions, insertions, and up- and down-regulation of genes encoding for metabolic enzymes. Reviews of the most prominent computational tools and workflows for the strain design are provided elsewhere (Costa et al, 2016; Long et al, 2015; Wang et al, 2017)

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