A systems-level approach for metabolic engineering of yeast cell factories

Abstract

The generation of novel yeast cell factories for production of high-value industrial biotechnological products relies on three metabolic engineering principles: design, construction, and analysis. In the last two decades, strong efforts have been put on developing faster and more efficient strategies and/or technologies for each one of these principles. For design and construction, three major strategies are described in this review: (1) rational metabolic engineering; (2) inverse metabolic engineering; and (3) evolutionary strategies. Independent of the selected strategy, the process of designing yeast strains involves five decision points: (1) choice of product, (2) choice of chassis, (3) identification of target genes, (4) regulating the expression level of target genes, and (5) network balancing of the target genes. At the construction level, several molecular biology tools have been developed through the concept of synthetic biology and applied for the generation of novel, engineered yeast strains. For comprehensive and quantitative analysis of constructed strains, systems biology tools are commonly used and using a multi-omics approach. Key information about the biological system can be revealed, for example, identification of genetic regulatory mechanisms and competitive pathways, thereby assisting the in silico design of metabolic engineering strategies for improving strain performance. Examples on how systems and synthetic biology brought yeast metabolic engineering closer to industrial biotechnology are described in this review, and these examples should demonstrate the potential of a systems-level approach for fast and efficient generation of yeast cell factories.

Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models

“…However, engineering of microbial cell factories requires a simultaneous optimization of several criteria such as productivity, yield, titer, stress tolerance, all the while retaining the efficient, cost-effective and robust process. Several metabolic engineering strategies capable of integrating available proteomics, transcriptomics, metabolomics, fluxomics, and other types of 'omics' data into a systems design have been developed to meet this kind of specifications (Chen and Nielsen, 2013;Kim et al, 2012;Lewis et al, 2012;Thomas et al, 2007). An essential part of these strategies are in silico tools that can help in: (i) improving the production of the desired chemicals from the natural producers; (ii) identifying enzymes from other organisms capable of performing desired catalytic activity; or even (iii) synthetizing pathways not present in any known organism (Hatzimanikatis et al, 2005;Leonard et al, 2008;Soh and Hatzimanikatis, 2010a;Yim et al, 2011).…”

Metabolic engineering of Saccharomyces cerevisiae: a key cell factory platform for future biorefineries

“…Furthermore, a number of academic studies have illustrated the suitability of this cell factory for the production of a range of chemicals [5,14], e.g., lactic acid, glycerol, and malic acid. Several recent reviews provide an overview of the many different metabolic engineering examples using yeast as a cell factory [15,16], and Table 1 provides a summary of some of these key developments. In addition, the wide use of this organism is illustrated by the very large number of patents filed on the use of yeast and/or S. cerevisiae for production of fuels and chemicals (Table 2).…”

The Problem of Futile Cycles in Metabolic Flux Modeling: Flux Space Characterization and Practical Approaches to Its Solution

“…Both FBA and 13 C-MFA require the use of a metabolic network and the assumption of a steady state for internal metabolites disregarding dynamic intracellular behavior. However, FBA characterizes the "optimal" metabolism for the desired functional metabolic output (phenotype) and can be implemented on GEM, whereas 13 C-MFA profiles in vivo metabolic flux distribution in a metabolic network and current technique only allows it to work on a small-sized central metabolic network [44]. Nevertheless, as the 13 C-MFA approach determines enzymatic rates at a specific growth condition experimentally, its resultant flux values are more precise than the prediction results of FBA.…”