Advances in metabolic engineering and fermentation for 3-hydroxypropionic acid biosynthesis: a comprehensive review.

A fundamental challenge in Systems Biology is whether a cell-scale metabolic model can predict patterns of genome evolution by realistically accounting for associated biochemical constraints. Here, we study the order in which genes are lost in an in silico evolutionary process, leading from the metabolic network of Escherichia coli to that of the endosymbiont Buchnera aphidicola. We examine how this order correlates with the order by which the genes were actually lost, as estimated from a phylogenetic reconstruction. By optimizing this correlation across the space of potential growth and biomass conditions, we compute an upper bound estimate on the model's prediction accuracy (R=0.54). The model's network-based predictive ability outperforms predictions obtained using genomic features of individual genes, reflecting the effect of selection imposed by metabolic stoichiometric constraints. Thus, while the timing of gene loss might be expected to be a completely stochastic evolutionary process, remarkably, we find that metabolic considerations, on their own, make a marked 40% contribution to determining when such losses occur.

Advanced Strategies for Production of Natural Products in Yeast.

Flux Balance Analysis for Media Optimization and Genetic Targets to Improve Heterologous Siderophore Production.

Engineering Static and Dynamic Control of Synthetic Pathways

Cocoa butter is extracted from cocoa beans, and it is mainly used as the raw material for the production of chocolate and cosmetics. Increased demands and insufficient cocoa plants led to a shortage of cocoa butter supply, and there is therefore much interesting in finding an alternative cocoa butter supply. However, the most valuable component of cocoa butter is rarely available in other vegetable oils. Saccharomyces cerevisiae is an important industrial host for production of chemicals, enzyme and pharmaceuticals. Advances in synthetical biology and metabolic engineering had enabled high-level of triacylglycerols (TAG) production in yeast, which provided possible solutions for cocoa butter equivalents (CBEs) production. Diverse engineering strategies focused on the fatty acid-producing pathway had been applied in S. cerevisiae, and the key enzymes determining the TAG structure were considered as the main engineering targets. Recent development in phytomics and multi-omics technologies provided clues to identify potential targeted enzymes, which are responsible for CBE production. In this review, we have summarized recent progress in identification of the key plant enzymes for CBE production, and discussed recent and future metabolic engineering and synthetic biology strategies for increased CBE production in S. cerevisiae.

Constraint-based metabolic control analysis for rational strain engineering

Multi-scale exploration of the technical, economic, and environmental dimensions of bio-based chemical production

BackgroundThe systems-scale analysis of cellular metabolites, “metabolomics,” provides data ideal for applications in metabolic engineering. However, many of the computational tools for strain design are built around Flux Balance Analysis (FBA), which makes assumptions that preclude direct integration of metabolomics data into the underlying models. Finding a way to retain the advantages of FBA’s linear structure while relaxing some of its assumptions could allow us to account for metabolite levels and metabolite-dependent regulation in strain design tools built from FBA, improving the accuracy of predictions made by these tools. We designed, implemented, and characterized a modeling strategy based on Dynamic FBA (DFBA), called Linear Kinetics-Dynamic Flux Balance Analysis (LK-DFBA), to satisfy these specifications. Our strategy adds constraints describing the dynamics and regulation of metabolism that are strictly linear. We evaluated LK-DFBA against alternative modeling frameworks using simulated noisy data from a small in silico model and a larger model of central carbon metabolism in E. coli, and compared each framework’s ability to recapitulate the original system.ResultsIn the smaller model, we found that we could use regression from a dynamic flux estimation (DFE) with an optional non-linear parameter optimization to reproduce metabolite concentration dynamic trends more effectively than an ordinary differential equation model with generalized mass action rate laws when tested under realistic data sampling frequency and noise levels. We observed detrimental effects across all tested modeling approaches when metabolite time course data were missing, but found these effects to be smaller for LK-DFBA in most cases. With the E. coli model, we produced qualitatively reasonable results with similar properties to the smaller model and explored two different parameterization structures that yield trade-offs in computation time and accuracy.ConclusionsLK-DFBA allows for calculation of metabolite concentrations and considers metabolite-dependent regulation while still retaining many computational advantages of FBA. This provides the proof-of-principle for a new metabolic modeling framework with the potential to create genome-scale dynamic models and the potential to be applied in strain engineering tools that currently use FBA.

Metabolic engineering — Integrating methodologies of molecular breeding and bioprocess systems engineering

Metabolic Engineering. Integrating Methodologies of Molecular Breeding and Bioprocess Systems Engineering.

Confronted with the gradual and inescapable exhaustion of the earth's fossil energy resources, the bio-based process to produce platform chemicals from renewable carbohydrates is attracting growing interest. Escherichia coli has been chosen as a workhouse for the production of many valuable chemicals due to its clear genetic background, convenient to be genetically modified and good growth properties with low nutrient requirements. Rational strain development of E. coli achieved by metabolic engineering strategies has provided new processes for efficiently biotechnological production of various high-value chemical building blocks. Compared to previous reviews, this review focuses on recent advances in metabolic engineering of the industrial model bacteria E. coli that lead to efficient recombinant biocatalysts for the production of high-value organic acids like succinic acid, lactic acid, 3-hydroxypropanoic acid and glucaric acid as well as alcohols like 1,3-propanediol, xylitol, mannitol, and glycerol with the discussion of the future research in this area. Besides, this review also discusses several platform chemicals, including fumaric acid, aspartic acid, glutamic acid, sorbitol, itaconic acid, and 2,5-furan dicarboxylic acid, which have not been produced by E. coli until now.

3-Hydroxypropionic acid (3-HP) represents an economically important platform compound from which a panel of bulk chemicals can be derived. Compared with petroleum-dependent chemical synthesis, bioproduction of 3-HP has attracted more attention due to utilization of renewable biomass. This review outlines bacterial production of 3-HP, covering aspects of host strains (e.g., Escherichia coli and Klebsiella pneumoniae), metabolic pathways, key enzymes, and hurdles hindering high-level production. Inspired by the state-of-the-art advances in metabolic engineering and synthetic biology, we come up with protocols to overcome the hurdles constraining 3-HP production. The protocols range from rewiring of metabolic networks, alleviation of metabolite toxicity, to dynamic control of cell size and density. Especially, this review highlights the substantial contribution of microbial growth to 3-HP production, as we recognize the synchronization between cell growth and 3-HP formation. Accordingly, we summarize the following growth-promoting strategies: (i) optimization of fermentation conditions; (ii) construction of gene circuits to alleviate feedback inhibition; (iii) recruitment of RNA polymerases to overexpress key enzymes which in turn boost cell growth and 3-HP production. Lastly, we propose metabolic engineering approaches to simplify downstream separation and purification. Overall, this review aims to portray a picture of bacterial production of 3-HP.

3-Hydroxypropionic acid (3-HP) is a commercially valuable platform chemical from which an array of C3 compounds can be generated. Klebsiella pneumoniae has been considered a promising species for biological production of 3-HP. Despite a wealth of reports related to 3-HP biosynthesis in K. pneumoniae, its commercialization is still in infancy. The major hurdle hindering 3-HP overproduction lies in the poor understanding of glycerol dissimilation in K. pneumoniae. To surmount this problem, this review aims to portray a picture of 3-HP biosynthesis, involving 3-HP-synthesizing strains, biochemical attributes, metabolic pathways and key enzymes. Inspired by the state-of-the-art advances in metabolic engineering and synthetic biology, here we advocate protocols for overproducing 3-HP in K. pneumoniae. These protocols range from cofactor regeneration, alleviation of metabolite toxicity, genome editing, remodeling of transport system, to carbon flux partition via logic gate. The feasibility for these protocols was also discussed.

A guide to metabolic flux analysis in metabolic engineering: Methods, tools and applications

Mathematical models of the cellular metabolism have become an essential tool for the optimization of biotechnological processes. They help to obtain a systemic understanding of the metabolic processes in the used microorganisms and to find suitable genetic modifications maximizing the production performance. In particular, methods of stoichiometric and constraint-based modeling are frequently used in the context of metabolic and bioprocess engineering. Since metabolic networks can be complex and comprise hundreds or even thousands of metabolites and reactions, dedicated software tools are required for an efficient analysis. One such software suite is CellNetAnalyzer, a MATLAB package providing, among others, various methods for analyzing stoichiometric and constraint-based metabolic models. CellNetAnalyzer can be used via command-line based operations or via a graphical user interface with embedded network visualizations. Herein we will present key functionalities of CellNetAnalyzer for applications in biotechnology and metabolic engineering and thereby review constraint-based modeling techniques such as metabolic flux analysis, flux balance analysis, flux variability analysis, metabolic pathway analysis (elementary flux modes) and methods for computational strain design.