Systems Metabolic Engineering

Cover illustration Special Issue: Systems Metabolic Engineering. Metabolic engineering combines a mix of approaches, including in silico modeling, omics studies, synthetic biology and protein engineering to improve microorganism strains for increased yields and reduced production costs of desirable chemicals. Such an achievement is exemplified on this Special Issue's cover, which shows an electron microscopy image of Corynebacterium glutamicum that has been engineered to produce a sustainable bio-nylon monomer from hemicellulose sugar found in the cell walls of plants. Image provided by Buschke et al.     

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data‐Driven Metabolic Engineering

The recent increase in high‐throughput capacity of ‘omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data‐driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of ‘omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data‐driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system‐scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.     

代谢工程

代谢工程,也称途径工程,是基因工程一个重要分支,一般是多基因的基因工程,与细胞的基因调控、代谢调控和生化工程密切相关。讨论了代谢工程的应用,包括通过改变代谢流和代谢途径提高产量,改善生产过程,构建新的代谢途径和产生新的代谢产物等。     

Systems Metabolic Engineering Strategies for Non‐Natural Microbial Polyester Production

Plastics, used everyday, are mostly synthetic polymers derived from fossil resources, and their accumulation is becoming a serious concern worldwide. Polyhydroxyalkanoates (PHAs) are naturally produced polyesters synthesized and intracellularly accumulated by many different microorganisms. PHAs are good alternatives to petroleum‐based plastics because they possess a wide range of material properties depending on monomer types and molecular weights. In addition, PHAs are biodegradable and can be produced from renewable biomass. Thus, producing PHAs through the development of high‐performance engineered microorganisms and efficient bioprocesses gained much interest. In addition, non‐natural polyesters comprising 2‐hydroxycarboxylic acids as monomers have been produced by fermentation of metabolically engineered bacteria. For example, poly(lactic acid) and poly(lactic acid‐co‐glycolic acid), which have been chemically synthesized using the corresponding monomers either fermentatively or chemically produced, can be produced by metabolically engineered bacteria by one‐step fermentation. Recently, PHAs containing aromatic monomers could be produced by fermentation of metabolically engineered bacteria. Here, metabolic engineering strategies applied in developing microbial strains capable of producing non‐natural polyesters in a stepwise manner are reviewed. It is hoped that the detailed strategies described will be helpful for designing metabolic engineering strategies for developing diverse microbial strains capable of producing various polymers that can replace petroleum‐derived polymers.     

Plant Metabolic Modeling: Achieving New Insight into Metabolism and Metabolic Engineering

Models are used to represent aspects of the real world for specific purposes, and mathematical models have opened up new approaches in studying the behavior and complexity of biological systems. However, modeling is often time-consuming and requires significant computational resources for data development, data analysis, and simulation. Computational modeling has been successfully applied as an aid for metabolic engineering in microorganisms. But such model-based approaches have only recently been extended to plant metabolic engineering, mainly due to greater pathway complexity in plants and their highly compartmentalized cellular structure. Recent progress in plant systems biology and bioinformatics has begun to disentangle this complexity and facilitate the creation of efficient plant metabolic models. This review highlights several aspects of plant metabolic modeling in the context of understanding, predicting and modifying complex plant metabolism. We discuss opportunities for engineering photosynthetic carbon metabolism, sucrose synthesis, and the tricarboxylic acid cycle in leaves and oil synthesis in seeds and the application of metabolic modeling to the study of plant acclimation to the environment. The aim of the review is to offer a current perspective for plant biologists without requiring specialized knowledge of bioinformatics or systems biology.     

Systems Metabolic Engineering of Escherichia coli Improves Coconversion of Lignocellulose‐Derived Sugars

Currently, microbial conversion of lignocellulose‐derived glucose and xylose to biofuels is hindered by the fact that most microbes (including Escherichia coli [E. coli], Saccharomyces cerevisiae, and Zymomonas mobilis) preferentially consume glucose first and consume xylose slowly after glucose is depleted in lignocellulosic hydrolysates. In this study, E. coli strains are developed that simultaneously utilize glucose and xylose in lignocellulosic biomass hydrolysate using genome‐scale models and adaptive laboratory evolution. E. coli strains are designed and constructed that coutilize glucose and xylose and adaptively evolve them to improve glucose and xylose utilization. Whole‐genome resequencing of the evolved strains find relevant mutations in metabolic and regulatory genes and the mutations’ involvement in sugar coutilization is investigated. The developed strains show significantly improved coconversion of sugars in lignocellulosic biomass hydrolysates and provide a promising platform for producing next‐generation biofuels.