Towards systems metabolic engineering of microorganisms for amino acid production

Microorganisms capable of efficient production of amino acids have traditionally been developed by random mutation and selection method, which might cause unwanted physiological changes in cellular metabolism. Rational genome-wide metabolic engineering based on systems and synthetic biology tools, which is termed 'systems metabolic engineering', is rising as an alternative to overcome these problems. Recently, several amino acid producers have been successfully developed by systems metabolic engineering, where the metabolic engineering procedures were performed within a systems biology framework, and entire metabolic networks, including complex regulatory circuits, were engineered in an integrated manner. Here we review the current status of systems metabolic engineering successfully applied for developing amino acid producing strains and discuss future prospects.     

Enabling inverse metabolic engineering through genomics

Inverse metabolic engineering (IME) is a powerful framework for engineering cellular phenotypes. Progress in this field has been limited by a lack of comprehensive methods for efficiently identifying the genetic basis of relevant phenotypes. Advances in genomics technologies, including DNA microarrays and gene sequencing, have dramatically improved our ability to relate changes in phenotype with associated changes in genotype. When applied in the context of IME, these tools should enable the integration of "evolutionary" and "direct" approaches to engineering cell physiology, which should improve our understanding of the complex interactions affecting the expression, evolution and engineering of traits in natural and industrial hosts.     

Systems metabolic engineering: Genome-scale models and beyond

The advent of high throughput genome-scale bioinformatics has led to an exponential increase in available cellular system data. Systems metabolic engineering attempts to use data-driven approaches – based on the data collected with high throughput technologies – to identify gene targets and optimize phenotypical properties on a systems level. Current systems metabolic engineering tools are limited for predicting and defining complex phenotypes such as chemical tolerances and other global, multigenic traits. The most pragmatic systems-based tool for metabolic engineering to arise is the in silico genome-scale metabolic reconstruction. This tool has seen wide adoption for modeling cell growth and predicting beneficial gene knockouts, and we examine here how this approach can be expanded for novel organisms. This review will highlight advances of the systems metabolic engineering approach with a focus on de novo development and use of genome-scale metabolic reconstructions for metabolic engineering applications. We will then discuss the challenges and prospects for this emerging field to enable model-based metabolic engineering. Specifically, we argue that current state-of-the-art systems metabolic engineering techniques represent a viable first step for improving product yield that still must be followed by combinatorial techniques or random strain mutagenesis to achieve optimal cellular systems.     

Modeling and simulation: tools for metabolic engineering.

Mathematical modeling is one of the key methodologies of metabolic engineering. Based on a given metabolic model different computational tools for the simulation, data evaluation, systems analysis, prediction, design and optimization of metabolic systems have been developed. The currently used metabolic modeling approaches can be subdivided into structural models, stoichiometric models, carbon flux models, stationary and nonstationary mechanistic models and models with gene regulation. However, the power of a model strongly depends on its basic modeling assumptions, the simplifications made and the data sources used. Model validation turns out to be particularly difficult for metabolic systems. The different modeling approaches are critically reviewed with respect to their potential and benefits for the metabolic engineering cycle. Several tools that have emerged from the different modeling approaches including structural pathway synthesis, stoichiometric pathway analysis, metabolic flux analysis, metabolic control analysis, optimization of regulatory architectures and the evaluation of rapid sampling experiments are discussed.     

Mapping phenotypic landscapes using DNA micro-arrays

Inverse metabolic engineering is a useful approach for engineering phenotypes in biological systems. The overarching objective of this approach is to combine the power of evolutionary engineering approaches with the precision of constructive metabolic engineering strategies. Often the difficulty in this approach is elucidating the genetic basis of the phenotypes that emerge as a result of evolutionary mechanisms. As a result of advances in genomics technologies, several techniques now exist that substantially improve researchers ability to identify such genes. Metabolic engineers now have the ability to map phenotypic landscapes of considerable genetic diversity, which should improve understanding of the relationships that exist among phenotype, genotype, and environment. In this mini-review, we will discuss several of such genomics tools that may be useful in developing inverse metabolic engineering strategies and, in particular, mapping phenotypic landscapes.     

Means, Advantages and Limits of Merging Biology with Technology

The natural world spent billions of years in solution-finding during evolution, which could benefit Technology. How do we put that in a nutshell? Biological systems are more complex than the most complex current technology. Any given functiofi and effect are simultaneously coordinated and linked with others at many levels of biological organisation-from cell organelle to organism, to population and ecosystem. Technology does not have tools to deal with the complexity and “goalintendedness“ of living systems. But limits for interaction exist on both sides-Biological science itself is also too empirical and not mature enough to provide a solid base for correlating living with technical systems. Moving towards a synthesis, where engineers can utilize the vast amount of available biological data, we suggest using a tool called “Theory of Inventive Problem Solving“ (TRIZ) and clarifying some important methodological issues, which have not previously been recognised in bionic engineering: 1) Requirement for more appropriate definitions of “system“, “effect“, “function“, “law“ and “rule“. 2) Requirement for understanding or even measuring the degree of contradiction or analogy between functions in biological and artificial and/or non-living engineering system-there is no simple direct correlation between what engineers find useful and what biology does.     

1H-NMR-based metabolomics for cancer targeting and metabolic engineering –A review

Nuclear magnetic resonance (NMR) spectroscopy acts as the best tool that can be used in tissue engineering scaffolds to investigate unknown metabolites. Moreover, metabolomics is a systems approach for examining in vivo and in vitro metabolic profiles, which promises to provide data on cancer metabolic alterations. However, metabolomic profiling allows for the activity of small molecules and metabolic alterations to be measured. Furthermore, metabolic profiling also provides high-spectral resolution, which can then be linked to potential metabolic relationships. An altered metabolism is a hallmark of cancer that can control many malignant properties to drive tumorigenesis. Metabolite targeting and metabolic engineering contribute to carcinogenesis by proliferation, and metabolic differentiation. The resulting the metabolic differences are examined with traditional chemometric methods such as principal component analysis (PCA), and partial least squares-discriminate analysis (PLS-DA). In this review, we examine NMR-based activity metabolomic platforms that can be used to analyze various fluxomics and for multivariant statistical analysis in cancer. We also aim to provide the reader with a basic understanding of NMR spectroscopy, cancer metabolomics, target profiling, chemometrics, and multifunctional tools for metabolomics discrimination, with a focus on metabolic phenotypic diversity for cancer therapeutics.     

Computational tools for metabolic engineering

A great variety of software applications are now employed in the metabolic engineering field. These applications have been created to support a wide range of experimental and analysis techniques. Computational tools are utilized throughout the metabolic engineering workflow to extract and interpret relevant information from large data sets, to present complex models in a more manageable form, and to propose efficient network design strategies. In this review, we present a number of tools that can assist in modifying and understanding cellular metabolic networks. The review covers seven areas of relevance to metabolic engineers. These include metabolic reconstruction efforts, network visualization, nucleic acid and protein engineering, metabolic flux analysis, pathway prospecting, post-structural network analysis and culture optimization. The list of available tools is extensive and we can only highlight a small, representative portion of the tools from each area.     

A generalized computational framework to streamline thermodynamics and kinetics analysis of metabolic pathways

Metabolic engineering is a critical biotechnological approach in addressing global energy and environment challenges. Most engineering efforts, however, consist of laborious and inefficient trial-and-error of target pathways, due in part to the lack of methodologies that can comprehensively assess pathway properties in thermodynamics and kinetics. Metabolic engineering can benefit from computational tools that evaluate feasibility, expense and stability of non-natural metabolic pathways. Such tools can also help us understand natural pathways and their regulation at systems level. Here we introduce a computational toolbox, PathParser, which, for the first time, integrates multiple important functions for pathway analysis including thermodynamics analysis, kinetics-based protein cost optimization and robustness analysis. Specifically, PathParser enables optimization of the driving force of a pathway by minimizing the Gibbs free energy of least thermodynamically favorable reaction. In addition, based on reaction thermodynamics and enzyme kinetics, it can compute the minimal enzyme protein cost that supports metabolic flux, and evaluate pathway stability and flux in response to enzyme concentration perturbations. In a demo analysis of the Calvin–Benson–Bassham cycle and photorespiration pathway in the model cyanobacterium Synechocystis PCC 6803, the computation results are corroborated by experimental proteomics data as well as metabolic engineering outcomes. This toolbox may have broad application in metabolic engineering and systems biology in other microbial systems.     

Toward systems metabolic engineering of Aspergillus and Pichia species for the production of chemicals and biofuels

Recently genome sequence data have become available for Aspergillus and Pichia species of industrial interest. This has stimulated the use of systems biology approaches for large-scale analysis of the molecular and metabolic responses of Aspergillus and Pichia under defined conditions, which has resulted in much new biological information. Case-specific contextualization of this information has been performed using comparative and functional genomic tools. Genomics data are also the basis for constructing genome-scale metabolic models, and these models have helped in the contextualization of knowledge on the fundamental biology of Aspergillus and Pichia species. Furthermore, with the availability of these models, the engineering of Aspergillus and Pichia is moving from traditional approaches, such as random mutagenesis, to a systems metabolic engineering approach. Here we review the recent trends in systems biology of Aspergillus and Pichia species, highlighting the relevance of these developments for systems metabolic engineering of these organisms for the production of hydrolytic enzymes, biofuels and chemicals from biomass.     

Metabolic pathways and fermentative production of L-aspartate family amino acids

The L -aspartate family amino acids (AFAAs), L -threonine, L -lysine, L -methionine and L -isoleucine have recently been of much interest due to their wide spectrum of applications including food additives, components of cosmetics and therapeutic agents, and animal feed additives. Among them, L -threonine, L -lysine and L -methionine are three major amino acids produced currently throughout the world. Recent advances in systems metabolic engineering, which combine various high-throughput omics technologies and computational analysis, are now facilitating development of microbial strains efficiently producing AFAAs. Thus, a thorough understanding of the metabolic and regulatory mechanisms of the biosynthesis of these amino acids is urgently needed for designing system-wide metabolic engineering strategies. Here we review the details of AFAA biosynthetic pathways, regulations involved, and export and transport systems, and provide general strategies for successful metabolic engineering along with relevant examples. Finally, perspectives of systems metabolic engineering for developing AFAA overproducers are suggested with selected exemplary studies.     

Metabolic engineering in the -omics era: elucidating and modulating regulatory networks.

The importance of regulatory control in metabolic processes is widely acknowledged, and several enquiries (both local and global) are being made in understanding regulation at various levels of the metabolic hierarchy. The wealth of biological information has enabled identifying the individual components (genes, proteins, and metabolites) of a biological system, and we are now in a position to understand the interactions between these components. Since phenotype is the net result of these interactions, it is immensely important to elucidate them not only for an integrated understanding of physiology, but also for practical applications of using biological systems as cell factories. We present some of the recent "-omics" approaches that have expanded our understanding of regulation at the gene, protein, and metabolite level, followed by analysis of the impact of this progress on the advancement of metabolic engineering. Although this review is by no means exhaustive, we attempt to convey our ideology that combining global information from various levels of metabolic hierarchy is absolutely essential in understanding and subsequently predicting the relationship between changes in gene expression and the resulting phenotype. The ultimate aim of this review is to provide metabolic engineers with an overview of recent advances in complementary aspects of regulation at the gene, protein, and metabolite level and those involved in fundamental research with potential hurdles in the path to implementing their discoveries in practical applications.     

Metabolic engineering of flavonoids in plants and microorganisms

Over 9,000 flavonoid compounds have been found in various plants, comprising one of the largest families of natural products. Flavonoids are an essential factor in plant interactions with the environment, often serving as the first line of defense against UV irradiation and pathogen attacks. Flavonoids are also major nutritional compounds in foods and beverages, with demonstrated health benefits. Some flavonoids are potent antioxidants, and specific flavonoid compounds are beneficial in many physiological and pharmacological processes. Therefore, engineering of flavonoid biosynthesis in plants or in microorganisms has significant scientific and economical importance. Construction of biosynthetic pathways in heterologous systems offers promising results for large-scale flavonoid production by fermentation or bioconversion. Genomics and metabolomics now offer unprecedented tools for detailed understanding of the engineered transgenic organism and for developing novel technologies to further increase flavonoid production yields. We summarize some of the recent metabolic engineering strategies in plants and microorganisms, with a focus on applications of metabolic flux analysis. We are confident that these engineering approaches will lead to successful industrial flavonoid production in the near future.     

Database and tools for metabolic network analysis

Metabolic network analysis has attracted much attention in the area of systems biology. It has a profound role in understanding the key features of organism metabolic networks and has been successfully applied in several fields of systems biology, including in silico gene knockouts, production yield improvement using engineered microbial strains, drug target identification, and phenotype prediction. A variety of metabolic network databases and tools have been developed in order to assist research in these fields. Databases that comprise biochemical data are normally integrated with the use of metabolic network analysis tools in order to give a more comprehensive result. This paper reviews and compares eight databases as well as twenty one recent tools. The aim of this review is to study the different types of tools in terms of the features and usability, as well as the databases in terms of the scope and data provided. These tools can be categorised into three main types: standalone tools; toolbox-based tools; and web-based tools. Furthermore, comparisons of the databases as well as the tools are also provided to help software developers and users gain a clearer insight and a better understanding of metabolic network analysis. Additionally, this review also helps to provide useful information that can be used as guidance in choosing tools and databases for a particular research interest.     

The path to next generation biofuels: successes and challenges in the era of synthetic biology

Volatility of oil prices along with major concerns about climate change, oil supply security and depleting reserves have sparked renewed interest in the production of fuels from renewable resources. Recent advances in synthetic biology provide new tools for metabolic engineers to direct their strategies and construct optimal biocatalysts for the sustainable production of biofuels. Metabolic engineering and synthetic biology efforts entailing the engineering of native and de novo pathways for conversion of biomass constituents to short-chain alcohols and advanced biofuels are herewith reviewed. In the foreseeable future, formal integration of functional genomics and systems biology with synthetic biology and metabolic engineering will undoubtedly support the discovery, characterization, and engineering of new metabolic routes and more efficient microbial systems for the production of biofuels.     

Synthetic biology advancing clinical applications

The 'omics' era, with its identification of genetic and protein components, has combined with systems biology, which provided insights into network structures, to set the stage for synthetic biology, an emerging interdisciplinary life science that uses engineering principles. By capitalizing on an iterative design cycle that involves molecular and computational biology tools to assemble functional designer devices from a comprehensive catalogue of standardized biological components with predictable functions, synthetic biology has significantly advanced our understanding of complex control dynamics that program living systems. Such insights, collected over the past decade, are priming a variety of synthetic biology-inspired biomedical applications that have the potential to revolutionize drug discovery and production technologies, as well as treatment strategies for infectious diseases and metabolic disorders.