Although rational design-based metabolic engineering has been applied widely to obtain promising microbial biocatalysts, conventional strategies such as adaptive laboratory evolution (ALE) and mutagenesis are still efficient approaches to improve microorganisms for exceptional features such as a broad spectrum of substrate utilization, robustness of cell growth, as well as high titer, yield, and productivity of the target products. In this chapter, we describe the procedure to generate mutant strains with desired phenotypes using ALE and a new mutagenesis approach of Atmosphere and Room Temperature Plasma (ARTP). In addition, we discuss the methodology to combine next-generation sequencing (NGS)-based genome-resequencing and RNA-Seq transcriptomics approaches to characterize the mutant strains and connect the phenotypes with their corresponding genotypic changes.We demonstrate a selection of network and machine learning techniques useful in the analysis of complex datasets, including 2-way similarity networks, Markov clustering, enrichment statistical networks, FCROS differential analysis, and random forests. We demonstrate each of these techniques on the Populus trichocarpa gene expression atlas.Metabolic flux analysis represents an essential perspective to understand cellular physiology and offers quantitative information to guide pathway engineering. A valuable approach for experimental elucidation of metabolic flux is dynamic flux analysis, which estimates the relative or absolute flow rates through a series of metabolic intermediates in a given pathway. It is based on kinetic isotope labeling experiments, liquid chromatography-mass spectrometry (LC-MS), and computational analysis that relate kinetic isotope trajectories of metabolites to pathway activity. Herein, we illustrate the mathematic principles underlying the dynamic flux analysis and mainly focus on describing the experimental procedures for data generation. This protocol is exemplified using cyanobacterial metabolism as an example, for which reliable labeling data for central carbon metabolites can be acquired quantitatively. This protocol is applicable to other microbial systems as well and can be readily adapted to address different metabolic processes.As genetic engineering of organisms has grown easier and more precise, computational modeling of metabolic systems has played an increasingly important role in both guiding experimental interventions and in understanding the results of metabolic perturbations.Thermophilic microbes are an attractive bioproduction platform due to their inherently lower contamination risk and their ability to perform thermostable enzymatic processes which may be required for biomass processing and other industrial applications. The engineering of microbes for industrial scale processes requires a suite of genetic engineering tools to optimize existing biological systems as well as to design and incorporate new metabolic pathways within strains. Yet, such tools are often lacking and/or inadequate for novel microbes, especially thermophiles. This chapter focuses on genetic tool development and engineering strategies, in addition to challenges, for thermophilic microbes. We provide detailed instructions and techniques for tool development for an anaerobic thermophile, Caldanaerobacter subterraneus subsp. tengcongensis, including culturing, plasmid construction, transformation, and selection. This establishes a foundation for advanced genetic tool development necessary for the metabolic engineering of this microbe and potentially other thermophilic organisms.Understanding the performance of key metabolic enzymes is critical to metabolic engineering. It is important to know the kinetic parameters of both native enzymes and heterologously expressed enzymes that play key roles in pathway performance (Zeldes et al., Front Microbiol 61209, 2015; Keller et al., Metab Eng 27101-106, 2015). This step cannot be overlooked as gene expression is not always a good indicator of the production of fully active enzymes, especially those requiring cofactor assembly and processing (Zeldes et al., Front Microbiol 61209, 2015; Chandrayan et al., J Biol Chem 2873257-3264, 2012; Basen et al., MBio 3e00053-e00012, 2012). https://www.selleckchem.com/products/zinc05007751.html Additionally, knowing kinetic parameters and having accurate and reproducible assays allows for the use of powerful computational and in vitro pathway optimization tools that can inform metabolic engineering efforts that in turn can lead to improvements in pathway performance (Keller et al., Metab Eng 27101-106, 2015; Copeland et al., Metab Eng 14270-280, 2012). To take full advantage of these tools, understanding the roles of both enzymes directly involved in a pathway of interest, together with those in related pathways that may syphon off key intermediates, is ideal (Keller et al., Metab Eng 27101-106, 2015; Thorgersen et al., Metab Eng 2283-88; Lin et al., Metab Engi 3144-52, 2015).The metabolic enzymes like any enzymes generally display globular architecture where secondary structure elements and interactions between them preserve the spatial organization of the protein. A typical enzyme features a well-defined active site, designed for selective binding of the reaction substrate and facilitating a chemical reaction converting the substrate into a product. While many chemical reactions could be facilitated using only the functional groups that are found in proteins, the large percentage or intracellular reactions require use of cofactors, varying from single metal ions to relatively large molecules like numerous coenzymes, nucleotides and their derivatives, dinucleotides or hemes. Quite often these large cofactors become important not only for the catalytic function of the enzyme but also for the structural stability of it, as those are buried deep in the enzyme.Biomass can be converted to various types of products in biological and metabolic processes. For an in-depth understanding of biomass conversion, quantitative and qualitative information of products in these conversion processes are essential. Here we introduce analytical techniques including high-performance liquid chromatography (HPLC), gas chromatography (GC), gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR) for biomass-based products characterization in biological and metabolic processes.