To contribute to the growing interest in using Raman spectroscopy to analyze biological samples and provide chemometric analysis, we have developed a Raman Chemometrics (Rametrix™) Toolbox for use with MATLAB®. The LITE version of the Rametrix™ Toolbox is free to academic users through GitHub (https://github.com/SengerLab/RametrixLITEToolbox) and provides a graphical user interface for application of the following to Raman spectra: baseline correction with the Goldindec algorithm, vector or specific band normalization, principal component analysis (PCA), discriminant analysis of principal components (DAPC), identification of wavenumber loadings for PCA and DAPC, and calculation of total canonical distance. Raman spectroscopy and analysis with the Rametrix™ LITE Toolbox were applied to generate calibration curves, monitor enzymatic reactions, and track Escherichia coli culture growth. Results were quantitatively consistent with traditional methods of analysis. Additionally, the ability to distinguish urine specimens from healthy individuals and from patients receiving treatment for chronic kidney disease through peritoneal dialysis was demonstrated using PCA and DAPC of Raman spectra, suggesting future applications to detect or monitor progression of the disease. Overall, the Rametrix™ LITE Toolbox provides a streamlined application of PCA and DAPC chemometric techniques, and total canonical distance offers an additional quantitative measure to interpret Raman spectra of biological samples.
Microbial cell factories (MCFs) are of considerable interest to convert low value renewable substrates to biofuels and high value chemicals. This review highlights the progress of computational models for the rational design of an MCF to produce a target bio-commodity. In particular, the rational design of an MCF involves: (i) product selection, (ii) de novo biosynthetic pathway identification (i.e., rational, heterologous, or artificial), (iii) MCF chassis selection, (iv) enzyme engineering of promiscuity to enable the formation of new products, and (v) metabolic engineering to ensure optimal use of the pathway by the MCF host. Computational tools such as (i) de novo biosynthetic pathway builders, (ii) docking, (iii) molecular dynamics (MD) and steered MD (SMD), and (iv) genome-scale metabolic flux modeling all play critical roles in the rational design of an MCF. Genome-scale metabolic flux models are of considerable use to the design process since they can reveal metabolic capabilities of MCF hosts. These can be used for host selection as well as optimizing precursors and cofactors of artificial de novo biosynthetic pathways. In addition, recent advances in genome-scale modeling have enabled the derivation of metabolic engineering strategies, which can be implemented using the genomic tools reviewed here as well.
New in silico tools that make use of genome-scale metabolic flux modeling are improving the design of metabolic engineering strategies. This review highlights the latest developments in this area, explains the interface between these in silico tools and the experimental implementation tools of metabolic engineers, and provides a way forward so that in silico predictions can better mimic reality and more experimental methods can be considered in simulation studies. The several methodologies for solving genome-scale models (eg, flux balance analysis [FBA], parsimonious FBA, flux variability analysis, and minimization of metabolic adjustment) all have unique advantages and applications. There are two basic approaches to designing metabolic engineering strategies in silico, and both have demonstrated success in the literature. The first involves: 1) making a genetic manipulation in a model; 2) testing for improved performance through simulation; and 3) iterating the process. The second approach has been used in more recently designed in silico tools and involves: 1) comparing metabolic flux profiles of a wild-type and ideally engineered state and 2) designing engineering strategies based on the differences in these flux profiles. Improvements in genome-scale modeling are anticipated in areas such as the inclusion of all relevant cellular machinery, the ability to understand and anticipate the results of combinatorial enrichment experiments, and constructing dynamic and flexible biomass equations that can respond to environmental and genetic manipulations. A brief introduction to genome-scale metabolic flux modelingA "genome-scale" metabolic flux model (GEM) consists of a network of biochemical reactions that is reconstructed based on the genomic sequence and annotation of a cell. Assuming a "steady-state" metabolism (ie, a snapshot of metabolism at one time point) is reached on a short time-scale, these reactions can be represented by a linear system of equations. Then, problems such as maximizing specific chemical production or growth can be solved efficiently by linear programming. GEMs and their uses have been reviewed thoroughly, and they are most basically used to predict reaction flux, which is the overall rate of metabolite conversion. 1,2 Often, laboratory measurements including the rates of substrate consumption, product formation, and growth are used as model constraints so calculations coincide with observations. Other model constraints can be derived from reaction thermodynamics, 3 cellular regulatory networks, 4 and -omics datasets. 5 GEMs have been constructed and utilized for intensively
Lambda-polymerase chain reaction (λ-PCR) is a novel and open-source method for DNA assembly and cloning projects. λ-PCR uses overlap extension to ultimately assemble linear and circular DNA fragments, but it allows the singlestranded DNA (ssDNA) primers of the PCR extension to first exist as double-stranded DNA (dsDNA). Having dsDNA at this step is advantageous for the stability of large insertion products, to avoid inhibitory secondary structures during direct synthesis, and to reduce costs. Three variations of λ-PCR were created to convert an initial dsDNA product into an ssDNA "megaprimer" to be used in overlap extension: (i) complete digestion by λ-exonuclease, (ii) asymmetric PCR, and (iii) partial digestion by λ-exonuclease.Four case studies are presented that demonstrate the use of λ-PCR in simple gene cloning, simultaneous multipart assemblies, gene cloning not achievable with commercial kits, and the use of thermodynamic simulations to guide λ-PCR assembly strategies. High DNA assembly and cloning efficiencies have been achieved with λ-PCR for a fraction of the cost and time associated with conventional methods and some commercial kits.
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