The use of spectroscopic sensors for bioprocess monitoring is a powerful tool within the process analytical technology (PAT) initiative of the US Food and Drug Administration. Spectroscopic sensors enable the simultaneous real-time bioprocess monitoring of various critical process parameters including biological, chemical, and physical variables during the entire biotechnological production process. This potential can be realized through the combination of spectroscopic measurements (UV/Vis spectroscopy, IR spectroscopy, fluorescence spectroscopy, and Raman spectroscopy) with multivariate data analysis to obtain relevant process information out of an enormous amount of data. This review summarizes the newest results from science and industry after the establishment of the PAT initiative and gives a critical overview of the most common in-line spectroscopic techniques. Examples are provided of the wide range of possible applications in upstream processing and downstream processing of spectroscopic sensors for real-time monitoring to optimize productivity and ensure product quality in the pharmaceutical industry.
Flow cytometry is a general method for rapidly analyzing large numbers of cells individually using light-scattering, fluorescence, and absorbence measurements. The power of this method lies both in the wide range of cellular parameters that can be determined and in the ability to obtain information on how these parameters are distributed in the cell population. Flow cytometric assays have been developed to determine both cellular characteristics such as size, membrane potential, and intracellular pH, and the levels of cellular components such as DNA, protein, surface receptors, and calcium. Measurements that reveal the distribution of these parameters in cell populations are important for biotechnology, because they better describe the population than the average values obtained from traditional techniques. This Mini-Review provides an overview of the principles of flow cytometry, with descriptions of methods used to measure various cellular parameters and examples of the application of flow cytometry in biotechnology. Finally, a discussion of the challenges and limitations of the method is presented along with a future outlook.
Cyanobacteria are photosynthetic bacteria notable for their ability to produce hydrogen and a variety of interesting secondary metabolites. As a result of the growing number of completed cyanobacterial genome projects, the development of post-genomics analysis for this important group has been accelerating. DNA microarrays and classical two-dimensional gel electrophoresis (2DE) were the first technologies applied in such analyses. In many other systems, 'shotgun' proteomics employing multi-dimensional liquid chromatography and tandem mass spectrometry has proven to be a powerful tool. However, this approach has been relatively under-utilized in cyanobacteria. This study assesses progress in cyanobacterial shotgun proteomics to date, and adds a new perspective by developing a protocol for the shotgun proteomic analysis of the filamentous cyanobacterium Anabaena variabilis ATCC 29413, a model for N(2) fixation. Using approaches for enhanced protein extraction, 646 proteins were identified, which is more than double the previous results obtained using 2DE. Notably, the improved extraction method and shotgun approach resulted in a significantly higher representation of basic and hydrophobic proteins. The use of protein bioinformatics tools to further mine these shotgun data is illustrated through the application of PSORTb for localization, the grand average hydropathy (GRAVY) index for hydrophobicity, LipoP for lipoproteins and the exponentially modified protein abundance index (emPAI) for abundance. The results are compared with the most well-studied cyanobacterium, Synechocystis sp. PCC 6803. Some general issues in shotgun proteome identification and quantification are then addressed.
Microbial growth on pollutant mixtures is an important aspect of bioremediation and wastewater treatment. However, efforts to develop mathematical models for mixed substrate kinetics have been limited. Nearly all models group either the microbial population (as "biomass") or the chemical species (e.g., as biological oxygen demand). When individual chemical species are considered, most models assume either no interaction or that the nature of the interaction is competition for the same rate-limiting enzyme. And when individual microbial species are considered, simple competition for the growth substrate is the only interaction included. Here, we present results using Pseudomonas putida F1 and Burkholderia sp. strain JS150 growing individually and together on benzene, toluene, phenol, and their mixtures and compare mathematical models to describe these results. We demonstrate that the simple models do not accurately predict the outcome of these biodegradation experiments, and we describe the development of a new model for substrate mixtures, the sum kinetics with interaction parameters (SKIP) model. In mixed-culture experiments, the interactions between species were substrate dependent and could not be predicted by simple competition models. Together, this set of experimental and modeling results presents our current state of work in this area and identifies challenges for future modeling efforts.
While microalgae are a promising feedstock for production of fuels and other chemicals, a challenge for the algal bioproducts industry is obtaining consistent, robust algae growth. Algal cultures include complex bacterial communities and can be difficult to manage because specific bacteria can promote or reduce algae growth. To overcome bacterial contamination, algae growers may use closed photobioreactors designed to reduce the number of contaminant organisms. Even with closed systems, bacteria are known to enter and cohabitate, but little is known about these communities. Therefore, the richness, structure, and composition of bacterial communities were characterized in closed photobioreactor cultivations of in F/2 medium at different scales, across nine months spanning late summer-early spring, and during a sequence of serially inoculated cultivations. Using sequence data from 275 samples, bacterial communities in small, medium, and large cultures were shown to be significantly different. Larger systems contained richer bacterial communities compared to smaller systems. Relationships between bacterial communities and algae growth were complex. On one hand, blooms of a specific bacterial type were observed in three abnormal, poorly performing replicate cultivations, while on the other, notable changes in the bacterial community structures were observed in a series of serial large-scale batch cultivations that had similar growth rates. Bacteria common to the majority of samples were identified, including a single OTU within the class that was found in all samples. This study contributes important information for crop protection in algae systems, and demonstrates the complex ecosystems that need to be understood for consistent, successful industrial algae cultivation. This is the first study to profile bacterial communities during the scale-up process of industrial algae systems.
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