94 research outputs found

    Simulation techniques in energy analysis

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    Simulation is one of the most frequently used techniques in energy modelling. After some general remarks on the nature of simulation models, a more detailed description of a large scale dynamic energy simulation model for the Federal Republic of Germany is given. The paper continues with a discussion of some model results and concludes with some brief remarks on the limitations of the simulation approach

    Utilizing high-throughput experimentation to enhance specific productivity of an E.coli T7 expression system by phosphate limitation

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    <p>Abstract</p> <p>Background</p> <p>The specific productivity of cultivation processes can be optimized, amongst others, by using genetic engineering of strains, choice of suitable host/vector systems or process optimization (e.g. choosing the right induction time). A further possibility is to reduce biomass buildup in favor of an enhanced product formation, e.g. by limiting secondary substrates in the medium, such as phosphate. However, with conventional techniques (e.g. small scale cultivations in shake flasks), it is very tedious to establish optimal conditions for cell growth and protein expression, as the start of protein expression (induction time) and the degree of phosphate limitation have to be determined in numerous concerted, manually conducted experiments.</p> <p>Results</p> <p>We investigated the effect of different induction times and a concurrent phosphate limitation on the specific productivity of the T7 expression system <it>E.coli </it>BL21(DE3) pRhotHi-2-EcFbFP, which produces the model fluorescence protein EcFbFP upon induction. Therefore, specific online-monitoring tools for small scale cultivations (RAMOS, BioLector) as well as a novel cultivation platform (Robo-Lector) were used for rapid process optimization. The RAMOS system monitored the oxygen transfer rate in shake flasks, whereas the BioLector device allowed to monitor microbial growth and the production of EcFbFP in microtiter plates. The Robo-Lector is a combination of a BioLector and a pipetting robot and can conduct high-throughput experiments fully automated. By using these tools, it was possible to determine the optimal induction time and to increase the specific productivity for EcFbFP from 22% (for unlimited conditions) to 31% of total protein content of the <it>E.coli </it>cells via a phosphate limitation.</p> <p>Conclusions</p> <p>The results revealed that a phosphate limitation at the right induction time was suitable to redirect the available cellular resources during cultivation to protein expression rather than in biomass production. To our knowledge, such an effect was shown for the first time for an IPTG-inducible expression system. Finally, this finding and the utilization of the introduced high-throughput experimentation approach could help to find new targets to further enhance the production capacity of recombinant <it>E.coli</it>-strains.</p

    Switchable Gene Expression in Escherichia coli Using a Miniaturized Photobioreactor

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    We present a light-switchable gene expression system for both inducible and switchable control of gene expression at a single cell level in Escherichia coli using a previously constructed light-sensing system. The lambda cl repressor gene with an LVA degradation tag was expressed under the control of the ompC promoter on the chromosome. The green fluorescent protein (GFP) gene fused to a lambda repressor-repressible promoter was used as a reporter. This light-switchable system allows rapid and reversible induction or repression of expression of the target gene at any desired time. This system also ensures homogenous expression across the entire cell population. We also report the design of a miniaturized photobioreactor to be used in combination with the light-switchable gene expression system. The miniaturized photobioreactor helps to reduce unintended induction of the light receptor due to environmental disturbances and allows precise control over the duration of induction. This system would be a good tool for switchable, homogenous, strong, and highly regulatable expression of target genes over a wide range of induction times. Hence, it could be applied to study gene function, optimize metabolic pathways, and control biological systems both spatially and temporally.open0

    Parallel use of shake flask and microtiter plate online measuring devices (RAMOS and BioLector) reduces the number of experiments in laboratory-scale stirred tank bioreactors

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    Background Conventional experiments in small scale are often performed in a Black Box fashion, analyzing only the product concentration in the final sample. Online monitoring of relevant process characteristics and parameters such as substrate limitation, product inhibition and oxygen supply is lacking. Therefore, fully equipped laboratory-scale stirred tank bioreactors are hitherto required for detailed studies of new microbial systems. However, they are too spacious, laborious and expensive to be operated in larger number in parallel. Thus, the aim of this study is to present a new experimental approach to obtain dense quantitative process information by parallel use of two small-scale culture systems with online monitoring capabilities: Respiration Activity MOnitoring System (RAMOS) and the BioLector device. Results The same mastermix (medium plus microorganisms) was distributed to the different small-scale culture systems: 1) RAMOS device; 2) 48-well microtiter plate for BioLector device; and 3) separate shake flasks or microtiter plates for offline sampling. By adjusting the same maximum oxygen transfer capacity (OTRmax), the results from the RAMOS and BioLector online monitoring systems supplemented each other very well for all studied microbial systems (E. coli, G. oxydans, K. lactis) and culture conditions (oxygen limitation, diauxic growth, auto-induction, buffer effects). Conclusions The parallel use of RAMOS and BioLector devices is a suitable and fast approach to gain comprehensive quantitative data about growth and production behavior of the evaluated microorganisms. These acquired data largely reduce the necessary number of experiments in laboratory-scale stirred tank bioreactors for basic process development. Thus, much more quantitative information is obtained in parallel in shorter time.Cluster of Excellence “Tailor-Made Fuels from Biomass”, which is funded by the Excellence Initiative by the German federal and state governments to promote science and research at German universities

    A highly efficient multi-core algorithm for clustering extremely large datasets

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    <p>Abstract</p> <p>Background</p> <p>In recent years, the demand for computational power in computational biology has increased due to rapidly growing data sets from microarray and other high-throughput technologies. This demand is likely to increase. Standard algorithms for analyzing data, such as cluster algorithms, need to be parallelized for fast processing. Unfortunately, most approaches for parallelizing algorithms largely rely on network communication protocols connecting and requiring multiple computers. One answer to this problem is to utilize the intrinsic capabilities in current multi-core hardware to distribute the tasks among the different cores of one computer.</p> <p>Results</p> <p>We introduce a multi-core parallelization of the k-means and k-modes cluster algorithms based on the design principles of transactional memory for clustering gene expression microarray type data and categorial SNP data. Our new shared memory parallel algorithms show to be highly efficient. We demonstrate their computational power and show their utility in cluster stability and sensitivity analysis employing repeated runs with slightly changed parameters. Computation speed of our Java based algorithm was increased by a factor of 10 for large data sets while preserving computational accuracy compared to single-core implementations and a recently published network based parallelization.</p> <p>Conclusions</p> <p>Most desktop computers and even notebooks provide at least dual-core processors. Our multi-core algorithms show that using modern algorithmic concepts, parallelization makes it possible to perform even such laborious tasks as cluster sensitivity and cluster number estimation on the laboratory computer.</p

    Single-Cell Census of Mechanosensitive Channels in Living Bacteria

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    Bacteria are subjected to a host of different environmental stresses. One such insult occurs when cells encounter changes in the osmolarity of the surrounding media resulting in an osmotic shock. In recent years, a great deal has been learned about mechanosensitive (MS) channels which are thought to provide osmoprotection in these circumstances by opening emergency release valves in response to membrane tension. However, even the most elementary physiological parameters such as the number of MS channels per cell, how MS channel expression levels influence the physiological response of the cells, and how this mean number of channels varies from cell to cell remain unanswered. In this paper, we make a detailed quantitative study of the expression of the mechanosensitive channel of large conductance (MscL) in different media and at various stages in the growth history of bacterial cultures. Using both quantitative fluorescence microscopy and quantitative Western blots our study complements earlier electrophysiology-based estimates and results in the following key insights: i) the mean number of channels per cell is much higher than previously estimated, ii) measurement of the single-cell distributions of such channels reveals marked variability from cell to cell and iii) the mean number of channels varies under different environmental conditions. The regulation of MscL expression displays rich behaviors that depend strongly on culturing conditions and stress factors, which may give clues to the physiological role of MscL. The number of stress-induced MscL channels and the associated variability have far reaching implications for the in vivo response of the channels and for modeling of this response. As shown by numerous biophysical models, both the number of such channels and their variability can impact many physiological processes including osmoprotection, channel gating probability, and channel clustering
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