The main goal of this work was to develop a strategy that enables tuning of recombinant gene expression relative to the metabolic capacity of the host cell synthesis machinery. In the past, strong expression systems have been developed in order to maximize recombinant gene expression. However, these systems exert an extremely high metabolic burden onto the host cell, which may even lead to cell death. Hence, the period of recombinant gene expression is significantly reduced, and therefore, maximal yield cannot be attained. To extend the production phase and to achieve optimal yields, adjustment of recombinant gene expression by modulation of the transcription rate is required. To control transcription, we designed a feed regime, which continuously supplies limiting amounts of inducer in a constant ratio to biomass. For the accurate determination of appropriate amounts of inducer, a time shifted exponential substrate and inducer feed strategy has been developed. The potential of this metabolic and engineering integrated approach was proven in fed-batch cultivation experiments using E. coli HMS174(DE3)(pET11ahSOD) as model system. Furthermore, our strategy enables the use of lactose as inducer, since its consumption can be compensated by appropriate feed profiles. The attained results fully comply with all requirements of industrial large scale cultivation and improve the applicability of strong expression systems.

No abstract

No abstract

BackgroundInterpretation of comprehensive DNA microarray data sets is a challenging task for biologists and process engineers where scientific assistance of statistics and bioinformatics is essential. Interdisciplinary cooperation and concerted development of software-tools for simplified and accelerated data analysis and interpretation is the key to overcome the bottleneck in data-analysis workflows. This approach is exemplified by gcExplorer an interactive visualization toolbox based on cluster analysis. Clustering is an important tool in gene expression data analysis to find groups of co-expressed genes which can finally suggest functional pathways and interactions between genes. The visualization of gene clusters gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results.ResultsIn this study the interactive visualization toolbox gcExplorer is applied to the interpretation of E. coli microarray data. The data sets derive from two fedbatch experiments conducted in order to investigate the impact of different induction strategies on the host metabolism and product yield. The software enables direct graphical comparison of these two experiments. The identification of potentially interesting gene candidates or functional groups is substantially accelerated and eased.ConclusionIt was shown that gcExplorer is a very helpful tool to gain a general overview of microarray experiments. Interesting gene expression patterns can easily be found, compared among different experiments and combined with information about gene function from publicly available databases.