Neue Analyse- und Visualisierungsmöglichkeiten im Monitor der Siedlungs- und Freiraumentwicklung (IÖR-Monitor)

Der Monitor der Siedlungs- und Freiraumentwicklung, kurz: „IÖR-Monitor“ informiert seit 2010 im Internet über die Entwicklung der Siedlungs- und Freiraumstruktur in Deutschland. Dabei befindet er sich in stetiger Weiterentwicklung, sowohl was das Indikatorenset betrifft, als auch die Visualisierungs- und die Analysemöglichkeiten. Der Beitrag beschreibt die neuesten Entwicklungen des Übersichts-Viewers und gibt einen Einblick in den entstehenden Detail-Viewer

Modeling Lactococcus lactis using a genome-scale flux model

BACKGROUND: Genome-scale flux models are useful tools to represent and analyze microbial metabolism. In this work we reconstructed the metabolic network of the lactic acid bacteria Lactococcus lactis and developed a genome-scale flux model able to simulate and analyze network capabilities and whole-cell function under aerobic and anaerobic continuous cultures. Flux balance analysis (FBA) and minimization of metabolic adjustment (MOMA) were used as modeling frameworks. RESULTS: The metabolic network was reconstructed using the annotated genome sequence from L. lactis ssp. lactis IL1403 together with physiological and biochemical information. The established network comprised a total of 621 reactions and 509 metabolites, representing the overall metabolism of L. lactis. Experimental data reported in the literature was used to fit the model to phenotypic observations. Regulatory constraints had to be included to simulate certain metabolic features, such as the shift from homo to heterolactic fermentation. A minimal medium for in silico growth was identified, indicating the requirement of four amino acids in addition to a sugar. Remarkably, de novo biosynthesis of four other amino acids was observed even when all amino acids were supplied, which is in good agreement with experimental observations. Additionally, enhanced metabolic engineering strategies for improved diacetyl producing strains were designed. CONCLUSION: The L. lactis metabolic network can now be used for a better understanding of lactococcal metabolic capabilities and potential, for the design of enhanced metabolic engineering strategies and for integration with other types of 'omic' data, to assist in finding new information on cellular organization and function

Evolutionary programming as a platform for in silico metabolic engineering.

BACKGROUND: Through genetic engineering it is possible to introduce targeted genetic changes and hereby engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, owing to the complexity of metabolic networks, both in terms of structure and regulation, it is often difficult to predict the effects of genetic modifications on the resulting phenotype. Recently genome-scale metabolic models have been compiled for several different microorganisms where structural and stoichiometric complexity is inherently accounted for. New algorithms are being developed by using genome-scale metabolic models that enable identification of gene knockout strategies for obtaining improved phenotypes. However, the problem of finding optimal gene deletion strategy is combinatorial and consequently the computational time increases exponentially with the size of the problem, and it is therefore interesting to develop new faster algorithms. RESULTS: In this study we report an evolutionary programming based method to rapidly identify gene deletion strategies for optimization of a desired phenotypic objective function. We illustrate the proposed method for two important design parameters in industrial fermentations, one linear and other non-linear, by using a genome-scale model of the yeast Saccharomyces cerevisiae. Potential metabolic engineering targets for improved production of succinic acid, glycerol and vanillin are identified and underlying flux changes for the predicted mutants are discussed. CONCLUSION: We show that evolutionary programming enables solving large gene knockout problems in relatively short computational time. The proposed algorithm also allows the optimization of non-linear objective functions or incorporation of non-linear constraints and additionally provides a family of close to optimal solutions. The identified metabolic engineering strategies suggest that non-intuitive genetic modifications span several different pathways and may be necessary for solving challenging metabolic engineering problems

Pea aphids (Hemiptera: Aphididae) have diurnal rhythms when raised independently of a host plant

Seasonal timing is assumed to involve the circadian clock, an endogenous mechanism to track time and measure day length. Some debate persists, however, and aphids were among the first organisms for which circadian clock involvement was questioned. Inferences about links to phenology are problematic, as the clock itself is little investigated in aphids. For instance, it is unknown whether aphids possess diurnal rhythms at all. Possibly, the close interaction with host plants prevents independent measurements of rhythmicity. We reared the pea aphid Acyrthosiphon pisum (Harris) on an artificial diet, and recorded survival, moulting, and honeydew excretion. Despite their plant-dependent life style, aphids were independently rhythmic under light-dark conditions. This first demonstration of diurnal aphid rhythms shows that aphids do not simply track the host plant's rhythmicity