Ein neues Festival eröffnet das Tanzjahr: Melanie Zimmermann erklärt im Gespräch mit Falk Schreiber, wie Hannover eine Heimat für «Real Dance» werden soll
Johannes Wieland war 15 Jahre lang Tanzdirektor in Kassel – und arbeitet jetzt wieder frei. Ein Gespräch mit Falk Schreiber über enge Strukturen am Staatstheater, finanzielle Unsicherheit und die sogenannte Provinz
Pseudomonas aeruginosa often causes multidrug-resistant infections in immunocompromised patients, and polymyxins are often used as the last-line therapy. Alarmingly, resistance to polymyxins has been increasingly reported worldwide recently. To rescue this last-resort class of antibiotics, it is necessary to systematically understand how P. aeruginosa alters its metabolism in response to polymyxin treatment, thereby facilitating the development of effective therapies. To this end, a genome-scale metabolic model (GSMM) was used to analyze bacterial metabolic changes at the systems level.A high-quality GSMM iPAO1 was constructed for P. aeruginosa PAO1 for antimicrobial pharmacological research. Model iPAO1 encompasses an additional periplasmic compartment and contains 3022 metabolites, 4265 reactions, and 1458 genes in total. Growth prediction on 190 carbon and 95 nitrogen sources achieved an accuracy of 89.1%, outperforming all reported P. aeruginosa models. Notably, prediction of the essential genes for growth achieved a high accuracy of 87.9%. Metabolic simulation showed that lipid A modifications associated with polymyxin resistance exert a limited impact on bacterial growth and metabolism but remarkably change the physiochemical properties of the outer membrane. Modeling with transcriptomics constraints revealed a broad range of metabolic responses to polymyxin treatment, including reduced biomass synthesis, upregulated amino acid catabolism, induced flux through the tricarboxylic acid cycle, and increased redox turnover.Overall, iPAO1 represents the most comprehensive GSMM constructed to date for Pseudomonas. It provides a powerful systems pharmacology platform for the elucidation of complex killing mechanisms of antibiotics.
Summary The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail. The SBGN Activity Flow language represents the influences of activities among various entities within a network. Unlike SBGN PD and ER that focus on the entities and their relationships with others, SBGN AF puts the emphasis on the functions (or activities) performed by the entities, and their effects to the functions of the same or other entities. The nodes (elements) describe the biological activities of the entities, such as protein kinase activity, binding activity or receptor activity, which can be easily mapped to Gene Ontology molecular function terms. The edges (connections) provide descriptions of relationships (or influences) between the activities, e.g., positive influence and negative influence. Among all three languages of SBGN, AF is the closest to signaling pathways in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.
Summary Standards are essential to the advancement of science and technology. In systems and synthetic biology, numerous standards and associated tools have been developed over the last 16 years. This special issue of the Journal of Integrative Bioinformatics aims to support the exchange, distribution and archiving of these standards, as well as to provide centralised and easily citable access to them.
This data sets contains Molecular Dynamics (MD) Simulation files and analysis. Microcystin (MC) congeners were simulated in solvent (water) and in complex with protein phosphatase 1 (PPP1). MD Simulation was repeated for three times. This data is replicate 1 and related data sets are available. Please cite the original publication when using all or part of the data: S. Jaeger-Honz, J. Nitschke, S. Altaner, K. Klein, D. R. Dietrich, F. Schreiber: Investigation of microcystin conformation and binding towards PPP1 by molecular dynamics simulation. Chemico-Biological Interactions, 2021
Marina Abramović ist eine Legende. 1946 in Belgrad geboren, entwickelte sie sich ab den 1960ern mit schonungslosen, radikalen, schockierenden Arbeiten zur bedeutendsten Performance-Künstlerin der Welt. Mit Falk Schreiber spricht sie darüber, was von ihr in Erinnerung bleiben wird
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