[Resilience of German clinics in amok and terrorist situations : Results of an online-based survey in German emergency departments]. / Resilienz deutscher Kliniken in Amok- und Terrorlagen : Ergebnisse einer onlinebasierten Umfrage deutscher Notaufnahmen.

BACKGROUND: As a critical infrastructure (CRITIS) and soft target with high symbolic power, hospitals have to prepare for terrorist attacks, rampages, and establish damage-prevention measures. METHOD: The terrorist threat situation was quantified using an evaluation of the terrorist event database (TED) from 1970 to 2017. Additionally, the threat perception and precautionary measures were recorded by an online survey among the leaders of German emergency departments. RESULTS: The TED-evaluation shows an international increase in terrorist attacks on medical facilities, especially in civil war countries and the USA. The use of explosives caused a large number of injuries and deaths during the examined time period. Most online respondents felt that preparing the clinic for terrorist scenarios in their hospital emergency preparedness planning was important. Suitable measures were, however, often not established in hospital emergency preparedness planning. Only 59% dealt with the care of the injured due to a terrorist attack and only 34% with securing the infrastructure. CONCLUSION: German clinics should consider themselves as critical infrastructure and potential targets of terrorist attacks and prepare themselves for terrorist threats. Many measures that could be implemented cheaply have not been utilized in the clinics and should be integrated promptly into both hospital emergency preparedness planning and daily routines.

Effects of Fe2+/Fe3+ Binding to Human Frataxin and Its D122Y Variant, as Revealed by Site-Directed Spin Labeling (SDSL) EPR Complemented by Fluorescence and Circular Dichroism Spectroscopies.

Frataxin is a highly conserved protein whose deficiency results in the neurodegenerative disease Friederich's ataxia. Frataxin's actual physiological function has been debated for a long time without reaching a general agreement; however, it is commonly accepted that the protein is involved in the biosynthetic iron-sulphur cluster (ISC) machinery, and several authors have pointed out that it also participates in iron homeostasis. In this work, we use site-directed spin labeling coupled to electron paramagnetic resonance (SDSL EPR) to add new information on the effects of ferric and ferrous iron binding on the properties of human frataxin in vitro. Using SDSL EPR and relating the results to fluorescence experiments commonly performed to study iron binding to FXN, we produced evidence that ferric iron causes reversible aggregation without preferred interfaces in a concentration-dependent fashion, starting at relatively low concentrations (micromolar range), whereas ferrous iron binds without inducing aggregation. Moreover, our experiments show that the ferrous binding does not lead to changes of protein conformation. The data reported in this study reveal that the currently reported binding stoichiometries should be taken with caution. The use of a spin label resistant to reduction, as well as the comparison of the binding effect of Fe2+ in wild type and in the pathological D122Y variant of frataxin, allowed us to characterize the Fe2+ binding properties of different protein sites and highlight the effect of the D122Y substitution on the surrounding residues. We suggest that both Fe2+ and Fe3+ might play a relevant role in the context of the proposed FXN physiological functions.

Biographer: web-based editing and rendering of SBGN compliant biochemical networks.

MOTIVATION: The rapid accumulation of knowledge in the field of Systems Biology during the past years requires advanced, but simple-to-use, methods for the visualization of information in a structured and easily comprehensible manner. RESULTS: We have developed biographer, a web-based renderer and editor for reaction networks, which can be integrated as a library into tools dealing with network-related information. Our software enables visualizations based on the emerging standard Systems Biology Graphical Notation. It is able to import networks encoded in various formats such as SBML, SBGN-ML and jSBGN, a custom lightweight exchange format. The core package is implemented in HTML5, CSS and JavaScript and can be used within any kind of web-based project. It features interactive graph-editing tools and automatic graph layout algorithms. In addition, we provide a standalone graph editor and a web server, which contains enhanced features like web services for the import and export of models and visualizations in different formats. AVAILABILITY: The biographer tool can be used at and downloaded from the web page http://biographer.biologie.hu-berlin.de/. The different software packages, including a server-independent version as well as a web server for Windows and Linux based systems, are available at http://code.google.com/p/biographer/ under the open-source license LGPL

Propagating semantic information in biochemical network models.

BACKGROUND: To enable automatic searches, alignments, and model combination, the elements of systems biology models need to be compared and matched across models. Elements can be identified by machine-readable biological annotations, but assigning such annotations and matching non-annotated elements is tedious work and calls for automation. RESULTS: A new method called "semantic propagation" allows the comparison of model elements based not only on their own annotations, but also on annotations of surrounding elements in the network. One may either propagate feature vectors, describing the annotations of individual elements, or quantitative similarities between elements from different models. Based on semantic propagation, we align partially annotated models and find annotations for non-annotated model elements. CONCLUSIONS: Semantic propagation and model alignment are included in the open-source library semanticSBML, available on sourceforge. Online services for model alignment and for annotation prediction can be used at http://www.semanticsbml.org.

Sustainable model building the role of standards and biological semantics.

Systems biology models can be reused within new simulation scenarios, as parts of more complex models or as sources of biochemical knowledge. Reusability does not come by itself but has to be ensured while creating a model. Most important, models should be designed to remain valid in different contexts-for example, for different experimental conditions-and be published in a standardized and well-documented form. Creating reusable models is worthwhile, but it requires some efforts when a model is developed, implemented, documented, and published. Minimum requirements for published systems biology models have been formulated by the MIRIAM initiative. Main criteria are completeness of information and documentation, availability of machine-readable models in standard formats, and semantic annotations connecting the model elements with entries in biological Web resources. In this chapter, we discuss the assumptions behind bottom-up modeling; present important standards like MIRIAM, the Systems Biology Markup Language (SBML), and the Systems Biology Graphical Notation (SBGN); and describe software tools and services for handling semantic annotations. Finally, we show how standards can facilitate the construction of large metabolic network models.

Retrieval, alignment, and clustering of computational models based on semantic annotations.

The exploding number of computational models produced by Systems Biologists over the last years is an invitation to structure and exploit this new wealth of information. Researchers would like to trace models relevant to specific scientific questions, to explore their biological content, to align and combine them, and to match them with experimental data. To automate these processes, it is essential to consider semantic annotations, which describe their biological meaning. As a prerequisite for a wide range of computational methods, we propose general and flexible similarity measures for Systems Biology models computed from semantic annotations. By using these measures and a large extensible ontology, we implement a platform that can retrieve, cluster, and align Systems Biology models and experimental data sets. At present, its major application is the search for relevant models in the BioModels Database, starting from initial models, data sets, or lists of biological concepts. Beyond similarity searches, the representation of models by semantic feature vectors may pave the way for visualisation, exploration, and statistical analysis of large collections of models and corresponding data.

Parameter balancing in kinetic models of cell metabolism.

Kinetic modeling of metabolic pathways has become a major field of systems biology. It combines structural information about metabolic pathways with quantitative enzymatic rate laws. Some of the kinetic constants needed for a model could be collected from ever-growing literature and public web resources, but they are often incomplete, incompatible, or simply not available. We address this lack of information by parameter balancing, a method to complete given sets of kinetic constants. Based on Bayesian parameter estimation, it exploits the thermodynamic dependencies among different biochemical quantities to guess realistic model parameters from available kinetic data. Our algorithm accounts for varying measurement conditions in the input data (pH value and temperature). It can process kinetic constants and state-dependent quantities such as metabolite concentrations or chemical potentials, and uses prior distributions and data augmentation to keep the estimated quantities within plausible ranges. An online service and free software for parameter balancing with models provided in SBML format (Systems Biology Markup Language) is accessible at www.semanticsbml.org. We demonstrate its practical use with a small model of the phosphofructokinase reaction and discuss its possible applications and limitations. In the future, parameter balancing could become an important routine step in the kinetic modeling of large metabolic networks.

Annotation and merging of SBML models with semanticSBML.

SUMMARY: Systems Biology Markup Language (SBML) is the leading exchange format for mathematical models in Systems Biology. Semantic annotations link model elements with external knowledge via unique database identifiers and ontology terms, enabling software to check and process models by their biochemical meaning. Such information is essential for model merging, one of the key steps towards the construction of large kinetic models. SemanticSBML is a tool that helps users to check and edit MIRIAM annotations and SBO terms in SBML models. Using a large collection of biochemical names and database identifiers, it supports modellers in finding the right annotations and in merging existing models. Initially, an element matching is derived from the MIRIAM annotations and conflicting element attributes are categorized and highlighted. Conflicts can then be resolved automatically or manually, allowing the user to control the merging process in detail. AVAILABILITY: SemanticSBML comes as a free software written in Python and released under the GPL 3. A Debian package, a source package for other Linux distributions, a Windows installer and an online version of semanticSBML with limited functionality are available at http://www.semanticsbml.org. A preinstalled version can be found on the Linux live DVD SB.OS, available at http://www.sbos.eu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

TIde: a software for the systematic scanning of drug targets in kinetic network models.

BACKGROUND: During the stages of the development of a potent drug candidate compounds can fail for several reasons. One of them, the efficacy of a candidate, can be estimated in silico if an appropriate ordinary differential equation model of the affected pathway is available. With such a model at hand it is also possible to detect reactions having a large effect on a certain variable such as a substance concentration. RESULTS: We show an algorithm that systematically tests the influence of activators and inhibitors of different type and strength acting at different positions in the network. The effect on a quantity to be selected (e.g. a steady state flux or concentration) is calculated. Moreover, combinations of two inhibitors or one inhibitor and one activator targeting different network positions are analysed. Furthermore, we present TIde (Target Identification), an open source, platform independent tool to investigate ordinary differential equation models in the common systems biology markup language format. It automatically assigns the respectively altered kinetics to the inhibited or activated reactions, performs the necessary calculations, and provides a graphical output of the analysis results. For illustration, TIde is used to detect optimal inhibitor positions in simple branched networks, a signalling pathway, and a well studied model of glycolysis in Trypanosoma brucei. CONCLUSION: Using TIde, we show in the branched models under which conditions inhibitions in a certain pathway can affect a molecule concentrations in a different. In the signalling pathway we illuminate which inhibitions have an effect on the signalling characteristics of the last active kinase. Finally, we compare our set of best targets in the glycolysis model with a similar analysis showing the applicability of our tool.

SBMLmerge, a system for combining biochemical network models.

The Systems Biology Markup Language (SBML) is an XML-based format for representing mathematical models of biochemical reaction networks, and it is likely to become a main standard in the systems biology community. As published mathematical models in cell biology are growing in number and size, modular modelling approaches will gain additional importance. The main issue to be addressed in computer-assisted model combination is the specification and handling of model semantics. The software SBMLmerge assists the user in combining models of biological subsystems to larger biochemical networks. First, the program helps the user in annotating all model elements with unique identifiers pointing to databases such as KEGG or Gene Ontology. Second, during merging, SBMLmerge detects and resolves various syntactic and semantic problems. Typical problems are conflicting variable names, elements which appear in more than one input model, and mathematical problems arising from the combination of equations. If the input models make contradicting statements about a biochemical quantity, the user is asked to choose between them. In the end the merging process results in a new, valid SBML model.