Natürlicher Libertarismus. Kritische Studien im Umkreis von Robert Kanes Theorie libertarischer Willensfreiheit

Im ersten Kapitel der Arbeit wird in Robert Kanes Theorie libertarischer Willensfreiheit eingeführt. Neben der genauen Explikation dessen, was ein Libertarier wie Kane eigentlich im Einzelnen unter „Willensfreiheit” versteht, werden in diesem Kapitel die drei Hauptthesen des natürlichen Libertarismus eingeführt, die den Untersuchungsgegenstand der restlichen Arbeit bilden. Das zweite Kapitel untersucht die erste dieser Hauptthesen des natürlichen Libertarismus, die Unvereinbarkeitsthese, welche die Unvereinbarkeit von libertarischer Willensfreiheit mit einem Determinismus in der Willensfestlegung behauptet. Im Zentrum der Untersuchung steht dabei die Frage, ob sich nicht doch gegen Kanes These eine Vereinbarkeit von libertarischer Willensfreiheit und Determinismus zeigen lässt. Das dritte Kapitel widmet sich der Untersuchung der zweiten Hauptthese Kanes, der Vereinbarkeitsthese, welche besagt, dass libertarische Willensfreiheit mit einem Indeterminismus an relevanter Stelle in der Willensfestlegung vereinbar ist. Hier stehen diejenigen Möglichkeiten im Zentrum der Untersuchung, die der natürliche Libertarismus hat, um insbesondere auf unterschiedliche Zufallseinwände und den Einwand der mangelnden rationalen Erklärbarkeit zu antworten, die gegen die Vereinbarkeitsthese formuliert wurden. Schließlich untersucht das vierte und letzte Kapitel Kanes dritte Hauptthese, die Existenzthese, dass libertarische Willensfreiheit nicht bloß eine sinnvolle theoretische Möglichkeit ist, sondern tatsächlich in dieser Welt gegeben ist

Normalization And Matching Of Chemical Compound Names

We have developed ChemHits (http://sabio.h-its.org/chemHits/), an application which detects and matches synonymic names of chemical compounds. The tool is based on natural language processing (NLP) methods and applies rules to systematically normalize chemical compound names. Subsequently, matching of synonymous names is achieved by comparison of the normalized name forms. The tool is capable of normalizing a given name of a chemical compound and matching it against names in (bio-)chemical databases, like SABIO-RK, PubChem, ChEBI or KEGG, even when there is no exact name-to-name-match

26. Spotlight: Expert*innen auf dem Gebiet der SynBio : Eine Recherche unter Anwendung des ExpertExplorers

In the ‘Fifth Gene Technology Report’, renowned experts provide an overview of current developments and their applications in the dynamically evolving research field of gene and biotechnologies. They examine, among other topics, genetic diagnostics, somatic gene therapy, the development of vaccines, stem cell and organoid research, green gene technology, synthetic biology, gene drives, genome editing, epigenetics and single cell analysis. In addition to reporting on the current state of affairs in this field, the authors also discuss society’s perception of gene technologies and ethical and legal issues relating to them, such as genome edit-ing, cerebral organoids and big data in personalised medicine. Moreover, the interdisciplinary task force ‘Gentechnologiebericht’ (Gene Technology Report) offers recommendations on action that could be taken in relation to the key issues. With contributions by Karla Alex, Sina Bartfeld, Meik Bittkowski, Inge Broer, Lorina Buhr, Stephan Clemens, Wolfgang Van den Daele, Hans-Georg Dederer, Tobias J. Erb, Nina Gasparoni, Heiner Fangerau, Boris Fehse, Jürgen Hampel, Louise Herde, Ferdinand Hucho, Ali Jawaid, Aida Khachatryan, Sarah Kohler, Alma Kolleck, Martin Korte, Cordula Kropp, Alfons Labisch, Markus Lehmkuhl, Melanie Leidecker-Sandmann, Annette Leßmöllmann, Isabelle M. Mansuy, Lilian Marx-Stölting, Andreas Merk, Yannick Milhahn, Fruzsina Molnár-Gábor, Stefan Mundlos, Staffan Müller-Wille, Angela Osterheider, Anja Pichl, Barbara Prainsack, Jens Reich, Marlen Reinschke, Ortwin Renn, Hans-Jörg Rheinberger, Arnold Sauter, Hannah Schickl, Silke Schicktanz, Volker Stollorz, Constanze Störk-Biber, Jochen Taupitz, Jörn Walter, Eva C. Winkler, Martin Zenke and Michael M. Zwick

Normalization and Matching of Chemical Compound Names

The identification of a chemical compound solely based on its name requires comprehensive chemical knowledge and often extensive searches in chemical databases. However, it is crucial for the integration of biochemical data extracted from the literature, since many publications exclusively describe a compound by its name. We have developed an application which matches synonymic names of chemical compounds and thereby facilitates the bundling of corresponding data referring to the same compound.

The tool that we have developed is based on natural language processing (NLP) methods and applies rules to systematically normalize chemical compound names. Matching of synonymous names is achieved by comparison of the normalized name forms. It is capable of normalizing a given name of a chemical compound and matching it against names in (bio-)chemical databases (e.g. SABIO-RK, ChEBI or PubChem), even when there is no exact name-to-name-match. The tool is also able to match a complete list of compound names against these databases which makes it useful for the automatic annotation of chemical data.

This normalization and matching of various synonyms of a chemical compound constitutes a platform for the unambiguous identification of compounds described in the literature or in databases.
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Data extraction for the reaction kinetics database SABIO-RK

SABIO-RK (http://sabio.h-its.org/) is a web-accessible, manually curated database that has been established as a resource for biochemical reactions and their kinetic properties with a focus on supporting the computational modeling to create models of biochemical reaction networks. SABIO-RK data are mainly extracted from literature but also directly submitted from lab experiments. In most cases the information in the literature is distributed across the whole publication, insufficiently structured and often described without standard terminology. Therefore the manual extraction of knowledge from the literature requires biological experts to understand the paper and interpret the data. The database offers the literature data in a structured format including annotations to controlled vocabularies, ontologies and external databases which supports modellers, as well as experimentalists, in the very time consuming process of collecting information from different publications. Here we describe the data extraction and curation efforts needed for SABIO-RK and give recommendations for publishing kinetic data in a complete and structured manner

SEEK : a systems biology data and model management platform

CITATION: Wolstencroft, K. et al. 2015. SEEK : a systems biology data and model management platform. BMC Systems Biology, 9:33, doi:10.1186/s12918-015-0174-y.The original publication is available at http://bmcsystbiol.biomedcentral.comBackground: Systems biology research typically involves the integration and analysis of heterogeneous data types in order to model and predict biological processes. Researchers therefore require tools and resources to facilitate the sharing and integration of data, and for linking of data to systems biology models. There are a large number of public repositories for storing biological data of a particular type, for example transcriptomics or proteomics, and there are several model repositories. However, this silo-type storage of data and models is not conducive to systems biology investigations. Interdependencies between multiple omics datasets and between datasets and models are essential. Researchers require an environment that will allow the management and sharing of heterogeneous data and models in the context of the experiments which created them. Results: The SEEK is a suite of tools to support the management, sharing and exploration of data and models in systems biology. The SEEK platform provides an access-controlled, web-based environment for scientists to share and exchange data and models for day-to-day collaboration and for public dissemination. A plug-in architecture allows the linking of experiments, their protocols, data, models and results in a configurable system that is available 'off the shelf'. Tools to run model simulations, plot experimental data and assist with data annotation and standardisation combine to produce a collection of resources that support analysis as well as sharing. Underlying semantic web resources additionally extract and serve SEEK metadata in RDF (Resource Description Format). SEEK RDF enables rich semantic queries, both within SEEK and between related resources in the web of Linked Open Data. Conclusion: The SEEK platform has been adopted by many systems biology consortia across Europe. It is a data management environment that has a low barrier of uptake and provides rich resources for collaboration. This paper provides an update on the functions and features of the SEEK software, and describes the use of the SEEK in the SysMO consortium (Systems biology for Micro-organisms), and the VLN (virtual Liver Network), two large systems biology initiatives with different research aims and different scientific communities.http://bmcsystbiol.biomedcentral.com/articles/10.1186/s12918-015-0174-yPublisher's versio

SEEK: a systems biology data and model management platform.

Computer Systems, Imagery and Medi