Organigramm Des Rektorats Einrichtungen Des Rektorats Der

Organizational chart of the University of Veterinary Medicine, Vienna Governing Bodies of the University Senate Rectorate University Council Research and Teaching Department 1 Department 2 Department 3 Department 4 Department 5 ________________________________________________________ ________________________________________________________ ________________________________________________________ ________________________________________________________ ________________________________________________________ Department of Biomedical Sciences Department of Pathobiology Department/University Clinic for Farm Department/University Clinic for Department of Interdisciplinary Life Animals and Veterinary Public Health Companion Animals and Horses Sciences Institute of Computational Medicine Institute of Morphology Institute for in-vivo and in-vitro models Institute of Microbiology Institute of Food Safety, Food Technology and University Clinic* for Small Animals Research Institute of Wildlife Ecology Institute of Medical Biochemistry Functional Microbiology Veterinary Public Health Anaesthesiology and perioperative Intensive- Conservation Medicine Institute of Pharmacology and Toxicology Institute of Immunology Food Microbiology Care Medicine Konrad Lorenz Institute of Ethology Clinical Pharmacology Institute of Parasitology Food Hygiene and Technology Diagnostic Imaging Ornithology Institute of Physiology, Patho physiology and Institute of Pathology Veterinary Public Health and Epidemiology Obstetrics, Gynaecology and Andrology Messerli Research Institute Biophysics Pathology of Laboratory Animals Institute of Animal Nutrition and Functional Plant Internal Medicine Small Animals Nutritional Physiology Institute of Virology Compounds Small Animal Surgery Physiology, Pathophysiology and Experimental Institute of Animal Welfare Science Equine University Clinic* Endocrinology Clinical Pathology Platform University Clinic* for Poultry and Fish Medicine Equine Internal Medicine Physiology and Biophysics Fish Medicine Equine Surgery Institute of Population Genetics Poultry Medicine Institute of Animal Breeding and Genetics University Clinic* for Swine Insemination and Embryotransfer Platform Molecular Genetics University Clinic* for Ruminants Radiooncology and Nuclear Medicine Platform Reproductive Biology Herd Health Management for Ruminants Functional Cancer Genomics Ruminant Medicine Bioinformatics and Biostatistics Platform Biomodels Austria Platform Interuniv ersity Institutions Interinstitutional Establishments Department for Agrobiotechnology (IFA Tulln) Graf Lehndorff Institute for Equine Science Service and Administratives Vice-Rector for Study Affairs Vice-Rector for Research Vice-Rector for Resources Rector and Clinical Veterinary Medicine and International Relations and Digitalisation Service and Administrative Units: Service and Administrative Units: Service and Administrative Units: Service and Administrative Units: Public Relations Study Affairs Research Support and Innovation Campus Management Legal Services E-Learning & New Media International Relations Accounting und Controlling Office of the Rectorate Pharmacy Postgraduate Studies Strategic Process Control Ethics and Animal Welfare Committee Information- and Knowledge Management Other: Central Special Facility: IT-Services Other: Quality Management and Monitoring of Teaching VetCore Human Resources Reporting and Development Planning Administrative Head of the University Hospital for Staff Development Internal Auditing Small Animals University Library Quality Management/Quality Development and Evaluation Other: Data Protection Officer Strategic Resource and Risk Management Central Special Facility: Core Facility Wolf Science Center (WSC) VetFarm *In accordance with § 36 and § 20(5) of the 2002 Universities Act, the University Clinics do not represent organizational units as of July 1st, 2021.

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Computational Medical Bioinformatics
MOJ Applied Bionics and Biomechanics Mini Review Open Access Computational medical bioinformatics Abstract Volume 2 Issue 4 - 2018 Biotechnology is a technological discipline based on Biology and applied to meet the Rodrigo Arturo Marquet Rivera, Guillermo needs of human life. Which can be considered essential, as is health. This discipline has developed in the last decades a series of medical applications that are interrelated Urriolagoitia Sosa, Rosa Alicia Hernández with other fields of science, which are not precisely those inherent in the health Vázquez, Beatriz Romero Ángeles, Juan sciences. One of the main ones is Computational Bioinformatics, which has become Alejandro Vázquez Feijoo, Guillermo a useful tool in the practice of the different medical areas through the generation of Urriolagoitia-Calderón biomodels. Instituto Politécnico Nacional, México Correspondence: Guillermo Urriolagoitia Sosa, Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Sección de Estudios de Posgrado e Investigación Unidad Profesional Adolfo López Mateos, Zacatenco. Av. IPN s/n Edificio 5, 2° piso Col. Lindavista, Delegación Gustavo A. Madero, C.P. 07320, México, Email [email protected] Received: June 30, 2018 | Published: August 17, 2018 Introduction the images obtained from them allow a visualization and manipulation that offers better results (Figure 1). Computational Bioinformatics is a novel tool that can be applied in the study of the behaviour presented by the different levels of Proposal organization of living matter (cells, tissues and organs), under the effects of stimuli and external agents, such as burdens mechanical, With the images obtained from the imaging studies, it is possible to which influence the behaviour of cellular metabolism.1 Through generate biomodels that allow a better exploration of the anatomical the generation of anatomical biomodels, is interested in solving structures to be treated, observe them with greater precision and biological problems in a multidisciplinary and interdisciplinary way.
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Data Extraction for the Reaction Kinetics Database SABIO-RK$
Perspectives in Science (]]]]) ], ]]]–]]] Available online at www.sciencedirect.com www.elsevier.com/locate/pisc REVIEW Data extraction for the reaction kinetics database SABIO-RK$ Ulrike Wittign, Renate Kania, Meik Bittkowski, Elina Wetsch, Lei Shi, Lenneke Jong, Martin Golebiewski, Maja Rey, Andreas Weidemann, Isabel Rojas, Wolfgang Müller Scientiﬁc Databases and Visualization Group, Heidelberg Institute for Theoretical Studies (HITS), Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany Received 5 March 2013; accepted 4 November 2013; Available online XX, XX, 2014 KEYWORDS Abstract Database; SABIO-RK (http://sabio.h-its.org/) is a web-accessible, manually curated database that has Reaction kinetics; been established as a resource for biochemical reactions and their kinetic properties with a Biocuration; focus on supporting the computational modeling to create models of biochemical reaction Ontology 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. & 2014 The Authors. Published by Elsevier GmbH. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).
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Artificial Intelligence in Biological Modelling François Fages
Artificial Intelligence in Biological Modelling François Fages To cite this version: François Fages. Artificial Intelligence in Biological Modelling. A Guided Tour of Artificial Intelligence Research, 2020, Volume III: Interfaces and Applications of Artificial Intelligence, 978-3-030-06170-8_8. 10.1007/978-3-030-06170-8_8. hal-01409753v2 HAL Id: hal-01409753 https://hal.inria.fr/hal-01409753v2 Submitted on 11 May 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. AI in Biological Modelling François Fages Abstract Systems Biology aims at elucidating the high-level functions of the cell from their biochemical basis at the molecular level. A lot of work has been done for collecting genomic and post-genomic data, making them available in databases and ontologies, building dynamical models of cell metabolism, signalling, division cy- cle, apoptosis, and publishing them in model repositories. In this chapter we review different applications of AI to biological systems modelling. We focus on cell pro- cesses at the unicellular level which constitutes most of the work achieved in the last two decades in the domain of Systems Biology. We show how rule-based languages and logical methods have played an important role in the study of molecular inter- action networks and of their emergent properties responsible for cell behaviours.
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Expanding Horizons to Include More Modelling Approaches and Formats Mihai Glont1, Tung V
D1248–D1253 Nucleic Acids Research, 2018, Vol. 46, Database issue Published online 2 November 2017 doi: 10.1093/nar/gkx1023 BioModels: expanding horizons to include more modelling approaches and formats Mihai Glont1, Tung V. N. Nguyen1, Martin Graesslin2, Robert Halke¨ 3,RazaAli1, Jochen Schramm2, Sarala M. Wimalaratne1, Varun B. Kothamachu1,4, Nicolas Rodriguez4, Maciej J. Swat1, Jurgen Eils2, Roland Eils2, Camille Laibe1, Rahuman S. Malik-Sheriff1,*, Vijayalakshmi Chelliah1, Nicolas Le Novere` 4,* and Henning Hermjakob1,* 1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK, 2Department of Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, University of Heidelberg, BioQuant, IPMB and DKFZ Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany, 3University of Rostock, Rostock, Germany and 4Babraham Institute, Cambridge CB22 3AT, UK Received September 25, 2017; Editorial Decision October 17, 2017; Accepted October 18, 2017 ABSTRACT a resource that facilitates the exchange, reuse and repurpos- ing of the models. Since its inception, BioModels’ content BioModels serves as a central repository of mathe- has been steadily increasing, making it a central portal that matical models representing biological processes. It provides reproducible, high-quality, freely accessible mod- offers a platform to make mathematical models easily els published in the scientific literature. BioModels hosts shareable across the systems modelling community, over 8400 models from the scientific literature submitted thereby supporting model reuse. To facilitate host- by authors as well as internal and external BioModels cu- ing a broader range of model formats derived from rators. This includes a recent submission of 6750 patient- diverse modelling approaches and tools, a new in- specific genome-scale metabolic models derived from tu- frastructure for BioModels has been developed that mour samples of individual patients (2).
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SABIO-RK: a Data Warehouse for Biochemical Reactions and Their Kinetics
Journal of Integrative Bioinformatics 2007 http://journal.imbio.de/ SABIO-RK: A data warehouse for biochemical reactions and their kinetics. Olga Krebs*, Martin Golebiewski, Renate Kania, Saqib Mir, Jasmin Saric, Andreas Weidemann, Ulrike Wittig and Isabel Rojas Scientific Databases and Visualisation Group, EML Research gGmbH, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany Abstract Systems biology is an emerging field that aims at obtaining a system-level understanding of biological processes. The modelling and simulation of networks of biochemical reactions have great and promising application potential but require reliable kinetic data. In order to support the systems biology community with such data we have developed SABIO-RK (System for the Analysis of Biochemical Pathways - Reaction Kinetics), a curated database with information about biochemical reactions and their kinetic (http://creativecommons.org/licenses/by-nc-nd/3.0/). properties, which allows researchers to obtain and compare kinetic data and to integrate them into models of biochemical networks. SABIO-RK is freely available for academic License use at http://sabio.villa-bosch.de/SABIORK/. Unported 1 Introduction 3.0 Systems biology deals with the analysis and prediction of the dynamic behaviour of biological networks through mathematical modelling based on experimental data [1]. It focuses on the connections and interactions of the components in the cell and in general in the organism, all as part of one system. The modelling and simulation of biochemical reaction
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Press Release
Press Release Europe and India join forces to make more biological models available for research Hinxton, 4 December 2006 – The BioModels Since its launch in April 2005, BioModels has provid- Database, hosted by the European Molecular Biology ed access to published, peer-reviewed models of bio- Laboratory’s European Bioinformatics Institute chemical and cell-biological systems. Its models are (EMBL-EBI) in Cambridge, UK, has entered a formal annotated and linked to other biological data data-exchange agreement with the Database of resources. DOQCS has operated with similar goals Quantitative Chemical Signalling (DOQCS) of the since 2003, focusing on models of neuronal systems National Centre for Biological Sciences (NCBS) in and providing additional notes on how the models Bangalore, India. Both data resources will now simul- were developed. The merged collections of BioModels taneously release computer models of biological Database and DOQCS, will include more than 250 bio- processes to the community. This agreement is the cul- logical models, covering over 5000 reactions. mination of more than a year’s work to make the mod- Computer models are an important tool to visualize els in DOQCS available in Systems Biology Markup and understand complex biological processes and sci- Language (SBML). The agreement will provide users entists worldwide will benefit from the new of both databases with a larger pool of models, whilst Europe–India collaboration, which will make it easier giving them the freedom to use the different interfaces to access
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Systems Biology
GENERAL ARTICLE Systems Biology Karthik Raman and Nagasuma Chandra Systems biology seeks to study biological systems as a whole, contrary to the reductionist approach that has dominated biology. Such a view of biological systems emanating from strong foundations of molecular level understanding of the individual components in terms of their form, function and Karthik Raman recently interactions is promising to transform the level at which we completed his PhD in understand biology. Systems are defined and abstracted at computational systems biology different levels, which are simulated and analysed using from IISc, Bangalore. He is different types of mathematical and computational tech- currently a postdoctoral research associate in the niques. Insights obtained from systems level studies readily Department of Biochemistry, lend to their use in several applications in biotechnology and at the University of Zurich. drug discovery, making it even more important to study His research interests include systems as a whole. the modelling of complex biological networks and the analysis of their robustness 1. Introduction and evolvability. Nagasuma Chandra obtained Biological systems are enormously complex, organised across her PhD in structural biology several levels of hierarchy. At the core of this organisation is the from the University of Bristol, genome that contains information in a digital form to make UK. She serves on the faculty thousands of different molecules and drive various biological of Bioinformatics at IISc, Bangalore. Her current processes. This genomic view of biology has been primarily research interests are in ushered in by the human genome project. The development of computational systems sequencing and other high-throughput technologies that generate biology, cell modeling and vast amounts of biological data has fuelled the development of structural bioinformatics and in applying these to address new ways of hypothesis-driven research.
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Biochemistry Biostatistics and Bioinformatics Modeling
1 Biostatistics and Bioinformatics Biochemistry Modeling Biochemical Pathways Description of Module Subject Name Biochemistry Paper Name 13 Biostatistics and Bioinformatics Module Name/Title 16 Modeling Biochemical Pathways Dr. Vijaya Khader Dr. MC Varadaraj 2 Biostatistics and Bioinformatics Biochemistry Modeling Biochemical Pathways 1. Objectives: In this module, the students will understand 1. Biochemical Pathways and Pathways databases 2. Biochemical Pathway models and models databases 3. Simulating Biochemical Pathways 4. Modeling Biochemical Pathways using COPASI Brief Description Biochemical Pathways Biochemical Pathway Models Simulating Biochemical Pathway Modeling Biochemical Pathway Summary 2. Brief Description Dear students, Biochemistry is the study of enzyme catalysed reactions. The product of one enzyme catalyzed reaction may form the substrate of second enzyme catalysed reaction to yield a new product. In the second reaction, the product of first reaction acts as substrate to take the reaction forward to form another product. In addition, the product of the first reaction may bind to first enzyme to take the first reaction backward to form the first substrate. Further, the new product of the second reaction may form the substrate of third enzyme catalysed reaction and so on. Therefore, in this way, various substrates and products (collectively known as metabolites) are chained together in series, to yield, what we call the metabolic pathways. Consequently, metabolic pathway occurring within a cell is a series of enzyme catalysed chemical reactions starting with a substrate, chained together through intermediate metabolites to yield the final end product. The rate of turnover of these metabolites in a metabolic pathway is called Flux, or metabolic flux. 3 Biostatistics and Bioinformatics Biochemistry Modeling Biochemical Pathways In addition, the end product of one pathway may be used immediately to initiate another metabolic pathway.
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Modelbricks—Modules for Reproducible Modeling Improving Model Annotation and Provenance
www.nature.com/npjsba PERSPECTIVE OPEN ModelBricks—modules for reproducible modeling improving model annotation and provenance Ann E. Cowan 1,2, Pedro Mendes 1,3,4 and Michael L. Blinov 1,5* Most computational models in biology are built and intended for “single-use”; the lack of appropriate annotation creates models where the assumptions are unknown, and model elements are not uniquely identified. Simply recreating a simulation result from a publication can be daunting; expanding models to new and more complex situations is a herculean task. As a result, new models are almost always created anew, repeating literature searches for kinetic parameters, initial conditions and modeling specifics. It is akin to building a brick house starting with a pile of clay. Here we discuss a concept for building annotated, reusable models, by starting with small well-annotated modules we call ModelBricks. Curated ModelBricks, accessible through an open database, could be used to construct new models that will inherit ModelBricks annotations and thus be easier to understand and reuse. Key features of ModelBricks include reliance on a commonly used standard language (SBML), rule-based specification describing species as a collection of uniquely identifiable molecules, association with model specific numerical parameters, and more common annotations. Physical bricks can vary substantively; likewise, to be useful the structure of ModelBricks must be highly flexible—it should encapsulate mechanisms from single reactions to multiple reactions in a complex process. Ultimately, a modeler would be able to construct large models by using multiple ModelBricks, preserving annotations and provenance of model elements, resulting in a highly annotated model.
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Biomodels Linked Dataset Sarala M Wimalaratne1*, Pierre Grenon2, Henning Hermjakob1, Nicolas Le Novère1,3 and Camille Laibe1
Wimalaratne et al. BMC Systems Biology 2014, 8:91 http://www.biomedcentral.com/1752-0509/8/91 METHODOLOGY ARTICLE Open Access BioModels linked dataset Sarala M Wimalaratne1*, Pierre Grenon2, Henning Hermjakob1, Nicolas Le Novère1,3 and Camille Laibe1 Abstract Background: BioModels Database is a reference repository of mathematical models used in biology. Models are stored as SBML files on a file system and metadata is provided in a relational database. Models can be retrieved through a web interface and programmatically via web services. In addition to those more traditional ways to access information, Linked Data using Semantic Web technologies (such as the Resource Description Framework, RDF), is becoming an increasingly popular means to describe and expose biological relevant data. Results: We present the BioModels Linked Dataset, which exposes the models’ content as a dereferencable interlinked dataset. BioModels Linked Dataset makes use of the wealth of annotations available within a large number of manually curated models to link and integrate data and models from other resources. Conclusions: The BioModels Linked Dataset provides users with a dataset interoperable with other semantic web resources. It supports powerful search queries, some of which were not previously available to users and allow integration of data from multiple resources. This provides a distributed platform to find similar models for comparison, processing and enrichment. Keywords: BioModels database, Semantic web, Resource description framework, Linked data, SPARQL Background to an external resource is specified using terms from a con- The number of mathematical models of biological pro- trolled vocabulary [13]. cesses published over the last decade has grown in part The model files are stored in a file system, while meta- due to standard format and software tool development ef- data, such as elements’ name and annotations, are stored forts in the Systems Biology community.
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Down‐Regulation of Mir‐135B in Colon Adenocarcinoma Induced by a TGF‐Β Receptor I Kinase Inhibitor (SD‐208)
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