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Your search keyword '"Martin, Golebiewski"' showing total 14 results
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14 results on '"Martin, Golebiewski"'

1. The AIMe registry for artificial intelligence in biomedical research

Author

Dominik G. Grimm, Zakaria Louadi, Gerda Cristal Villalba Silva, Tim Kacprowski, Massimiliano Zanin, Jan Baumbach, Markus List, Martiela Vaz de Freitas, Veit Schwämmle, Nico Pfeifer, Nicolas Alcaraz, Kristel Van Steen, Alberto Traverso, Leonard Wee, Arriel Benis, David B. Blumenthal, Olga Lazareva, Julian Matschinske, Gregor Sturm, Nina K. Wenke, Olga Zolotareva, Lukas Heumos, Josch Konstantin Pauling, Martin Golebiewski, Richard Röttger, Radiotherapie, and RS: GROW - R3 - Innovative Cancer Diagnostics & Therapy

Subjects
Standards, Engineering, Biomedical Research, GUIDELINES, Biochemistry, GeneralLiterature_MISCELLANEOUS, 03 medical and health sciences, 0302 clinical medicine, Artificial Intelligence, REPRODUCIBILITY, Machine learning, Registries, Molecular Biology, Biomedicine, 030304 developmental biology, 0303 health sciences, business.industry, Computational Biology, Usability, Cell Biology, Data science, OPEN SCIENCE, ComputingMethodologies_PATTERNRECOGNITION, BIAS, Research Design, business, Software, 030217 neurology & neurosurgery, Biotechnology
Abstract

We present the AIMe registry, a community-driven reporting platform for AI in biomedicine. It aims to enhance the accessibility, reproducibility and usability of biomedical AI models, and allows future revisions by the community.

Published
2021
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2. Facilitating Study and Item Level Browsing for Clinical and Epidemiological COVID-19 Studies

Author

Christian R. Bauer, Dagmar Waltemath, Atinkut Alamirrew Zeleke, Vivien Junker, Iris Pigeot, Bastian Seifert, Aliaksandra Shutsko, Ulrich Sax, Rajini Nagrani, Matthias Löbe, Johannes Darms, Martin Golebiewski, Theresa Bender, Carsten Oliver Schmidt, Juliane Fluck, Xiaoming Hu, Birte Lindstädt, Michael Lieser, Sophie Anne Ines Klopfenstein, and Sofiya Koleva

Subjects
medicine.medical_specialty, Coronavirus disease 2019 (COVID-19), Computer science, Relational database, Public health, Metadata modeling, 030210 environmental & occupational health, 3. Good health, Metadata, Clinical trial, World Wide Web, 03 medical and health sciences, 0302 clinical medicine, Epidemiology, medicine, 030212 general & internal medicine, User needs
Abstract

COVID-19 poses a major challenge to individuals and societies around the world. Yet, it is difficult to obtain a good overview of studies across different medical fields of research such as clinical trials, epidemiology, and public health. Here, we describe a consensus metadata model to facilitate structured searches of COVID-19 studies and resources along with its implementation in three linked complementary web-based platforms. A relational database serves as central study metadata hub that secures compatibilities with common trials registries (e.g. ICTRP and standards like HL7 FHIR, CDISC ODM, and DataCite). The Central Search Hub was developed as a single-page application, the other two components with additional frontends are based on the SEEK platform and MICA, respectively. These platforms have different features concerning cohort browsing, item browsing, and access to documents and other study resources to meet divergent user needs. By this we want to promote transparent and harmonized COVID-19 research.

Published
2021
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3. COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host interaction mechanisms

Author

Dezso Modos, Julia Scheel, Ralf Stephan, Piotr Gawron, Kristina Hanspers, Jean-Marie Ravel, Alexander Mazein, Corinna Montrone, Alberto Valdeolivas, Marek Ostaszewski, Susan L. Coort, Hiroaki Kitano, Tom C. Freeman, Vera Ortseifen, Vanessa Nakonecnij, M Orlic-Milacic, Jan Hasenauer, Veronica Shamovsky, Venkata P. Satagopam, Goar Frishman, M. Vazquez, D A B Rex, Miguel Ponce de Leon, Sylvain Soliman, Luana Licata, Ebru Kocakaya, Kinza Rian, Yusuke Hiki, Marton Olbei, Martina Kutmon, Bijay Jassal, Akira Funahashi, Lisa Matthews, Xiaoming Hu, Marcio Luis Acencio, Zsolt Bocskei, Robin Haw, Joaquín Dopazo, Agatha Treveil, Andrea Senff-Ribeiro, Tomáš Helikar, Chris T. Evelo, John A. Bachman, Julio Saez-Rodriguez, Takahiro G. Yamada, Emmanuel Barillot, Anton Yuryev, Bertrand De Meulder, Matti Hoch, Guanming Wu, Daniela Börnigen, Rudi Balling, Karen Rothfels, Pablo Porras, Hanna Borlinghaus, Andreas Dräger, Aurélien Naldi, Marta Iannuccelli, Leonard Schmiester, Dieter Maier, Jason E. Shoemaker, Valentin Grouès, Jakob Vanhoefer, Marius Rameil, Sara Sadat Aghamiri, Tobias Czauderna, Inna Kuperstein, Nhung Pham, Reinhard Schneider, Marina Esteban-Medina, Carole Goble, Benjamin M. Gyori, Emily E. Ackerman, Anita de Waard, Bhanwar Lal Puniya, Olaf Wolkenhauer, Alfonso Valencia, Julia Somers, Ewa Smula, Gisela Fobo, Robert Phair, Chris Sander, Laurent Heirendt, Carlos Vega, Jeremy Teuton, Emek Demir, Alina Renz, Tamas Korcsmaros, Vidisha Singh, Anastasia P. Nesterova, Andreas Ruepp, Lars M. T. Eijssen, Thawfeek M. Varusai, Aurelien Dugourd, Silvia Marchesi, Cristoffer Sevilla, Falk Schreiber, Barbara Brauner, Noriko Hiroi, Augustin Luna, Muying Wang, Shailendra K. Gupta, Egon Willighagen, Charles Auffray, Denise Slenter, Kristie L. Oxford, Franck Augé, Friederike Ehrhart, Muhammad Naveez, Stuart Owen, Arnau Montagud, Liam Fergusson, Enrico Glaab, Martin Golebiewski, Laurence Calzone, Alexander R. Pico, Dénes Türei, Marvin Martens, Henning Hermjakob, Rupert W. Overall, Jeremy Zucker, Özgün Babur, Gokce Yagmur Summak, Lincoln Stein, Anders Riutta, Anna Niarakis, Aurelio Orta-Resendiz, Francesca Sacco, Maria Peña-Chilet, Marta Conti, Jacques S. Beckmann, Angela Bauch, and University of Luxembourg [Luxembourg]

Subjects
0303 health sciences, Coronavirus disease 2019 (COVID-19), Computer science, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Interoperability, Disease, Data science, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Domain (software engineering), 03 medical and health sciences, 0302 clinical medicine, 030220 oncology & carcinogenesis, Relevance (information retrieval), 030304 developmental biology
Abstract

We describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in the enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.; We describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in the enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.

Published
2021

4. COVID-19-Forschungsdaten leichter zugänglich machen – Aufbau einer bundesweiten Informationsinfrastruktur

Author

Horst K. Hahn, Iris Pigeot, Ulrich Sax, Matthias Löbe, Carsten Oliver Schmidt, Linus Grabenhenrich, Martin Golebiewski, Sebastian Klammt, Sylvia Thun, Toralf Kirsten, and Juliane Fluck

Subjects
0301 basic medicine, medicine.medical_specialty, Epidemiology, Computer science, Interoperability, COVID-19-Studienportal, Context (language use), COVID-19, Public health, COVID-19 study portal, Clinical trials, Klinische Studien, Epidemiologie, FAIR principles, FAIR-Prinzipien, Public Health, 03 medical and health sciences, 0302 clinical medicine, medicine, ddc:610, 030212 general & internal medicine, Public Health, Environmental and Occupational Health, Findability, Data science, Metadata, 030104 developmental biology, Action (philosophy), Work (electrical), Information infrastructure, 610 Medizin und Gesundheit
Abstract

Public-Health-Forschung, epidemiologische und klinische Studien sind erforderlich, um die COVID-19-Pandemie besser zu verstehen und geeignete Ma��nahmen zu ergreifen. Daher wurden auch in Deutschland zahlreiche Forschungsprojekte initiiert. Zum heutigen Zeitpunkt ist es ob der F��lle an Informationen jedoch kaum noch m��glich, einen ��berblick ��ber die vielf��ltigen Forschungsaktivit��ten und deren Ergebnisse zu erhalten. Im Rahmen der Initiative ���Nationale Forschungsdateninfrastruktur f��r personenbezogene Gesundheitsdaten��� (NFDI4Health) schafft die ���Task Force COVID-19��� einen leichteren Zugang zu SARS-CoV-2- und COVID-19-bezogenen klinischen, epidemiologischen und Public-Health-Forschungsdaten. Dabei werden die sogenannten FAIR-Prinzipien (Findable, Accessible, Interoperable, Reusable) ber��cksichtigt, die eine schnellere Kommunikation von Ergebnissen bef��rdern sollen. Zu den wesentlichen Arbeitsinhalten der Taskforce geh��ren die Erstellung eines Studienportals mit Metadaten, Erhebungsinstrumenten, Studiendokumenten, Studienergebnissen und Ver��ffentlichungen sowie einer Suchmaschine f��r Preprint-Publikationen. Weitere Inhalte sind ein Konzept zur Verkn��pfung von Forschungs- und Routinedaten, Services zum verbesserten Umgang mit Bilddaten und die Anwendung standardisierter Analyseroutinen f��r harmonisierte Qualit��tsbewertungen. Die im Aufbau befindliche Infrastruktur erleichtert die Auffindbarkeit von und den Umgang mit deutscher COVID-19-Forschung. Die im Rahmen der NFDI4Health Task Force COVID-19 begonnenen Entwicklungen sind f��r weitere Forschungsthemen nachnutzbar, da die adressierten Herausforderungen generisch f��r die Auffindbarkeit von und den Umgang mit Forschungsdaten sind., Public health research and epidemiological and clinical studies are necessary to understand the COVID-19 pandemic and to take appropriate action. Therefore, since early 2020, numerous research projects have also been initiated in Germany. However, due to the large amount of information, it is currently difficult to get an overview of the diverse research activities and their results. Based on the ���Federated research data infrastructure for personal health data��� (NFDI4Health) initiative, the ���COVID-19 task force��� is able to create easier access to SARS-CoV-2- and COVID-19-related clinical, epidemiological, and public health research data. Therefore, the so-called FAIR data principles (findable, accessible, interoperable, reusable) are taken into account and should allow an expedited communication of results. The most essential work of the task force includes the generation of a study portal with metadata, selected instruments, other study documents, and study results as well as a search engine for preprint publications. Additional contents include a concept for the linkage between research and routine data, a service for an enhanced practice of image data, and the application of a standardized analysis routine for harmonized quality assessment. This infrastructure, currently being established, will facilitate the findability and handling of German COVID-19 research. The developments initiated in the context of the NFDI4Health COVID-19 task force are reusable for further research topics, as the challenges addressed are generic for the findability of and the handling with research data.

Published
2021

5. Towards standardization guidelines for in silico approaches in personalized medicine

Author

Ingrid Kockum, Martin Golebiewski, Heike Moser, Marc Kirschner, Katharina Ó Cathaoir, Catherine Bjerre Collin, Dagmar Waltemath, and Søren Brunak

Subjects
Standardization, Status quo, Computer science, Process (engineering), In silico, media_common.quotation_subject, computer.software_genre, in silico modelling, Health data, 03 medical and health sciences, 0302 clinical medicine, Humans, Computer Simulation, Precision Medicine, Workshop, data integration, reproducibility, 030304 developmental biology, media_common, 0303 health sciences, business.industry, personalized medicine, General Medicine, Reference Standards, Data science, Analytics, 030220 oncology & carcinogenesis, standards, Personalized medicine, business, computer, TP248.13-248.65, Biotechnology, Data integration
Abstract

Despite the ever-progressing technological advances in producing data in health and clinical research, the generation of new knowledge for medical benefits through advanced analytics still lags behind its full potential. Reasons for this obstacle are the inherent heterogeneity of data sources and the lack of broadly accepted standards. Further hurdles are associated with legal and ethical issues surrounding the use of personal/patient data across disciplines and borders. Consequently, there is a need for broadly applicable standards compliant with legal and ethical regulations that allow interpretation of heterogeneous health data through in silico methodologies to advance personalized medicine. To tackle these standardization challenges, the Horizon2020 Coordinating and Support Action EU-STANDS4PM initiated an EU-wide mapping process to evaluate strategies for data integration and data-driven in silico modelling approaches to develop standards, recommendations and guidelines for personalized medicine. A first step towards this goal is a broad stakeholder consultation process initiated by an EU-STANDS4PM workshop at the annual COMBINE meeting (COMBINE 2019 workshop report in same issue). This forum analysed the status quo of data and model standards and reflected on possibilities as well as challenges for cross-domain data integration to facilitate in silico modelling approaches for personalized medicine.

Published
2020
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6. Specifications of standards in systems and synthetic biology: status and developments in 2020

Author

Sarah M. Keating, Matthias König, Thomas E. Gorochowski, Michael Hucka, Dagmar Waltemath, Björn Sommer, David P. Nickerson, Falk Schreiber, Tobias Czauderna, Chris J. Myers, and Martin Golebiewski

Subjects
0303 health sciences, Integrative bioinformatics, Computer science, Systems biology, Computational Biology, systems biology, General Medicine, Reference Standards, Data science, 03 medical and health sciences, Synthetic biology, Editorial, 0302 clinical medicine, standards, ontologies, Synthetic Biology, GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), TP248.13-248.65, 030217 neurology & neurosurgery, Biotechnology, 030304 developmental biology
Abstract

This special issue of the Journal of Integrative Bioinformatics presents papers related to the 10th COMBINE meeting together with the annual update of COMBINE standards in systems and synthetic biology.

Published
2020
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7. The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE)

Author

Michael L. Blinov, Olga Krebs, Henning Hermjakob, David P. Nickerson, Sarah M. Keating, Ulrike Wittig, Matthias König, Padraig Gleeson, Melanie I. Stefan, Rahuman S Malik-Sheriff, Esther Thea Inau, Lucian P. Smith, Michael Hucka, Dagmar Waltemath, Falk Schreiber, Herbert M. Sauro, Chris J. Myers, Martin Golebiewski, and Ernst Oberortner

Subjects
Community building, Standardization, Interoperability, COMBINE, Field (computer science), Article, 03 medical and health sciences, 0302 clinical medicine, Germany, Community standards, 030304 developmental biology, meeting report, standardization, 0303 health sciences, Event (computing), Stakeholder, Computational Biology, General Medicine, Reference Standards, Data science, community building, Data sharing, Synthetic Biology, ddc:004, 030217 neurology & neurosurgery, TP248.13-248.65, Software, Biotechnology
Abstract

This paper presents a report on outcomes of the 10th Computational Modeling in Biology Network (COMBINE) meeting that was held in Heidelberg, Germany, in July of 2019. The annual event brings together researchers, biocurators and software engineers to present recent results and discuss future work in the area of standards for systems and synthetic biology. The COMBINE initiative coordinates the development of various community standards and formats for computational models in the life sciences. Over the past 10 years, COMBINE has brought together standard communities that have further developed and harmonized their standards for better interoperability of models and data. COMBINE 2019 was co-located with a stakeholder workshop of the European EU-STANDS4PM initiative that aims at harmonized data and model standardization for in silico models in the field of personalized medicine, as well as with the FAIRDOM PALs meeting to discuss findable, accessible, interoperable and reusable (FAIR) data sharing. This report briefly describes the work discussed in invited and contributed talks as well as during breakout sessions. It also highlights recent advancements in data, model, and annotation standardization efforts. Finally, this report concludes with some challenges and opportunities that this community will face during the next 10 years.

Published
2020

8. SBML Level 3: an extensible format for the exchange and reuse of biological models

Author

Edda Klipp, Marco Antoniotti, Frank Bergmann, James C. Schaff, Peter D. Karp, Daniel Lucio, Kedar Nath Natarajan, Thomas M. Hamm, Leandro Watanabe, Henning Hermjakob, David Tolnay, John Wagner, Joerg Stelling, Alida Palmisano, Falk Schreiber, Yukiko Matsuoka, Harold F. Gómez, Huaiyu Mi, Carole J. Proctor, Ulrike Wittig, Neil Swainston, Jan Červený, Denis Thieffry, Piero Dalle Pezze, Julio Saez-Rodriguez, Maciej J. Swat, Bin Hu, Martina Kutmon, Thomas Pfau, Bas Teusink, Sarah M. Keating, Fedor A. Kolpakov, Andreas Dräger, Pedro Mendes, Martin Scharm, Emek Demir, Ioannis Xenarios, Christoph Flamm, Axel von Kamp, Darren J. Wilkinson, Nick Juty, Fengkai Zhang, Leonard A. Harris, Michael Schubert, Dagmar Waltemath, Lucian P. Smith, Steffen Klamt, Herbert M. Sauro, Ali Ebrahim, Wolfram Liebermeister, Christian Knüpfer, Nicolas Rodriguez, Tramy Nguyen, Naoki Tanimura, Christopher Cox, Stuart C. Sealfon, Nicholas Alexander Allen, Clemens Wrzodek, Bastian R. Angermann, Martin Meier-Schellersheim, Anna Zhukova, Jean-Baptiste Pettit, Hovakim Grabski, Devin P. Sullivan, Claudine Chaouiya, Michael L. Blinov, John Doyle, Ilya Kiselev, Roman Schulte, Alex Gutteridge, Mélanie Courtot, Eric Mjolsness, Finja Wrzodek, Rahuman S Malik-Sheriff, Ronan M. T. Fleming, Bruce E. Shapiro, Kimberly Begley, Leslie M. Loew, Colin S. Gillespie, Ibrahim Vazirabad, Michael Hucka, Akira Funahashi, Bernhard O. Palsson, Hamid Bolouri, Tomáš Helikar, Camille Laibe, William S. Denney, Chris T. Evelo, Florian Mittag, William S. Hlavacek, Ron Henkel, Harish Dharuri, Julien Dorier, Karthik Raman, Martina Fröhlich, Conor Lawless, Rainer Machné, Falko Krause, Damon Hachmeister, Matthias König, Clifford A. Shaffer, Benjamin D. Heavner, Douglas B. Kell, Jonathan R. Karr, Mihai Glont, Lukas Endler, Melanie I. Stefan, Robert Phair, Lu Li, Henning Schmidt, Dirk Drasdo, Johan Elf, Allyson L. Lister, Hiroaki Kitano, Richard R. Adams, Oliver A. Ruebenacker, Roland Keller, Sven Sahle, Ion I. Moraru, Gary D. Bader, Poul M. F. Nielsen, Johann M. Rohwer, Johannes Eichner, Daniel R. Hyduke, James R. Faeder, Stefan Hoops, Emanuel Gonçalves, Yuichiro Inagaki, Aurélien Naldi, Koichi Takahashi, Sylvain Soliman, Brett G. Olivier, Kieran Smallbone, Stuart L. Moodie, Pedro T. Monteiro, Chris J. Myers, Martin Golebiewski, Tomas Radivoyevitch, Jeremy Zucker, Hidde de Jong, Andrew Finney, Keating, S, Waltemath, D, König, M, Zhang, F, Dräger, A, Chaouiya, C, Bergmann, F, Finney, A, Gillespie, C, Helikar, T, Hoops, S, Malik-Sheriff, R, Moodie, S, Moraru, I, Myers, C, Naldi, A, Olivier, B, Sahle, S, Schaff, J, Smith, L, Swat, M, Thieffry, D, Watanabe, L, Wilkinson, D, Blinov, M, Begley, K, Faeder, J, Gómez, H, Hamm, T, Inagaki, Y, Liebermeister, W, Lister, A, Lucio, D, Mjolsness, E, Proctor, C, Raman, K, Rodriguez, N, Shaffer, C, Shapiro, B, Stelling, J, Swainston, N, Tanimura, N, Wagner, J, Meier-Schellersheim, M, Sauro, H, Palsson, B, Bolouri, H, Kitano, H, Funahashi, A, Hermjakob, H, Doyle, J, Hucka, M, Adams, R, Allen, N, Angermann, B, Antoniotti, M, Bader, G, Červený, J, Courtot, M, Cox, C, Dalle Pezze, P, Demir, E, Denney, W, Dharuri, H, Dorier, J, Drasdo, D, Ebrahim, A, Eichner, J, Elf, J, Endler, L, Evelo, C, Flamm, C, Fleming, R, Fröhlich, M, Glont, M, Gonçalves, E, Golebiewski, M, Grabski, H, Gutteridge, A, Hachmeister, D, Harris, L, Heavner, B, Henkel, R, Hlavacek, W, Hu, B, Hyduke, D, Jong, H, Juty, N, Karp, P, Karr, J, Kell, D, Keller, R, Kiselev, I, Klamt, S, Klipp, E, Knüpfer, C, Kolpakov, F, Krause, F, Kutmon, M, Laibe, C, Lawless, C, Li, L, Loew, L, Machne, R, Matsuoka, Y, Mendes, P, Mi, H, Mittag, F, Monteiro, P, Natarajan, K, Nielsen, P, Nguyen, T, Palmisano, A, Jean-Baptiste, P, Pfau, T, Phair, R, Radivoyevitch, T, Rohwer, J, Ruebenacker, O, Saez-Rodriguez, J, Scharm, M, Schmidt, H, Schreiber, F, Schubert, M, Schulte, R, Sealfon, S, Smallbone, K, Soliman, S, Stefan, M, Sullivan, D, Takahashi, K, Teusink, B, Tolnay, D, Vazirabad, I, Kamp, A, Wittig, U, Wrzodek, C, Wrzodek, F, Xenarios, I, Zhukova, A, Zucker, J, European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, Heidelberg University Hospital [Heidelberg], Swiss Institute of Bioinformatics [Lausanne] (SIB), Université de Lausanne = University of Lausanne (UNIL), European Molecular Biology Laboratory (EMBL), University of Connecticut (UCONN), National Institutes of Health [Bethesda] (NIH), Chercheur indépendant, Amazon Web Services [Seattle] (AWS), Università degli Studi di Milano-Bicocca = University of Milano-Bicocca (UNIMIB), University of Toronto, Masaryk University [Brno] (MUNI), Terry Fox Laboratory, BC Cancer Agency (BCCRC)-British Columbia Cancer Agency Research Centre, The University of Tennessee [Knoxville], The Babraham Institute [Cambridge, UK], Oregon Health and Science University [Portland] (OHSU), Human Predictions LLC, Illumina, Swiss-Prot Group, Swiss Institute of Bioinformatics [Genève] (SIB), Modelling and Analysis for Medical and Biological Applications (MAMBA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jacques-Louis Lions (LJLL (UMR_7598)), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), University of California [San Diego] (UC San Diego), University of California (UC), Center for Bioinformatics (ZBIT), Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Uppsala University, Institut für Populationsgenetik [Vienna], Veterinärmedizinische Universität Wien, Maastricht University [Maastricht], Alpen-Adria-Universität Klagenfurt [Klagenfurt, Austria], Medizinische Universität Wien = Medical University of Vienna, German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Heidelberg Institute for Theoretical Studies (HITS ), Russian-Armenian University (RAU), GlaxoSmithKline [Stevenage, UK] (GSK), GlaxoSmithKline [Headquarters, London, UK] (GSK), Microsoft Technology Licensing (MTL), Microsoft Corporation [Redmond, Wash.], Vanderbilt University School of Medicine [Nashville], University of Washington [Seattle], University of Rostock, Los Alamos National Laboratory (LANL), Lorentz Institute, Universiteit Leiden, Tegmine Therapeutics, Modeling, simulation, measurement, and control of bacterial regulatory networks (IBIS), Laboratoire Adaptation et pathogénie des micro-organismes [Grenoble] (LAPM), Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Jean Roget, SRI International [Menlo Park] (SRI), Icahn School of Medicine at Mount Sinai [New York] (MSSM), University of Liverpool, Universitätsklinikum Tübingen - University Hospital of Tübingen, Institute of Information and Computational Technologies (IICT), Max Planck Institute for Dynamics of Complex Technical Systems, Max-Planck-Gesellschaft, Max-Planck-Institut für Molekulare Genetik (MPIMG), Friedrich-Schiller-Universität = Friedrich Schiller University Jena [Jena, Germany], Humboldt University Of Berlin, Newcastle University [Newcastle], École polytechnique (X), Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], The Systems Biology Institute [Tokyo] (SBI), Centro de Quimica Estrutural (CQE), Instituto Superior Técnico, Universidade Técnica de Lisboa (IST), University of Southern California (USC), Instituto Gulbenkian de Ciência [Oeiras] (IGC), Fundação Calouste Gulbenkian, University of Southern Denmark (SDU), University of Auckland [Auckland], University of Utah, Virginia Tech [Blacksburg], University of Luxembourg [Luxembourg], Integrative Bioinformatics Inc [Mountain View], Cleveland Clinic, Stellenbosch University, Broad Institute of MIT and Harvard (BROAD INSTITUTE), Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], Universität Heidelberg [Heidelberg] = Heidelberg University, Leibniz Institute of Plant Genetics and Crop Plant Research [Gatersleben] (IPK-Gatersleben), Laboratoire de Biologie du Développement de Villefranche sur mer (LBDV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Mount Sinai School of Medicine, Department of Psychiatry-Icahn School of Medicine at Mount Sinai [New York] (MSSM), University of Manchester [Manchester], Computational systems biology and optimization (Lifeware), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), California Institute of Technology (CALTECH), Encodia Inc [San Diego], Shinshu University [Nagano], University of Amsterdam [Amsterdam] (UvA), Versiti Blood Center of Wisconsin, Greifswald University Hospital, Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Pacific Northwest National Laboratory (PNNL), National Institute of Allergy and Infectious Diseases [Bethesda] (NIAID-NIH), Department of Bioengineering, University of California (UC)-University of California (UC), ANSYS, Virginia Polytechnic Institute and State University [Blacksburg], Eight Pillars Ltd, Center for Integrative Genomics - Institute of Bioinformatics, Génopode (CIG), Université de Lausanne = University of Lausanne (UNIL)-Université de Lausanne = University of Lausanne (UNIL), Universität Heidelberg, Bioquant, Applied Biomathematics [New York], SimCYP Ltd, Institut de biologie de l'ENS Paris (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), University of Utah School of Medicine [Salt Lake City], University of Pittsburgh School of Medicine, Pennsylvania Commonwealth System of Higher Education (PCSHE), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Mizuho Information and Research Institute, Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Oxford, Computer Science (North Carolina State University), North Carolina State University [Raleigh] (NC State), University of North Carolina System (UNC)-University of North Carolina System (UNC), University of California [Irvine] (UC Irvine), Indian Institute of Technology Madras (IIT Madras), California State University [Northridge] (CSUN), Biotechnology and Biological Sciences Research Council (BBSRC), IBM Research [Melbourne], Benaroya Research Institute [Seattle] (BRI), Okinawa Institute of Science and Technology Graduate University, Keio University, Department of Computing and Mathematical sciences, members, SBML Level 3 Community, Université de Lausanne (UNIL), Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), University of California, Universiteit Leiden [Leiden], Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, Humboldt University of Berlin, Universität Heidelberg [Heidelberg], Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Humboldt-Universität zu Berlin, University of California-University of California, Université de Lausanne (UNIL)-Université de Lausanne (UNIL), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Oxford [Oxford], University of California [Irvine] (UCI), Biotechnology and Biological Sciences Research Council, Computer Science, Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)

Subjects
computational modeling, Medicine (General), Markup language, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, INFORMATION, Interoperability, interoperability, Review, [SDV.BC.BC]Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC], ANNOTATION, 0302 clinical medicine, Software, file forma, Models, Biology (General), 0303 health sciences, Computational model, Applied Mathematics, Systems Biology, systems biology, File format, 3. Good health, Networking and Information Technology R&D, Networking and Information Technology R&D (NITRD), Computational Theory and Mathematics, SIMULATION, General Agricultural and Biological Sciences, STANDARDS, REPOSITORY, Information Systems, QH301-705.5, Bioinformatics, Systems biology, Software ecosystem, Reviews, Bioengineering, Methods & Resources, Biology, MARKUP LANGUAGE, Models, Biological, SBML Level 3 Community members, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, R5-920, Animals, Humans, SBML, reproducibility, 030304 developmental biology, ENVIRONMENT, General Immunology and Microbiology, file format, business.industry, Computational Biology, Biological, ONTOLOGY, Metabolism, Logistic Models, Biochemistry and Cell Biology, Other Biological Sciences, Software engineering, business, 030217 neurology & neurosurgery
Abstract

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution., Over the past two decades, scientists from different fields have been developing SBML, a standard format for encoding computational models in biology and medicine. This article summarizes recent progress and gives perspectives on emerging challenges.

Published
2020
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9. Specifications of Standards in Systems and Synthetic Biology : Status and Developments in 2019

Author

Falk Schreiber, Michael Hucka, Sarah M. Keating, Björn Sommer, Matthias König, David P. Nickerson, Gary D. Bader, Dagmar Waltemath, Padraig Gleeson, Chris J. Myers, and Martin Golebiewski

Subjects
Computer science, Systems biology, Models, Biological, 03 medical and health sciences, Synthetic biology, 0302 clinical medicine, BioPAX : Biological Pathways Exchange, Computer Simulation, SBML, 030304 developmental biology, 0303 health sciences, Integrative bioinformatics, business.industry, Systems Biology, CellML, General Medicine, Editorial, Process description, Programming Languages, Synthetic Biology, ddc:004, Software engineering, business, TP248.13-248.65, 030217 neurology & neurosurgery, Biotechnology
Abstract

This special issue of the Journal of Integrative Bioinformatics presents an overview of COMBINE standards and their latest specifications. The standards cover representation formats for computational modeling in synthetic and systems biology and include BioPAX, CellML, NeuroML, SBML, SBGN, SBOL and SED-ML. The articles in this issue contain updated specifications of SBGN Process Description Level 1 Version 2, SBML Level 3 Core Version 2 Release 2, SBOL Version 2.3.0, and SBOL Visual Version 2.1.

Published
2019

10. Harmonizing semantic annotations for computational models in biology

Author

Jacky L. Snoep, Brett G. Olivier, Maxwell Lewis Neal, Vasundra Touré, Samuel H. Friedman, Daniel L. Cook, Alan Garny, Chris J. Myers, Martin Golebiewski, David P. Nickerson, Michael T. Cooling, Olaf Wolkenhauer, Sharon M. Crook, Nick Juty, Goksel Misirli, Miguel de Alba, John H. Gennari, Martin Scharm, Koray Atalag, Herbert M. Sauro, Vijayalakshmi Chelliah, Dagmar Waltemath, Reza Kalbasi, Matthias König, Padraig Gleeson, Anil Wipat, Michael Hucka, Andreas Dräger, Systems Bioinformatics, AIMMS, and Molecular Cell Physiology

Subjects
0301 basic medicine, computational modeling, QA75, Databases, Factual, Knowledge representation and reasoning, Interoperability, Review Article, computer.software_genre, Biological Science Disciplines, semantic annotation, 03 medical and health sciences, Annotation, QH301, 0302 clinical medicine, Knowledge integration, Component (UML), Humans, Computer Simulation, Molecular Biology, Protocol (object-oriented programming), data integration, modeling standards, Computational model, knowledge representation, Computational Biology, Data science, Semantics, 030104 developmental biology, computer, Software, 030217 neurology & neurosurgery, Information Systems, Data integration, QD415
Abstract

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.

Published
2018
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11. Harmonizing semantic annotations for computational models in biology

Author

Padraig Gleeson, Anil Wipat, Andreas Dräger, Goksel Misirli, Samuel H. Friedman, Reza Kalbasi, Olaf Wolkenhauer, Matthias König, Daniel L. Cook, Michael T. Cooling, Brett G. Olivier, Chris J. Myers, Martin Golebiewski, Michael Hucka, Nick Juty, Miguel de Alba, Dagmar Waltemath, Maxwell Lewis Neal, Martin Scharm, David P. Nickerson, John H. Gennari, Sharon M. Crook, Vasundra Touré, Jacky L. Snoep, Alan Garny, Vijayalakshmi Chelliah, Koray Atalag, Herbert M. Sauro, and Nicolas Le Novère

Subjects
0303 health sciences, Computational model, Interoperability, Data science, Variety (cybernetics), Set (abstract data type), 03 medical and health sciences, Annotation, 0302 clinical medicine, Knowledge integration, Component (UML), Protocol (object-oriented programming), 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition, and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current semantic annotation practices among the COmputational Modeling in BIology NEtwork (COMBINE) community and provide a set of recommendations for building a consensus approach to semantic annotation.

Published
2018
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12. Promoting Coordinated Development of Community-Based Information Standards for Modeling in Biology: The COMBINE Initiative

Author

Frank Bergmann, Jonathan Cooper, Falk Schreiber, David P. Nickerson, Emek Demir, Michael Hucka, Gary D. Bader, Alan Garny, Nicolas La Novere, Chris J. Myers, Martin Golebiewski, and Dagmar Waltemath

Subjects
computational modeling, Histology, Standardization, Computer science, data sharing, lcsh:Biotechnology, Interoperability, Biomedical Engineering, Bioengineering, reproducible science, 03 medical and health sciences, File formats, 0302 clinical medicine, Resource (project management), lcsh:TP248.13-248.65, Community standards, Biology, 030304 developmental biology, standardization, 0303 health sciences, Computational model, Bioengineering and Biotechnology, File format, Data science, Data sharing, Facilitator, Perspective Article, ddc:004, 030217 neurology & neurosurgery, Biotechnology
Abstract

The Computational Modeling in Biology Network (COMBINE) is a consortium of groups involved in the development of open community standards and formats used in computational modeling in biology. COMBINE's aim is to act as a coordinator, facilitator, and resource for different standardization efforts whose domains of use cover related areas of the computational biology space. In this perspective article, we summarize COMBINE, its general organization, and the community standards and other efforts involved in it. Our goals are to help guide readers toward standards that may be suitable for their research activities, as well as to direct interested readers to relevant communities where they can best expect to receive assistance in how to develop interoperable computational models. published

Published
2015
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13. Meeting report from the fourth meeting of the Computational Modeling in Biology Network (COMBINE)

Author

Dagmar Waltemath, Frank T. Bergmann, Claudine Chaouiya, Tobias Czauderna, Padraig Gleeson, Carole Goble, Martin Golebiewski, Michael Hucka, Nick Juty, Olga Krebs, Nicolas Le Novère, Huaiyu Mi, Ion I. Moraru, Chris J. Myers, David Nickerson, Brett G. Olivier, Nicolas Rodriguez, Falk Schreiber, Lucian Smith, Fengkai Zhang, Eric Bonnet, Systems Bioinformatics, and AIMMS

Subjects
0303 health sciences, Markup language, Computer science, Computational Modeling, Modelling biological systems, Systems biology, CellML, Biology Network, Data science, SBML, Outreach, Meeting Reports, 03 medical and health sciences, 0302 clinical medicine, Genetics, BioPAX : Biological Pathways Exchange, Community standards, ddc:004, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

This report summarizes the topics and activities of the fourth edition of the annual COMBINE meeting, held in Paris during September 16-20 2013, The Computational Modeling in Biology Network (COMBINE) is an initiative to coordinate the development of community standards and formats in computational systems biology and related fields. This report summarizes the topics and activities of the fourth edition of the annual COMBINE meeting, held in Paris during September 16-20 2013, and attended by a total of 96 people. This edition pioneered a first day devoted to modeling approaches in biology, which attracted a broad audience of scientists thanks to a panel of renowned speakers. During subsequent days, discussions were held on many subjects including the introduction of new features in the various COMBINE standards, new software tools that use the standards, and outreach efforts. Significant emphasis went into work on extensions of the SBML format, and also into community-building. This year’s edition once again demonstrated that the COMBINE community is thriving, and still manages to help coordinate activities between different standards in computational systems biology. Foundation Pierre-Gilles de Gennes; Institut des Systèmes Complexes and the Groupement de Recherches en Bioinformatique Moléculaire du CNRS; NIH (intramural program of NIAID).

Published
2014
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14. SBML2L(A)T(E)X: conversion of SBML files into human-readable reports

Author

Hannes Planatscher, Lukas Endler, Dieudonné Motsou Wouamba, Martin Golebiewski, Adrian Schröder, Wolfgang Müller, Andreas Dräger, Andreas Zell, and Michael Hucka

Subjects
Statistics and Probability, Source code, Markup language, Computer science, computer.internet_protocol, media_common.quotation_subject, computer.software_genre, Biochemistry, Personalization, World Wide Web, 03 medical and health sciences, User-Computer Interface, 0302 clinical medicine, Documentation, Systems Biology Ontology, Humans, SBML, Molecular Biology, 030304 developmental biology, media_common, 0303 health sciences, Database, Systems Biology, Computer Science Applications, Computational Mathematics, Applications Note, Computational Theory and Mathematics, 030220 oncology & carcinogenesis, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, Database Management Systems, Programming Languages, Web service, computer, XML, Software
Abstract

Summary: The XML-based Systems Biology Markup Language (SBML) has emerged as a standard for storage, communication and interchange of models in systems biology. As a machine-readable format XML is difficult for humans to read and understand. Many tools are available that visualize the reaction pathways stored in SBML files, but many components, e.g. unit declarations, complex kinetic equations or links to MIRIAM resources, are often not made visible in these diagrams. For a broader understanding of the models, support in scientific writing and error detection, a human-readable report of the complete model is needed. We present SBML2LaTEX, a Java-based stand-alone program to fill this gap. A convenient web service allows users to directly convert SBML to various formats, including DVI, LaTEX and PDF, and provides many settings for customization. Availability: Source code, documentation and a web service are freely available at http://www.ra.cs.uni-tuebingen.de/software/SBML2LaTeX. Contact: andreas.draeger@uni-tuebingen.de Supplementary information: Supplementary data are available at Bioinformatics online.

Published
2009

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