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1. Towards an Interoperability Landscape for a National Research Data Infrastructure for Personal Health Data

Author

Carina Nina Vorisek, Sophie Anne Inès Klopfenstein, Matthias Löbe, Carsten Oliver Schmidt, Paula Josephine Mayer, Martin Golebiewski, and Sylvia Thun

Subjects
Science
Abstract

Abstract The German initiative “National Research Data Infrastructure for Personal Health Data” (NFDI4Health) focuses on research data management in health research. It aims to foster and develop harmonized informatics standards for public health, epidemiological studies, and clinical trials, facilitating access to relevant data and metadata standards. This publication lists syntactic and semantic data standards of potential use for NFDI4Health and beyond, based on interdisciplinary meetings and workshops, mappings of study questionnaires and the NFDI4Health metadata schema, and literature search. Included are 7 syntactic, 32 semantic and 9 combined syntactic and semantic standards. In addition, 101 ISO Standards from ISO/TC 215 Health Informatics and ISO/TC 276 Biotechnology could be identified as being potentially relevant. The work emphasizes the utilization of standards for epidemiological and health research data ensuring interoperability as well as the compatibility to NFDI4Health, its use cases, and to (inter-)national efforts within these sectors. The goal is to foster collaborative and inter-sectoral work in health research and initiate a debate around the potential of using common standards.

Published
2024
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5. Systems Biology in ELIXIR: modelling in the spotlight [version 2; peer review: 2 approved, 1 approved with reservations]

Author

Vitor Martins dos Santos, Mihail Anton, Barbara Szomolay, Marek Ostaszewski, Ilja Arts, Rui Benfeitas, Victoria Dominguez Del Angel, Elena Domínguez-Romero, Polonca Ferk, Dirk Fey, Carole Goble, Martin Golebiewski, Kristina Gruden, Katharina F. Heil, Henning Hermjakob, Pascal Kahlem, Maria I. Klapa, Jasper Koehorst, Alexey Kolodkin, Martina Kutmon, Brane Leskošek, Sébastien Moretti, Wolfgang Müller, Marco Pagni, Tadeja Rezen, Miguel Rocha, Damjana Rozman, David Šafránek, William T. Scott, Rahuman S. Malik Sheriff, Maria Suarez Diez, Kristel Van Steen, Hans V Westerhoff, Ulrike Wittig, Katherine Wolstencroft, Anze Zupanic, Chris T. Evelo, and John M. Hancock

Subjects
Opinion Article, Articles, Systems Biology, Systems Medicine, ELIXIR Communities, Biomolecular Models, Network Biology, FAIR, Biological data, Biotechnology
Abstract

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR’s future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

Published
2024
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6. Systems Biology in ELIXIR: modelling in the spotlight [version 2; peer review: 1 approved, 2 approved with reservations]

Author

Katharina F. Heil, Anze Zupanic, Chris T. Evelo, Carole Goble, Kristel Van Steen, Tadeja Rezen, Hans V Westerhoff, Rahuman S. Malik Sheriff, Maria Suarez Diez, Henning Hermjakob, Marco Pagni, Damjana Rozman, Barbara Szomolay, Miguel Rocha, Jasper Koehorst, Alexey Kolodkin, Victoria Dominguez Del Angel, David Šafránek, Dirk Fey, Ilja Arts, Rui Benfeitas, Vitor Martins dos Santos, Mihail Anton, Marek Ostaszewski, Ulrike Wittig, John M. Hancock, Brane Leskošek, Katherine Wolstencroft, Martin Golebiewski, Wolfgang Müller, Polonca Ferk, Kristina Gruden, William T. Scott, Elena Domínguez-Romero, Martina Kutmon, Maria I. Klapa, Sébastien Moretti, and Pascal Kahlem

Subjects
Systems Biology, Systems Medicine, ELIXIR Communities, Biomolecular Models, Network Biology, FAIR, eng, Medicine, Science
Abstract

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR’s future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

Published
2024
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9. Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

Author

Anna Niarakis, Marek Ostaszewski, Alexander Mazein, Inna Kuperstein, Martina Kutmon, Marc E. Gillespie, Akira Funahashi, Marcio Luis Acencio, Ahmed Hemedan, Michael Aichem, Karsten Klein, Tobias Czauderna, Felicia Burtscher, Takahiro G. Yamada, Yusuke Hiki, Noriko F. Hiroi, Finterly Hu, Nhung Pham, Friederike Ehrhart, Egon L. Willighagen, Alberto Valdeolivas, Aurelien Dugourd, Francesco Messina, Marina Esteban-Medina, Maria Peña-Chilet, Kinza Rian, Sylvain Soliman, Sara Sadat Aghamiri, Bhanwar Lal Puniya, Aurélien Naldi, Tomáš Helikar, Vidisha Singh, Marco Fariñas Fernández, Viviam Bermudez, Eirini Tsirvouli, Arnau Montagud, Vincent Noël, Miguel Ponce-de-Leon, Dieter Maier, Angela Bauch, Benjamin M. Gyori, John A. Bachman, Augustin Luna, Janet Piñero, Laura I. Furlong, Irina Balaur, Adrien Rougny, Yohan Jarosz, Rupert W. Overall, Robert Phair, Livia Perfetto, Lisa Matthews, Devasahayam Arokia Balaya Rex, Marija Orlic-Milacic, Luis Cristobal Monraz Gomez, Bertrand De Meulder, Jean Marie Ravel, Bijay Jassal, Venkata Satagopam, Guanming Wu, Martin Golebiewski, Piotr Gawron, Laurence Calzone, Jacques S. Beckmann, Chris T. Evelo, Peter D’Eustachio, Falk Schreiber, Julio Saez-Rodriguez, Joaquin Dopazo, Martin Kuiper, Alfonso Valencia, Olaf Wolkenhauer, Hiroaki Kitano, Emmanuel Barillot, Charles Auffray, Rudi Balling, Reinhard Schneider, and the COVID-19 Disease Map Community

Subjects
SARS-CoV-2, systems biology, disease maps, mechanistic models, dynamic models, systems medicine, Immunologic diseases. Allergy, RC581-607
Abstract

IntroductionThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. MethodsExtensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.ResultsResults revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. DiscussionThe key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

Published
2024
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10. Toward a common standard for data and specimen provenance in life sciences

Author

Rudolf Wittner, Petr Holub, Cecilia Mascia, Francesca Frexia, Heimo Müller, Markus Plass, Clare Allocca, Fay Betsou, Tony Burdett, Ibon Cancio, Adriane Chapman, Martin Chapman, Mélanie Courtot, Vasa Curcin, Johann Eder, Mark Elliot, Katrina Exter, Carole Goble, Martin Golebiewski, Bron Kisler, Andreas Kremer, Simone Leo, Sheng Lin‐Gibson, Anna Marsano, Marco Mattavelli, Josh Moore, Hiroki Nakae, Isabelle Perseil, Ayat Salman, James Sluka, Stian Soiland‐Reyes, Caterina Strambio‐De‐Castillia, Michael Sussman, Jason R. Swedlow, Kurt Zatloukal, and Jörg Geiger

Subjects
biotechnology, International Organization for Standardization, provenance information, standardization, Medicine (General), R5-920, Public aspects of medicine, RA1-1270
Abstract

Abstract Open and practical exchange, dissemination, and reuse of specimens and data have become a fundamental requirement for life sciences research. The quality of the data obtained and thus the findings and knowledge derived is thus significantly influenced by the quality of the samples, the experimental methods, and the data analysis. Therefore, a comprehensive and precise documentation of the pre‐analytical conditions, the analytical procedures, and the data processing are essential to be able to assess the validity of the research results. With the increasing importance of the exchange, reuse, and sharing of data and samples, procedures are required that enable cross‐organizational documentation, traceability, and non‐repudiation. At present, this information on the provenance of samples and data is mostly either sparse, incomplete, or incoherent. Since there is no uniform framework, this information is usually only provided within the organization and not interoperably. At the same time, the collection and sharing of biological and environmental specimens increasingly require definition and documentation of benefit sharing and compliance to regulatory requirements rather than consideration of pure scientific needs. In this publication, we present an ongoing standardization effort to provide trustworthy machine‐actionable documentation of the data lineage and specimens. We would like to invite experts from the biotechnology and biomedical fields to further contribute to the standard.

Published
2024
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18. Systems Biology in ELIXIR: modelling in the spotlight [version 1; peer review: 1 approved, 2 approved with reservations]

Author

Vitor Martins dos Santos, Mihail Anton, Barbara Szomolay, Marek Ostaszewski, Ilja Arts, Rui Benfeitas, Victoria Dominguez Del Angel, Polonca Ferk, Dirk Fey, Carole Goble, Martin Golebiewski, Kristina Gruden, Katharina F. Heil, Henning Hermjakob, Pascal Kahlem, Maria I. Klapa, Jasper Koehorst, Alexey Kolodkin, Martina Kutmon, Brane Leskošek, Sébastien Moretti, Wolfgang Müller, Marco Pagni, Tadeja Rezen, Miguel Rocha, Damjana Rozman, David Šafránek, Rahuman S. Malik Sheriff, Maria Suarez Diez, Kristel Van Steen, Hans V Westerhoff, Ulrike Wittig, Katherine Wolstencroft, Anze Zupanic, Chris T. Evelo, and John M. Hancock

Subjects
Opinion Article, Articles, Systems Biology, Systems Medicine, ELIXIR Communities, Biomolecular Models, Network Biology, FAIR, Biological data, Biotechnology
Abstract

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR’s future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

Published
2022
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19. COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms

Author

Marek Ostaszewski, Anna Niarakis, Alexander Mazein, Inna Kuperstein, Robert Phair, Aurelio Orta‐Resendiz, Vidisha Singh, Sara Sadat Aghamiri, Marcio Luis Acencio, Enrico Glaab, Andreas Ruepp, Gisela Fobo, Corinna Montrone, Barbara Brauner, Goar Frishman, Luis Cristóbal Monraz Gómez, Julia Somers, Matti Hoch, Shailendra Kumar Gupta, Julia Scheel, Hanna Borlinghaus, Tobias Czauderna, Falk Schreiber, Arnau Montagud, Miguel Ponce de Leon, Akira Funahashi, Yusuke Hiki, Noriko Hiroi, Takahiro G Yamada, Andreas Dräger, Alina Renz, Muhammad Naveez, Zsolt Bocskei, Francesco Messina, Daniela Börnigen, Liam Fergusson, Marta Conti, Marius Rameil, Vanessa Nakonecnij, Jakob Vanhoefer, Leonard Schmiester, Muying Wang, Emily E Ackerman, Jason E Shoemaker, Jeremy Zucker, Kristie Oxford, Jeremy Teuton, Ebru Kocakaya, Gökçe Yağmur Summak, Kristina Hanspers, Martina Kutmon, Susan Coort, Lars Eijssen, Friederike Ehrhart, Devasahayam Arokia Balaya Rex, Denise Slenter, Marvin Martens, Nhung Pham, Robin Haw, Bijay Jassal, Lisa Matthews, Marija Orlic‐Milacic, Andrea Senff-Ribeiro, Karen Rothfels, Veronica Shamovsky, Ralf Stephan, Cristoffer Sevilla, Thawfeek Varusai, Jean‐Marie Ravel, Rupsha Fraser, Vera Ortseifen, Silvia Marchesi, Piotr Gawron, Ewa Smula, Laurent Heirendt, Venkata Satagopam, Guanming Wu, Anders Riutta, Martin Golebiewski, Stuart Owen, Carole Goble, Xiaoming Hu, Rupert W Overall, Dieter Maier, Angela Bauch, Benjamin M Gyori, John A Bachman, Carlos Vega, Valentin Grouès, Miguel Vazquez, Pablo Porras, Luana Licata, Marta Iannuccelli, Francesca Sacco, Anastasia Nesterova, Anton Yuryev, Anita de Waard, Denes Turei, Augustin Luna, Ozgun Babur, Sylvain Soliman, Alberto Valdeolivas, Marina Esteban‐Medina, Maria Peña‐Chilet, Kinza Rian, Tomáš Helikar, Bhanwar Lal Puniya, Dezso Modos, Agatha Treveil, Marton Olbei, Bertrand De Meulder, Stephane Ballereau, Aurélien Dugourd, Aurélien Naldi, Vincent Noël, Laurence Calzone, Chris Sander, Emek Demir, Tamas Korcsmaros, Tom C Freeman, Franck Augé, Jacques S Beckmann, Jan Hasenauer, Olaf Wolkenhauer, Egon L Willighagen, Alexander R Pico, Chris T Evelo, Marc E Gillespie, Lincoln D Stein, Henning Hermjakob, Peter D'Eustachio, Julio Saez‐Rodriguez, Joaquin Dopazo, Alfonso Valencia, Hiroaki Kitano, Emmanuel Barillot, Charles Auffray, Rudi Balling, Reinhard Schneider, and the COVID‐19 Disease Map Community

Subjects
computable knowledge repository, large‐scale biocuration, omics data analysis, open access community effort, systems biomedicine, Biology (General), QH301-705.5, Medicine (General), R5-920
Abstract

Abstract We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS‐CoV‐2 infection, improving data interpretation and predicting key targets of intervention. Here, 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 (C19DMap) is a graphical, interactive representation of disease‐relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph‐based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS‐CoV‐2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID‐19 or similar pandemics in the long‐term perspective.

Published
2021
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20. Systems Biology in ELIXIR: modelling in the spotlight [version 1; peer review: 1 approved, 2 approved with reservations]

Author

Katharina F. Heil, Anze Zupanic, Chris T. Evelo, Carole Goble, Kristel Van Steen, Tadeja Rezen, Hans V Westerhoff, Rahuman S. Malik Sheriff, Maria Suarez Diez, Henning Hermjakob, Marco Pagni, Damjana Rozman, Barbara Szomolay, Miguel Rocha, Jasper Koehorst, Alexey Kolodkin, Victoria Dominguez Del Angel, David Šafránek, Dirk Fey, Ilja Arts, Rui Benfeitas, Vitor Martins dos Santos, Mihail Anton, Marek Ostaszewski, Ulrike Wittig, John M. Hancock, Brane Leskošek, Katherine Wolstencroft, Martin Golebiewski, Wolfgang Müller, Polonca Ferk, Kristina Gruden, Martina Kutmon, Maria I. Klapa, Sébastien Moretti, and Pascal Kahlem

Subjects
Systems Biology, Systems Medicine, ELIXIR Communities, Biomolecular Models, Network Biology, FAIR, eng, Medicine, Science
Abstract

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR’s future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

Published
2022
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21. SBML Level 3: an extensible format for the exchange and reuse of biological models

Author

Sarah M Keating, Dagmar Waltemath, Matthias König, Fengkai Zhang, Andreas Dräger, Claudine Chaouiya, Frank T Bergmann, Andrew Finney, Colin S Gillespie, Tomáš Helikar, Stefan Hoops, Rahuman S Malik‐Sheriff, Stuart L Moodie, Ion I Moraru, Chris J Myers, Aurélien Naldi, Brett G Olivier, Sven Sahle, James C Schaff, Lucian P Smith, Maciej J Swat, Denis Thieffry, Leandro Watanabe, Darren J Wilkinson, Michael L Blinov, Kimberly Begley, James R Faeder, Harold F Gómez, Thomas M Hamm, Yuichiro Inagaki, Wolfram Liebermeister, Allyson L Lister, Daniel Lucio, Eric Mjolsness, Carole J Proctor, Karthik Raman, Nicolas Rodriguez, Clifford A Shaffer, Bruce E Shapiro, Joerg Stelling, Neil Swainston, Naoki Tanimura, John Wagner, Martin Meier‐Schellersheim, Herbert M Sauro, Bernhard Palsson, Hamid Bolouri, Hiroaki Kitano, Akira Funahashi, Henning Hermjakob, John C Doyle, Michael Hucka, SBML Level 3 Community members, Richard R Adams, Nicholas A Allen, Bastian R Angermann, Marco Antoniotti, Gary D Bader, Jan Červený, Mélanie Courtot, Chris D Cox, Piero Dalle Pezze, Emek Demir, William S Denney, Harish Dharuri, Julien Dorier, Dirk Drasdo, Ali Ebrahim, Johannes Eichner, Johan Elf, Lukas Endler, Chris T Evelo, Christoph Flamm, Ronan MT Fleming, Martina Fröhlich, Mihai Glont, Emanuel Gonçalves, Martin Golebiewski, Hovakim Grabski, Alex Gutteridge, Damon Hachmeister, Leonard A Harris, Benjamin D Heavner, Ron Henkel, William S Hlavacek, Bin Hu, Daniel R Hyduke, Hidde de Jong, Nick Juty, Peter D Karp, Jonathan R Karr, Douglas B Kell, Roland Keller, Ilya Kiselev, Steffen Klamt, Edda Klipp, Christian Knüpfer, Fedor Kolpakov, Falko Krause, Martina Kutmon, Camille Laibe, Conor Lawless, Lu Li, Leslie M Loew, Rainer Machne, Yukiko Matsuoka, Pedro Mendes, Huaiyu Mi, Florian Mittag, Pedro T Monteiro, Kedar Nath Natarajan, Poul MF Nielsen, Tramy Nguyen, Alida Palmisano, Jean‐Baptiste Pettit, Thomas Pfau, Robert D Phair, Tomas Radivoyevitch, Johann M Rohwer, Oliver A Ruebenacker, Julio Saez‐Rodriguez, Martin Scharm, Henning Schmidt, Falk Schreiber, Michael Schubert, Roman Schulte, Stuart C Sealfon, Kieran Smallbone, Sylvain Soliman, Melanie I Stefan, Devin P Sullivan, Koichi Takahashi, Bas Teusink, David Tolnay, Ibrahim Vazirabad, Axel von Kamp, Ulrike Wittig, Clemens Wrzodek, Finja Wrzodek, Ioannis Xenarios, Anna Zhukova, and Jeremy Zucker

Subjects
computational modeling, file format, interoperability, reproducibility, systems biology, Biology (General), QH301-705.5, Medicine (General), R5-920
Abstract

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.

Published
2020
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22. NFDI4Health – Nationale Forschungsdateninfrastruktur für personenbezogene Gesundheitsdaten

Author

Juliane Fluck, Birte Lindstädt, Wolfgang Ahrens, Oya Beyan, Benedikt Buchner, Johannes Darms, Ralf Depping, Jens Dierkes, Hubertus Neuhausen, Wolfgang Müller, Hajo Zeeb, Martin Golebiewski, Markus Löffler, Matthias Löbe, Frank Meineke, Sebastian Klammt, Holger Fröhlich, Horst Hahn, Matthias Schulze, Tobias Pischon, Ute Nöthlings, Ulrich Sax, Harald Kusch, Linus Grabenhenrich, Carsten Oliver Schmidt, Dagmar Waltemath, Sebastian Semler, Juliane Gehrke, Toralf Kirsten, Fabian Praßer, Sylvia Thun, Lothar Wieler, and Iris Pigeot

Subjects
Forschungsdatenmanagement, Gesundheitsdaten, FAIR, Technology
Abstract

doch erfüllen Studienprotokolle, eingesetzte Erhebungsinstrumente und erhobene Daten die Anforderungen der FAIR-Prinzipien nicht in ausreichendem Maße. NFDI4Health wird daher eine Struktur schaffen, die eine zentrale Suche nach existierenden, dezentral verwalteten Datenkörpern und zugehörigen Dokumenten sowie einen FAIRen Zugang zu diesen erleichtert. Dazu werden die Auffindbarkeit und der Zugang zu strukturierten Gesundheitsdaten aus Registern, administrativen Gesundheitsdatenbanken, klinischen und epidemiologischen sowie Public Health-Studien verbessert und die Qualität und Harmonisierung der zugrundeliegenden Daten optimiert. Eine weitere Herausforderung entsteht durch die Verwendung personenbezogener Gesundheitsdaten. Diese sind hoch sensibel, so dass ihre Nutzung restriktive Datenschutzbestimmungen und informierte Einwilligungserklärungen der Studienteilnehmenden erfordert, was jedoch ihre Wiederverwendbarkeit einschränkt. NFDI4Health zielt daher darauf ab, den Austausch und die Verknüpfung von personenbezogenen Gesundheitsdaten sowie verteilte Datenanalysen unter Einhaltung datenschutzrechtlicher und ethischer Bestimmungen zu erleichtern. Um dies möglichst effizient zu erreichen, wird NFDI4Health die Entwicklung neuer, maschinenprozessierbarer Zustimmungsmöglichkeiten sowie innovativer Datenzugriffsservices auf Grundlage der FAIR-Prinzipien vorantreiben und die Interoperabilität von IT-Lösungen für Metadatenrepositorien stärken. Komplementiert wird dies durch die Entwicklung entsprechender Angebote für Training und Ausbildung, um der Herausforderung der Umsetzung der Lösungen in den Universitäten und Forschungseinrichtungen zu begegnen. Schließlich wird durch die gemeinsame Arbeit in der NFDI4Health die Kooperation zwischen klinischer und epidemiologischer/Public Health-Forschung gestärkt.

Published
2021
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24. Erratum To: COVID‐19 Disease Map, a computational knowledge repository of virus‐host interaction mechanisms

Author

Marek Ostaszewski, Anna Niarakis, Alexander Mazein, Inna Kuperstein, Robert Phair, Aurelio Orta‐Resendiz, Vidisha Singh, Sara Sadat Aghamiri, Marcio Luis Acencio, Enrico Glaab, Andreas Ruepp, Gisela Fobo, Corinna Montrone, Barbara Brauner, Goar Frishman, Luis Cristóbal Monraz Gómez, Julia Somers, Matti Hoch, Shailendra Kumar Gupta, Julia Scheel, Hanna Borlinghaus, Tobias Czauderna, Falk Schreiber, Arnau Montagud, Miguel Ponce de Leon, Akira Funahashi, Yusuke Hiki, Noriko Hiroi, Takahiro G Yamada, Andreas Dräger, Alina Renz, Muhammad Naveez, Zsolt Bocskei, Francesco Messina, Daniela Börnigen, Liam Fergusson, Marta Conti, Marius Rameil, Vanessa Nakonecnij, Jakob Vanhoefer, Leonard Schmiester, Muying Wang, Emily E Ackerman, Jason E Shoemaker, Jeremy Zucker, Kristie Oxford, Jeremy Teuton, Ebru Kocakaya, Gökçe Yağmur Summak, Kristina Hanspers, Martina Kutmon, Susan Coort, Lars Eijssen, Friederike Ehrhart, D A B Rex, Denise Slenter, Marvin Martens, Nhung Pham, Robin Haw, Bijay Jassal, Lisa Matthews, Marija Orlic‐Milacic, Andrea Senff‐Ribeiro, Karen Rothfels, Veronica Shamovsky, Ralf Stephan, Cristoffer Sevilla, Thawfeek Varusai, Jean‐Marie Ravel, Rupsha Fraser, Vera Ortseifen, Silvia Marchesi, Piotr Gawron, Ewa Smula, Laurent Heirendt, Venkata Satagopam, Guanming Wu, Anders Riutta, Martin Golebiewski, Stuart Owen, Carole Goble, Xiaoming Hu, Rupert W Overall, Dieter Maier, Angela Bauch, Benjamin M Gyori, John A Bachman, Carlos Vega, Valentin Grouès, Miguel Vazquez, Pablo Porras, Luana Licata, Marta Iannuccelli, Francesca Sacco, Anastasia Nesterova, Anton Yuryev, Anita de Waard, Denes Turei, Augustin Luna, Ozgun Babur, Sylvain Soliman, Alberto Valdeolivas, Marina Esteban‐Medina, Maria Peña‐Chilet, Kinza Rian, Tomáš Helikar, Bhanwar Lal Puniya, Dezso Modos, Agatha Treveil, Marton Olbei, Bertrand De Meulder, Stephane Ballereau, Aurélien Dugourd, Aurélien Naldi, Vincent Noël, Laurence Calzone, Chris Sander, Emek Demir, Tamas Korcsmaros, Tom C Freeman, Franck Augé, Jacques S Beckmann, Jan Hasenauer, Olaf Wolkenhauer, Egon L Willighagen, Alexander R Pico, Chris T Evelo, Marc E Gillespie, Lincoln D Stein, Henning Hermjakob, Peter D'Eustachio, Julio Saez‐Rodriguez, Joaquin Dopazo, Alfonso Valencia, Hiroaki Kitano, Emmanuel Barillot, Charles Auffray, Rudi Balling, Reinhard Schneider, and the COVID‐19 Disease Map Community

Subjects
Biology (General), QH301-705.5, Medicine (General), R5-920
Abstract

Graphical Abstract

Published
2021
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25. Harmonizing semantic annotations for computational models in biology.

Author

Maxwell Lewis Neal, Matthias König 0003, David P. Nickerson, Goksel Misirli, Reza Kalbasi, Andreas Dräger, Koray Atalag, Vijayalakshmi Chelliah, Michael T. Cooling, Daniel L. Cook, Sharon M. Crook, Miguel de Alba, Samuel H. Friedman, Alan Garny, John H. Gennari, Padraig Gleeson, Martin Golebiewski, Michael Hucka, Nick S. Juty, Chris J. Myers, Brett G. Olivier, Herbert M. Sauro, Martin Scharm, Jacky L. Snoep, Vasundra Touré, Anil Wipat, Olaf Wolkenhauer, and Dagmar Waltemath

Published
2019
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26. A brief history of COMBINE.

Author

Chris J. Myers, Gary D. Bader, Padraig Gleeson, Martin Golebiewski, Michael Hucka, Nicolas Le Novère, David P. Nickerson, Falk Schreiber, and Dagmar Waltemath

Published
2017
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29. The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE).

Author

Dagmar Waltemath, Martin Golebiewski, Michael L. Blinov, Padraig Gleeson, Henning Hermjakob, Michael Hucka, Esther Thea Inau, Sarah M. Keating, Matthias König 0003, Olga Krebs, Rahuman S. Malik-Sheriff, David P. Nickerson, Ernst Oberortner, Herbert M. Sauro, Falk Schreiber, Lucian P. Smith, Melanie I. Stefan, Ulrike Wittig, and Chris J. Myers

Published
2020
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33. Specifications of standards in systems and synthetic biology: status and developments in 2022 and the COMBINE meeting 2022

Author

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

Subjects
General Medicine
Abstract

This special issue of the Journal of Integrative Bioinformatics contains updated specifications of COMBINE standards in systems and synthetic biology. The 2022 special issue presents three updates to the standards: CellML 2.0.1, SBML Level 3 Package: Spatial Processes, Version 1, Release 1, and Synthetic Biology Open Language (SBOL) Version 3.1.0. This document can also be used to identify the latest specifications for all COMBINE standards. In addition, this editorial provides a brief overview of the COMBINE 2022 meeting in Berlin.

Published
2023

34. The evolution of standards and data management practices in systems biology

Author

Natalie J Stanford, Katherine Wolstencroft, Martin Golebiewski, Renate Kania, Nick Juty, Christopher Tomlinson, Stuart Owen, Sarah Butcher, Henning Hermjakob, Nicolas Le Novère, Wolfgang Mueller, Jacky Snoep, and Carole Goble

Subjects
Biology (General), QH301-705.5, Medicine (General), R5-920
Abstract

Graphical Abstract A recent community survey conducted by Infrastructure for Systems Biology Europe (ISBE) informs requirements for developing an efficient infrastructure for systems biology standards, data and model management.

Published
2015
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42. Controlled vocabularies and semantics in systems biology

Author

Mélanie Courtot, Nick Juty, Christian Knüpfer, Dagmar Waltemath, Anna Zhukova, Andreas Dräger, Michel Dumontier, Andrew Finney, Martin Golebiewski, Janna Hastings, Stefan Hoops, Sarah Keating, Douglas B Kell, Samuel Kerrien, James Lawson, Allyson Lister, James Lu, Rainer Machne, Pedro Mendes, Matthew Pocock, Nicolas Rodriguez, Alice Villeger, Darren J Wilkinson, Sarala Wimalaratne, Camille Laibe, Michael Hucka, and Nicolas Le Novère

Subjects
dynamics, kinetics, model, ontology, simulation, Biology (General), QH301-705.5, Medicine (General), R5-920
Abstract

Abstract The use of computational modeling to describe and analyze biological systems is at the heart of systems biology. Model structures, simulation descriptions and numerical results can be encoded in structured formats, but there is an increasing need to provide an additional semantic layer. Semantic information adds meaning to components of structured descriptions to help identify and interpret them unambiguously. Ontologies are one of the tools frequently used for this purpose. We describe here three ontologies created specifically to address the needs of the systems biology community. The Systems Biology Ontology (SBO) provides semantic information about the model components. The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships. The Terminology for the Description of Dynamics (TEDDY) categorizes dynamical features of the simulation results and general systems behavior. The provision of semantic information extends a model's longevity and facilitates its reuse. It provides useful insight into the biology of modeled processes, and may be used to make informed decisions on subsequent simulation experiments.

Published
2011
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44. Correction to: 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, and Eric Bonnet

Subjects
Genetics, QH426-470
Abstract

Following publication of the original article [1], the author requested this addition to the ‘Acknowledgements’ section of the article: “DW is funded by the German Federal Ministry of Education and Research (e: Bio program SEMS, FKZ 031 6194)”.

Published
2018
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46. Ten Simple Rules for FAIR Sharing of Experimental and Clinical Data with the Modeling Community

Author

Jan Grzegorzewski, Martin Golebiewski, Brett G. Olivier, Sarah M. Keating, David P. Nickerson, Henning Hermjakob, Matthias König, Dagmar Waltemath, Michael Hucka, Rahuman S. Malik Sheriff, and Falk Schreiber

Subjects
life_sciences_other, Data sharing, Computer science, Simple (abstract algebra), Data mining, computer.software_genre, computer
Abstract

Science continues to become more interdisciplinary and to involve increasingly complex data sets. Many projects in the biomedical and health-related sciences follow or aim to follow the principles of FAIR data sharing, which has been demonstrated to foster collaboration, to lead to better research outcomes, and to help ensure reproducibility of results. Data generated in the course of biomedical and health research present specific challenges for FAIR sharing in the sense that they are heterogeneous and highly sensitive to context and the needs of protection and privacy. Data sharing must respect these features without impeding timely dissemination of results, so that they can contribute to time-critical advances in medical therapy and treatment. Modeling and simulation of biomedical processes have become established tools, and a global community has been developing algorithms, methodologies, and standards for applying biomedical simulation models in clinical research. However, it can be difficult for clinician scientists to follow the specific rules and recommendations for FAIR data sharing within this domain. We seek to clarify the standard workflow for sharing experimental and clinical data with the simulation modeling community. By following these recommendations, data sharing will be improved, collaborations will become more effective, and the FAIR publication and subsequent reuse of data will become possible at the level of quality necessary to support biomedical and health-related sciences.

Published
2021
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47. Improving the FAIRness of Health Studies in Germany: The German Central Health Study Hub COVID-19

Author

Johannes, Darms, Jörg, Henke, Xioaming, Hu, Carsten Oliver, Schmidt, Martin, Golebiewski, and Juliane, Fluck

Subjects
Metadata, SARS-CoV-2, Germany, Surveys and Questionnaires, COVID-19, Humans
Abstract

The German Central Health Study Hub COVID-19 is an online service that offers bundled access to COVID-19 related studies conducted in Germany. It combines metadata and other information of epidemiologic, public health and clinical studies into a single data repository for FAIR data access. In addition to study characteristics the system also allows easy access to study documents, as well as instruments for data collection. Study metadata and survey instruments are decomposed into individual data items and semantically enriched to ease the findability. Data from existing clinical trial registries (DRKS, clinicaltrails.gov and WHO ICTRP) are merged with epidemiological and public health studies manually collected and entered. More than 850 studies are listed as of September 2021.

Published
2021

48. Improving the FAIRness of Health Studies in Germany: The German Central Health Study Hub COVID-19

Author

Carsten Oliver Schmidt, Jörg Henke, Johannes Darms, Martin Golebiewski, Xioaming Hu, and Juliane Fluck

Subjects
medicine.medical_specialty, Service (systems architecture), Data collection, Public health, Findability, Information repository, language.human_language, German, World Wide Web, Metadata, Data access, language, medicine, Business
Abstract

The German Central Health Study Hub COVID-19 is an online service that offers bundled access to COVID-19 related studies conducted in Germany. It combines metadata and other information of epidemiologic, public health and clinical studies into a single data repository for FAIR data access. In addition to study characteristics the system also allows easy access to study documents, as well as instruments for data collection. Study metadata and survey instruments are decomposed into individual data items and semantically enriched to ease the findability. Data from existing clinical trial registries (DRKS, clinicaltrails.gov and WHO ICTRP) are merged with epidemiological and public health studies manually collected and entered. More than 850 studies are listed as of September 2021.

Published
2021
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49. National Research Data Infrastructure for Personal Health Data (NFDI4Health): Version 2.0

Author

Juliane Fluck, Wolfgang Ahrens, Iris Pigeot, Hajo Zeeb, Fabian Prasser, Sylvia Thun, Matthias Schulze, Oya Beyan, Holger Fröhlich, Horst Hahn, Martin Golebiewski, Wolfgang Müller, Sebastian Klammt, Tobias Pischon, Linus Grabenhenrich, Lothar H. Wieler, Juliane Gehrke, Sebastian C. Semler, Ute Nöthlings, Benedikt Buchner, Ralf Depping, Jens Dierkes, Hubertus Neuhausen, Oana Brosteanu, Matthias Löbe, Markus Löffler, Frank Meineke, Toralf Kirsten, Harald Kusch, Ulrich Sax, Carsten Oliver Schmidt, Dagmar Waltemath, Johannes Darms, and Birte Lindstädt

Subjects
NFDI, Research data, FAIR
Abstract

Germany has accumulated a wealth of health-related personal data from well-designed cohort studies and health surveillance systems (healthy individuals) as well as clinical trials (patients) that are characterised by a deep phenotyping of study subjects with questionnaires, medical examinations and molecular/genetic profiling. Their longitudinal nature and high quality make these data a valuable research resource for the development of preventive and therapeutic measures on the individual and the population level. NFDI4Health represents an interdisciplinary research community by integrating major German institutions experienced as data holders, data analysts and methodology developers. It builds on established structures, competences and know-how and expects a rapidly growing support and participation of the research community. NFDI4Health aims to create the most comprehensive inventory of German epidemiological, public health and clinical trial data to date. NFDI4Health will build a centralised data catalogue with elaborate search functionalities, sophisticated data access management, and a data analysis toolbox, while respecting stringent requirements for privacy concerning personal health data. Standardisation services will ensure a high degree of interoperability. Use cases covering prototypical study types and areas of research will show the feasibility of a harmonised implementation of all infrastructures, tools and services in accordance with our user communities. &nbsp

Published
2021

50. 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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