Supplementary Information on model parameters

This file contains description of all model parameters and corresponding references

Creators: Alexey Kolodkin, Hans V. Westerhoff

Submitter: Alexey Kolodkin

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Comprehensive model for COVID-19 caused by SARS-CoV-2

Model building:

The module was built using modular bottom-up approach where every module describes a certain process and then, when modules are connected together like domino tiles, we can reconstruct the emergent behavior of the whole system.

This is a blueprint model and might be used for various country/data. If one wans to use it for a particular country/data, we can recommend following steps:

  1. Adjust total population by changing initial condition of A-Initial_population_innocent_non-tested ...

Creators: Alexey Kolodkin, Hans V. Westerhoff

Submitter: Alexey Kolodkin

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Gene Network Inference from Single-Cell Omics Data and Domain Knowledge for Constructing COVID-19-Specific ICAM1-Associated Pathways

Abstract (Expand)

Abstract ICAM-1 is critical for interactions between cells. Previous studies have suggested that ICAM-1 triggers cell-to-cell transmission of HIV-1 or HTLV-1. SARS-CoV-2 shares several features with … several features with these viruses in interactions between cells, and SARS-CoV-2 cell-to-cell transmission is associated with COVID-19 severity. However, ICAM-1 and its associated pathways are not identified as essential factors in interactions between cells in COVID-19. For example, the current COVID-19 Disease Map has no entry for those pathways. Therefore, discovering unknown ICAM-1 pathways will be indispensable for clarifying the mechanism of COVID-19. This study builds ICAM1-associated pathways by gene network inference from single-cell omics data and multiple knowledge bases. First, data analyses extracted coexpressed genes with significant differences in expression levels with spurious correlations removed. Second, knowledge bases validate models. Finally, mapping the models onto existing pathways identifies new ICAM1-associated pathways. These pathways indicate that (1) upstream pathways include proteins in noncanonical NF-kappaB pathway and that (2) downstream pathways contain integrins and cytoskeleton or motor proteins for cell transformation. In this way, data-driven and knowledge-based approaches are integrated into gene network inference for ICAM1-associated pathway construction. The results can contribute to repairing and completing the COVID-19 Disease Map, thereby improving our understanding of the mechanisms of COVID-19.

Authors: Mitsuhiro Odaka, Morgan Magnin, Katsumi Inoue

Date Published: 11th Feb 2022

Publication Type: Journal

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

Abstract (Expand)

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.

Authors: M. Ostaszewski, A. Niarakis, A. Mazein, I. Kuperstein, R. Phair, A. Orta-Resendiz, V. Singh, S. S. Aghamiri, M. L. Acencio, E. Glaab, A. Ruepp, G. Fobo, C. Montrone, B. Brauner, G. Frishman, L. C. Monraz Gomez, J. Somers, M. Hoch, S. Kumar Gupta, J. Scheel, H. Borlinghaus, T. Czauderna, F. Schreiber, A. Montagud, M. Ponce de Leon, A. Funahashi, Y. Hiki, N. Hiroi, T. G. Yamada, A. Drager, A. Renz, M. Naveez, Z. Bocskei, F. Messina, D. Bornigen, L. Fergusson, M. Conti, M. Rameil, V. Nakonecnij, J. Vanhoefer, L. Schmiester, M. Wang, E. E. Ackerman, J. E. Shoemaker, J. Zucker, K. Oxford, J. Teuton, E. Kocakaya, G. Y. Summak, K. Hanspers, M. Kutmon, S. Coort, L. Eijssen, F. Ehrhart, D. A. B. Rex, D. Slenter, M. Martens, N. Pham, R. Haw, B. Jassal, L. Matthews, M. Orlic-Milacic, A. Senff Ribeiro, K. Rothfels, V. Shamovsky, R. Stephan, C. Sevilla, T. Varusai, J. M. Ravel, R. Fraser, V. Ortseifen, S. Marchesi, P. Gawron, E. Smula, L. Heirendt, V. Satagopam, G. Wu, A. Riutta, M. Golebiewski, S. Owen, C. Goble, X. Hu, R. W. Overall, D. Maier, A. Bauch, B. M. Gyori, J. A. Bachman, C. Vega, V. Groues, M. Vazquez, P. Porras, L. Licata, M. Iannuccelli, F. Sacco, A. Nesterova, A. Yuryev, A. de Waard, D. Turei, A. Luna, O. Babur, S. Soliman, A. Valdeolivas, M. Esteban-Medina, M. Pena-Chilet, K. Rian, T. Helikar, B. L. Puniya, D. Modos, A. Treveil, M. Olbei, B. De Meulder, S. Ballereau, A. Dugourd, A. Naldi, V. Noel, L. Calzone, C. Sander, E. Demir, T. Korcsmaros, T. C. Freeman, F. Auge, J. S. Beckmann, J. Hasenauer, O. Wolkenhauer, E. L. Wilighagen, A. R. Pico, C. T. Evelo, M. E. Gillespie, L. D. Stein, H. Hermjakob, P. D'Eustachio, J. Saez-Rodriguez, J. Dopazo, A. Valencia, H. Kitano, E. Barillot, C. Auffray, R. Balling, R. Schneider

Date Published: 19th Oct 2021

Publication Type: Journal

The German Corona Consensus Dataset (GECCO): A standardized dataset for COVID-19 research

Abstract (Expand)

Background: The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing segmentation of … information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the "German Corona Consensus Dataset" (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data. Methods: Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats. Results: A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, anamnesis, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined. Conclusion: GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.

Authors: Julian Sass, Alexander Bartschke, Moritz Lehne, Andrea Essenwanger, Eugenia Rinaldi, Stefanie Rudolph, Kai Uwe Heitmann, Joerg Janne Vehreschild, Christof von Kalle, Sylvia Thun

Date Published: 29th Jul 2020

Publication Type: Journal

RDA response to the global COVID 19 pandemic (press release)

The Research Data Alliance (RDA) is a volunteer community of over 10,500 professionals from 145 countries across the globe. In less than two months, the community responded to an urgent call for action and defined much needed, comprehensive recommendations and guidelines for data sharing under the present COVID-19 circumstances.

Creators: Martin Golebiewski, see full list of members of the RDA COVID-19 working group: https://www.rd-alliance.org/node/68704/members

Submitter: Martin Golebiewski

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RDA COVID-19 Recommendations and Guidelines - 5th Release

This is the fifth and final draft of the Recommendations and Guidelines from the RDA COVID-19 working group, and is open for public comment until 8th of June 2020. Following the open period, feedback will be considered and then the WG will seek endorsement of the document from the RDA governance bodies prior to final publication.

Creators: Martin Golebiewski, see full list of members of the RDA COVID-19 working group: https://www.rd-alliance.org/node/68704/members

Submitter: Martin Golebiewski

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RDA COVID-19 Recommendations and Guidelines - 4th Release

Draft guidelines and recommendations; fourth release, 15 May 2020, version for public review

Creator: see full list of members of the RDA COVID-19 working group: https://www.rd-alliance.org/node/68704/members

Submitter: Martin Golebiewski

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