A versatile and interoperable computational framework for the analysis and modeling of COVID-19 disease mechanisms

Anna Niarakis(Université Paris-Saclay), Marek Ostaszewski(University of Luxembourg), Alexander Mazein(University of Luxembourg), Inna Kuperstein(Inserm), Martina Kutmon(Maastricht University), Marc Gillespie(Ontario Institute for Cancer Research), Akira Funahashi(Keio University), Márcio Luís Acencio(University of Luxembourg), Ahmed Abdelmonem Hemedan(University of Luxembourg), Michael Aichem(University of Konstanz), Karsten Klein(University of Konstanz), Tobias Czauderna(Hochschule Mittweida), Felicia Burtscher(University of Luxembourg), Takahiro Yamada(Keio University), Yusuke Hiki(Keio University), Noriko Hiroi(Kanagawa Institute of Technology), Finterly Hu(Maastricht University), Nhung Pham(Maastricht University), Friederike Ehrhart(Maastricht University), Egon Willighagen(Maastricht University), Alberto Valdeolivas(Heidelberg University), Aurélien Dugourd(Heidelberg University), Francesco Messina(Istituto Nazionale per le Malattie Infettive Lazzaro Spallanzani), Marina Esteban‐Medina(Fundación Progreso y Salud), María Peña-Chilet(Fundación Progreso y Salud), Kinza Rian(Fundación Progreso y Salud), Sylvain Soliman(Centre Inria de Saclay), Sara Sadat Aghamiri(University of Nebraska–Lincoln), Bhanwar Lal Puniya(University of Nebraska–Lincoln), Aurélien Naldi(Centre Inria de Saclay), Tomáš Helikar(University of Nebraska–Lincoln), Vidisha Singh(Université Paris-Saclay), Marco Fariñas Fernández(Norwegian University of Science and Technology), Viviam Bermudez(Norwegian University of Science and Technology), Eirini Tsirvouli(Norwegian University of Science and Technology), Arnau Montagud(Barcelona Supercomputing Center), Vincent Noël(Inserm), Miguel Ponce-de-León(Barcelona Supercomputing Center), Dieter Maier(Biomax Informatics (Germany)), Angela Bauch(Biomax Informatics (Germany)), Benjamin M. Gyori(Harvard University), John A. Bachman(Harvard University), Augustin Luna(Harvard University), Janet Piñero(Universitat Pompeu Fabra), Laura I. Furlong(Universitat Pompeu Fabra), Irina Balaur(University of Luxembourg), Adrien Rougny(National Institute of Advanced Industrial Science and Technology), Yohan Jarosz(University of Luxembourg), Rupert W. Overall(Humboldt-Universität zu Berlin), Robert D. Phair, Livia Perfetto(Sapienza University of Rome), Lisa Matthews(NYU Langone Health), Rex Devasahayam Arokia Balaya(Yenepoya University), M Orlic-Milacic(Ontario Institute for Cancer Research), Luis Cristóbal Monraz Gómez(Inserm), Bertrand De Meulder(European Institute for Systems Biology and Medicine), Jean‐Marie Ravel(Inserm), Bijay Jassal(Ontario Institute for Cancer Research), Venkata Satagopam(Goethe University Frankfurt), Guanming Wu(Oregon Health & Science University), Martin Golebiewski(Heidelberg Institute for Theoretical Studies), Piotr Gawron(University of Luxembourg), Laurence Calzone(Inserm), J. Beckmann(University of Lausanne), Chris T. Evelo(Maastricht University), Peter D’Eustachio(NYU Langone Health), Falk Schreiber(University of Konstanz), Julio Sáez-Rodríguez(Heidelberg University), Joaquı́n Dopazo(Fundación Progreso y Salud), Martin Kuiper(Norwegian University of Science and Technology), Alfonso Valencia(Biomax Informatics (Germany)), Olaf Wolkenhauer(Leibniz-Institute for Food Systems Biology at the Technical University of Munich), Hiroaki Kitano(Systems Biology Institute), Emmanuel Barillot(Inserm), Charles Auffray(European Institute for Systems Biology and Medicine), Rudi Balling(University of Luxembourg), Reinhard Schneider(University of Luxembourg), the COVID-19 Disease Map Community
bioRxiv (Cold Spring Harbor Laboratory)
December 19, 2022
Cited by 1Open Access
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Abstract

Abstract The 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. Community-driven and highly interdisciplinary, the project is collaborative and supports community standards, open access, and the FAIR data principles. The coordination of community work allowed for an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework links key molecules highlighted from broad omics data analysis and computational modeling to dysregulated pathways in a cell-, tissue- or patient-specific manner. We also employ text mining and AI-assisted analysis to identify potential drugs and drug targets and use topological analysis to reveal interesting structural features of the map. The proposed framework is versatile and expandable, offering a significant upgrade in the arsenal used to understand virus-host interactions and other complex pathologies.


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