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

Niarakis, A.; Ostaszewski, M.; Mazein, A.; Kuperstein, I.; Gillespie, M. E.; Kutmon, M.; Funahashi, A.; Acencio, M. L.; Hemedan, A.; Aichem, M.; Klein, K.; Czauderna, T.; Burtscher, F.; Yamada, T. G.; Hiki, Y.; Hiroi, N. F.; Hu, F.; Pham, N.; Ehrhart, F.; Willighagen, E. L.; Valdeolivas, A.; Dugourd, A.; Messina, F.; Esteban-Medina, M.; Pena-Chilet, M.; Rian, K.; Soliman, S.; Aghamiri, S. S.; Puniya, B. L.; Naldi, A.; Helikar, T.; Singh, V.; Farinas Fernandez, M.; Bermudez, V.; Tsirvouli, E.; Montagud, A.; Noel, V.; Ponce de Leon, M.; Maier, D.; Bauch, A.; Gyori, B. M.; Bachman, J. A.; Luna, A

doi:10.1101/2022.12.17.520865

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bioRxiv · 10.1101/2022.12.17.520865

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

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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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Niarakis, A., Ostaszewski, M., Mazein, A., Kuperstein, I., Gillespie, M. E., Kutmon, M., Funahashi, A., Acencio, M. L., Hemedan, A., Aichem, M., Klein, K., Czauderna, T., Burtscher, F., Yamada, T. G., Hiki, Y., Hiroi, N. F., Hu, F., Pham, N., Ehrhart, F., Willighagen, E. L., Valdeolivas, A., Dugourd, A., Messina, F., Esteban-Medina, M., Pena-Chilet, M., Rian, K., Soliman, S., Aghamiri, S. S., Puniya, B. L., Naldi, A., Helikar, T., Singh, V., Farinas Fernandez, M., Bermudez, V., Tsirvouli, E., Montagud, A., Noel, V., Ponce de Leon, M., Maier, D., Bauch, A., Gyori, B. M., Bachman, J. A., Luna, A. 2022-12-19. A versatile and interoperable computational framework for the analysis and modeling of COVID-19 disease mechanisms. https://doi.org/10.1101/2022.12.17.520865

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