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1. MetaLo: metabolic analysis of Logical models extracted from molecular interaction maps

Author

Aghakhani Sahar, Niarakis Anna, and Soliman Sylvain

Subjects
boolean model, computational biology, hybrid modeling, metabolism, molecular interaction map, Biotechnology, TP248.13-248.65
Abstract

Molecular interaction maps (MIMs) are static graphical representations depicting complex biochemical networks that can be formalized using one of the Systems Biology Graphical Notation languages. Regardless of their extensive coverage of various biological processes, they are limited in terms of dynamic insights. However, MIMs can serve as templates for developing dynamic computational models. We present MetaLo, an open-source Python package that enables the coupling of Boolean models inferred from process description MIMs with generic core metabolic networks. MetaLo provides a framework to study the impact of signaling cascades, gene regulation processes, and metabolic flux distribution of central energy production pathways. MetaLo computes the Boolean model’s asynchronous asymptotic behavior, through the identification of trap-spaces, and extracts metabolic constraints to contextualize the generic metabolic network. MetaLo is able to handle large-scale Boolean models and genome-scale metabolic models without requiring kinetic information or manual tuning. The framework behind MetaLo enables in depth analysis of the regulatory model, and may allow tackling a lack of omics data in poorly addressed biological fields to contextualize generic metabolic networks along with improper automatic reconstructions of cell- and/or disease-specific metabolic networks. MetaLo is available at https://pypi.org/project/metalo/ under the terms of the GNU General Public License v3.

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

Author

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

Abstract

Introduction: 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. Methods: Extensive 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. Results: Results 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. Discussion: The 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., Peer Reviewed

Published
2024

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

Author

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

Published
2021
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9. COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms

Author

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

Published
2021
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11. Author Correction: COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms

Author

Ostaszewski, Marek, Mazein, Alexander, Gillespie, Marc E., Kuperstein, Inna, Niarakis, Anna, Hermjakob, Henning, Pico, Alexander R., Willighagen, Egon L., Evelo, Chris T., Hasenauer, Jan, Schreiber, Falk, Dräger, Andreas, Demir, Emek, Wolkenhauer, Olaf, Furlong, Laura I., Barillot, Emmanuel, Dopazo, Joaquin, Orta-Resendiz, Aurelio, Messina, Francesco, Valencia, Alfonso, Funahashi, Akira, Kitano, Hiroaki, Auffray, Charles, Balling, Rudi, and Schneider, Reinhard

Published
2020
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12. COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms

Author

Ostaszewski, Marek, Mazein, Alexander, Gillespie, Marc E., Kuperstein, Inna, Niarakis, Anna, Hermjakob, Henning, Pico, Alexander R., Willighagen, Egon L., Evelo, Chris T., Hasenauer, Jan, Schreiber, Falk, Dräger, Andreas, Demir, Emek, Wolkenhauer, Olaf, Furlong, Laura I., Barillot, Emmanuel, Dopazo, Joaquin, Orta-Resendiz, Aurelio, Messina, Francesco, Valencia, Alfonso, Funahashi, Akira, Kitano, Hiroaki, Auffray, Charles, Balling, Rudi, and Schneider, Reinhard

Published
2020
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13. A guide for developing comprehensive systems biology maps of disease mechanisms: planning, construction and maintenance

Author

Mazein, Alexander, primary, Acencio, Marcio Luis, additional, Balaur, Irina, additional, Rougny, Adrien, additional, Welter, Danielle, additional, Niarakis, Anna, additional, Ramirez Ardila, Diana, additional, Dogrusoz, Ugur, additional, Gawron, Piotr, additional, Satagopam, Venkata, additional, Gu, Wei, additional, Kremer, Andreas, additional, Schneider, Reinhard, additional, and Ostaszewski, Marek, additional

Published
2023
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16. Drug-Target identification in COVID-19 disease mechanisms using computational systems biology approaches

Author

Barcelona Supercomputing Center, Niarakis, Anna, Ostaszewski, Marek, Mazein, Alexander, Kuperstein, Inna, Kutmon, Martina, Montagud, Arnau, Ponce De Leon, Miguel, Valencia, Alfonso, Barcelona Supercomputing Center, Niarakis, Anna, Ostaszewski, Marek, Mazein, Alexander, Kuperstein, Inna, Kutmon, Martina, Montagud, Arnau, Ponce De Leon, Miguel, and Valencia, Alfonso

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. Extensive 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. Results 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. The 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., The author(s) declare financial support was received for the research, authorship, and/or publication of this article. AN acknowledges support from SANOFI-AVENTIS R&D via the CIFRE contract, n° 2020/0766. MK, FH, NP, FE, and CE acknowledge the support of the ZonMw COVID-19 programme (Grant No.10430012010015). JD Spanish Ministry of Science and Innovation (Grant no. PID2020-117979RB-I00) and Instituto de Salud Carlos III (Grant no. IMP/00019). MAi, KK, FS: Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -Project-ID 251654672 - TRR 161 and under Germany’s Excellence Strategy - EXC 2117 - 422037984. FM: “5 per 1000–2021” grant of the Italian Ministry of Health (Grant No. 5M-2021-23683787) and European Commission with HORIZON programme, BY-COVID project (Grant No. 101046203—BY-COVID). National Institute for Infectious Diseases Lazzaro Spallanzani–IRCCS received financial support from the Italian Ministry of Health grant “Ricerca Corrente”. JP, LF: IMI2-JU grants, resources which are composed of financial contributions from the European Union’s Horizon 2020 Research and Innovation Programme and EFPIA [GA: 777365 eTRANSAFE], and the EU H2020 Programme [GA:964537 RISKHUNT3R]; Project 001-P-001647—Valorisation of EGA for Industry and Society funded by the European Regional Development Fund (ERDF) and Generalitat de Catalunya; Institute of Health Carlos III (project IMPaCT-Data, exp. IMP/00019), co-funded by the European Union, European Regional Development Fund (ERDF, “Away to make Europe”). AMo, MP and AV acknowledge the support of the European Commission under the INFORE project (H2020-ICT825070) and the PerMedCoE (H2020-ICT-951773). Contributions by TH and BLP were supported by NIH grant #R35GM119770 to TH. MaGo acknowledges funding from Deutsche Forschungsgemeinschaft (DFG) through grants no. 442326535 (NFDI4Health) and 451265285 (NFDI4Health Task Force COVID-19), from the European Commission through the Horizon 2020 framework program under grant no. 8, Peer Reviewed, "Article signat per més de 50 autors/es: Anna Niarakis, Marek Ostaszewski, Alexander Mazein, Inna Kuperstein, Martina Kutmon, Marc E. Gillespie, Akira Funahashi, Marcio L. 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 Willighagen, Alberto Valdeolivas, Aurelien Dugourd, Francesco Messina, Marina Esteban Medina, Maria Peña-Chilet, Kinza Rian, Sylvain Soliman, Sara S. 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, Cristobal Luis, Bertrand De Meulder, Jean M. Ravel, Bijay Jassal, Venkata P. Satagopam, Guanming Wu, Martin Golebiewski, Piotr Gawron, Laurence Calzone, Jacques S. Beckmann, Chris 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 ", Postprint (published version)

Published
2023

17. Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

Author

Sanofi, Instituto de Salud Carlos III, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), German Research Foundation, Ministero della Salute, European Commission, Generalitat de Catalunya, National Institutes of Health (US), Klaus Tschira Foundation, National Library of Medicine (US), Niarakis, Anna, Ostaszewski, Marek, Mazein, Alexander, Kuperstein, Inna, Kutmon, Martina, Gillespie, Marc E., Funahashi, Akira, Acencio, Marcio Luis, Hemedan, Ahmed, Aichem, Michael, Klein, Karsten, Czauderna, Tobias, Burtscher, Felicia, Yamada, Takahiro G., Hiki, Yusuke, Hiroi, Noriko F., Hu, Finterly, Pham, Nhung, Ehrhart, Friederike, Willighagen, Egon L., Valdeolivas, Alberto, Dugourd, Aurelien, Messina, Francesco, Esteban-Medina, Marina, Peña-Chilet, María, Rian, Kinza, Soliman, Sylvain, Aghamiri, Sara Sadat, Lal Puniya, Bhanwar, Naldi, Aurelien, Helikar, Tomas, Singh, Vidisha, Fariñas Fernández, Marco, Bermudez, Viviam, Tsirvouli, Eirini, Montagud, Arnau, Noël, Vincent, Ponce de León, Miguel, Maier, Dieter, Bauch, Angela, Gyori, Benjamin M., Bachman, John A., Luna, Augustin, Piñero, Janet, Furlong, Laura I., Balaur, Irina BalaurIrina, Rougny, Adrien, Jarosz, Yohan, Overall, Rupert W., Phair, Robert, Perfetto, Livia, Matthews, Lisa, Balaya Rex, Devasahayam Arokia, Orlic-Milacic, Marija, Monraz Gómez, Luis Cristóbal, De Meulder, Bertrand, Ravel, Jean Marie, Jassal, Bijay, Satagopam, Venkata, Wu, Guanming, Golebiewski, Martin, Gawron, Piotr, Calzone, Laurence, Beckmann, Jacques S., Evelo, Chris T., D’Eustachio, Peter, Schreiber, Falk, Sáez-Rodríguez, Julio, Dopazo, Joaquín, Kuiper, Martin, Valencia, Alfonso, Wolkenhauer, Olaf, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, Schneider, Reinhard, COVID- Disease Map Community the COVID-19 Disease Map Community, Sanofi, Instituto de Salud Carlos III, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), German Research Foundation, Ministero della Salute, European Commission, Generalitat de Catalunya, National Institutes of Health (US), Klaus Tschira Foundation, National Library of Medicine (US), Niarakis, Anna, Ostaszewski, Marek, Mazein, Alexander, Kuperstein, Inna, Kutmon, Martina, Gillespie, Marc E., Funahashi, Akira, Acencio, Marcio Luis, Hemedan, Ahmed, Aichem, Michael, Klein, Karsten, Czauderna, Tobias, Burtscher, Felicia, Yamada, Takahiro G., Hiki, Yusuke, Hiroi, Noriko F., Hu, Finterly, Pham, Nhung, Ehrhart, Friederike, Willighagen, Egon L., Valdeolivas, Alberto, Dugourd, Aurelien, Messina, Francesco, Esteban-Medina, Marina, Peña-Chilet, María, Rian, Kinza, Soliman, Sylvain, Aghamiri, Sara Sadat, Lal Puniya, Bhanwar, Naldi, Aurelien, Helikar, Tomas, Singh, Vidisha, Fariñas Fernández, Marco, Bermudez, Viviam, Tsirvouli, Eirini, Montagud, Arnau, Noël, Vincent, Ponce de León, Miguel, Maier, Dieter, Bauch, Angela, Gyori, Benjamin M., Bachman, John A., Luna, Augustin, Piñero, Janet, Furlong, Laura I., Balaur, Irina BalaurIrina, Rougny, Adrien, Jarosz, Yohan, Overall, Rupert W., Phair, Robert, Perfetto, Livia, Matthews, Lisa, Balaya Rex, Devasahayam Arokia, Orlic-Milacic, Marija, Monraz Gómez, Luis Cristóbal, De Meulder, Bertrand, Ravel, Jean Marie, Jassal, Bijay, Satagopam, Venkata, Wu, Guanming, Golebiewski, Martin, Gawron, Piotr, Calzone, Laurence, Beckmann, Jacques S., Evelo, Chris T., D’Eustachio, Peter, Schreiber, Falk, Sáez-Rodríguez, Julio, Dopazo, Joaquín, Kuiper, Martin, Valencia, Alfonso, Wolkenhauer, Olaf, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, Schneider, Reinhard, and COVID- Disease Map Community the COVID-19 Disease Map Community

Abstract

Introduction: 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., Methods: Extensive 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., Results: Results 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., Discussion: The 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
2023

19. Computational Modeling of the Main Signaling Pathways Involved in Mast Cell Activation

Author

Niarakis, Anna, Bounab, Yacine, Grieco, Luca, Roncagalli, Romain, Hesse, Anne-Marie, Garin, Jérôme, Malissen, Bernard, Daëron, Marc, Thieffry, Denis, Compans, Richard W, Series editor, Cooper, Max D., Series editor, Gleba, Yuri Y., Series editor, Honjo, Tasuku, Series editor, Oldstone, Michael B. A., Series editor, Vogt, Peter K., Series editor, Malissen, Bernard, Series editor, Aktories, Klaus, Series editor, Kawaoka, Yoshihiro, Series editor, Rappuoli, Rino, Series editor, Galan, Jorge E., Series editor, Ahmed, Rafi, Series editor, Daeron, Marc, editor, and Nimmerjahn, Falk, editor

Published
2014
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21. High-quality Reconstruction of Disease Mechanisms: Planning, Development and Maintenance

Author

Mazein, Alexander, primary, Acencio, Marcio Luis, additional, Balaur, Irina, additional, Rougny, Adrien, additional, Welter, Danielle, additional, Niarakis, Anna, additional, Ramirez Ardila, Diana, additional, Dogrusoz, Ugur, additional, Gawron, Piotr, additional, Satagopam, Venkata, additional, Gu, Wei, additional, Kremer, Andreas, additional, Schneider, Reinhard, additional, and Ostaszewski, Marek, additional

Published
2022
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23. A versatile and interoperable computational framework for the analysis and modeling of COVID-19 disease mechanisms

Author

Niarakis, Anna, Ostaszewski, Marek, Mazein, Alexander, Kuperstein, Inna, Kutmon, Martina, Gillespie, Marc, Funahashi, Akira, Acencio, Marcio, Hemedan, Ahmed, Aichem, Michael, Klein, Karsten, Czauderna, Tobias, Burtscher, Felicia, Yamada, Takahiro, Hiki, Yusuke, Hiroi, Noriko, Hu, Finterly, Pham, Nhung, Ehrhart, Friederike, Willighagen, Egon, Valdeolivas, Alberto, Dugourd, Aurelien, Messina, Francesco, Esteban-Medina, Marina, Pena-Chilet, Maria, Rian, Kinza, Soliman, Sylvain, Aghamiri, Sara, Puniya, Bhanwar, Naldi, Aurelien, Helikar, Tomas, Singh, Vidisha, Farinas Fernandez, Marco, Bermudez, Viviam, Tsirvouli, Eirini, Montagud, Arnau, Noel, Vincent, Ponce de Leon, Miguel, Maier, Dieter, Bauch, Angela, Gyori, Benjamin, Bachman, John, Luna, Agustin, Pinero, Janet, Furlong, Laura, Balaur, Irina, Rougny, Adrien, Jarosz, Yohan, Overall, Rupert, Phair, Robert, Perfetto, Livia, Matthews, Lisa, Rex, Devasahayam, Orlic-Milacic, Marija, Monraz Gomez, Luis, de Meulder, Bertrand, Ravel, Jean, Jassal, Bijay, Satagopam, Venkata, Wu, Guanming, Golebiewski, Martin, Gawron, Piotr, Calzone, Laurence, Beckmann, Jacques, Evelo, Chris, d'Eustachio, Peter, Schreiber, Falk, Saez-Rodriguez, Julio, Dopazo, Joaquin, Kuiper, Martin, Valencia, Alfonso, Wolkenhauer, Olaf, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, Schneider, Reinhard, 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), Laboratoire de recherche européen pour la polyarthrite rhumatoïde (GenHotel), Université d'Évry-Val-d'Essonne (UEVE)-Université Paris-Saclay, University of Luxembourg [Luxembourg], Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Paris sciences et lettres (PSL), Maastricht Centre for Systems Biology [Maastricht] (MaCSBio), Maastricht University [Maastricht], Ontario Institute for Cancer Research [Canada] (OICR), Ontario Institute for Cancer Research, Keio University, Luxembourg Centre For Systems Biomedicine (LCSB), University of Konstanz, Hochschule Mittweida - University of Applied Sciences, Kanagawa Institute of Technology, Heidelberg University Hospital [Heidelberg], National Institute for Infectious Diseases 'Lazzaro Spallanzani', Hospital Universitario Virgen del Rocío [Sevilla], Biomedicine Institute of Sevilla [Seville, Spain], University of Nebraska–Lincoln, University of Nebraska System, Norwegian University of Science and Technology [Trondheim] (NTNU), Norwegian University of Science and Technology (NTNU), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Harvard Medical School [Boston] (HMS), Universitat Pompeu Fabra [Barcelona] (UPF), National Institute of Advanced Industrial Science and Technology (AIST), Humboldt University Of Berlin, Integrative Bioinformatics Inc [Mountain View], Department of Informatics and System Sciences (Sapienza University of Rome), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), New York University Langone Medical Center (NYU Langone Medical Center), NYU System (NYU), Yenepoya University, Janet Piñero, Laura I. Furlong: IMI2-JU grants, resources which are composed of financial contributions from the European Union’s Horizon 2020 Research and Innovation Programme and EFPIA [GA: 777365 eTRANSAFE], and the EU H2020 Programme [GA:964537 RISKHUNT3R], Project 001-P-001647—Valorisation of EGA for Industry and Society funded by the European Regional Development Fund (ERDF) and Generalitat de Catalunya, and Institute of Health Carlos III (project IMPaCT-Data, exp. IMP/00019), co-funded by the European Union, European Regional Development Fund (ERDF, 'A way to make Europe').

Subjects
SARS-CoV-2, disease maps, systems biology, dynamic models, systems medicine, large-scale community effort, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], mechanistic models
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.

Published
2022

26. Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology

Author

Niarakis, Anna, primary, Waltemath, Dagmar, additional, Glazier, James, additional, Schreiber, Falk, additional, Keating, Sarah M, additional, Nickerson, David, additional, Chaouiya, Claudine, additional, Siegel, Anne, additional, Noël, Vincent, additional, Hermjakob, Henning, additional, Helikar, Tomáš, additional, Soliman, Sylvain, additional, and Calzone, Laurence, additional

Published
2022
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27. Toehold switch based biosensors for sensing the highly trafficked rosewood Dalbergia maritima

Author

Soudier, Paul, primary, Rodriguez Pinzon, Daniel, additional, Reif-Trauttmansdorff, Tristan, additional, Hijazi, Hassan, additional, Cherrière, Maëva, additional, Goncalves Pereira, Cátia, additional, Blaise, Doriane, additional, Pispisa, Maxime, additional, Saint-Julien, Angelyne, additional, Hamlet, William, additional, Nguevo, Melissa, additional, Gomes, Eva, additional, Belkhelfa, Sophia, additional, Niarakis, Anna, additional, Kushwaha, Manish, additional, and Grigoras, Ioana, additional

Published
2022
Full Text
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35. SysMod: the ISCB community for data-driven computational modelling and multi-scale analysis of biological systems

Author

Dräger, Andreas, primary, Helikar, Tomáš, additional, Barberis, Matteo, additional, Birtwistle, Marc, additional, Calzone, Laurence, additional, Chaouiya, Claudine, additional, Hasenauer, Jan, additional, Karr, Jonathan R, additional, Niarakis, Anna, additional, Rodríguez Martínez, María, additional, Saez-Rodriguez, Julio, additional, and Thakar, Juilee, additional

Published
2021
Full Text
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36. COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

Author

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

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

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

Author

Fonds National de la Recherche Luxembourg, European Commission, Federal Ministry of Education and Research (Germany), Ministry of Science, Research and Art Baden-Württemberg, German Center for Infection Research, Netherlands Organisation for Health Research and Development, National Institutes of Health (US), European Molecular Biology Laboratory, Ostaszewski, Marek, Niarakis, Anna, Mazein, Alexander, Kuperstein, Inna, Phair, Robert, Orta-Resendiz, Aurelio, Singh, Vidisha, Aghamiri, Sara Sadat, Acencio, Marcio Luis, Glaab, Enrico, Ruepp, Andreas, Schreiber, Falk, Montagud, Arnau, Ponce de León, Miguel, Funahashi, Akira, Hiki, Yusuke, Hiroi, Noriko, Yamada, Takahiro G., Dräger, Andreas, Renz, Alina, Naveez, Muhammad, Orlic-Milacic, Marija, Bocskei, Zsolt, Messina, Francesco, Börnigen, Daniela, Fergusson, Liam, Conti, Marta, Rameil, Marius, Nakonecnij, Vanessa, Vanhoefer, Jakob, Schmiester, Leonard, Wang, Muying, Senff Ribeiro, Andrea, Ackerman, Emily E., Shoemaker, Jason E., Zucker, Jeremy, Oxford, Kristie, Teuton, Jeremy, Kocakaya, Ebru, Summak, Gökçe Yagmu, Hanspers, Kristina, Kutmon, Martina, Coort, Susan, Rothfels, Karen, Eijssen, Lars, Ehrhart, Friederike, Arokia Balaya Rex, Devasahayam, Slenter, Denise, Martens, Marvin, Pham, Nhung, Haw, Robin, Jassal, Bijay, Matthews, Lisa, Shamovsky, Veronic, Stephan, Ralf, Sevilla, Cristoffer, Varusai, Thawfeek, Ravel, Jean-Marie, Fraser, Rupsha, Ortseifen, Vera, Soliman, Sylvain, Marchesi, Silvia, Gawron, Piotr, Smula, Ewa, Heirendt, Laurent, Satagopam, Venkata, Wu, Guanming, Riutta, Anders, Golebiewski, Martin, Owen, Stuart, Goble, Carole, Valdeolivas, Alberto, Hu, Xiaoming, Overall, Rupert W., Maier, Dieter, Bauch, Angela, Gyori, Benjamin M., Bachman, John A., Vega, Carlos, Groues, Valentin, Vázquez, Miguel, Porras, Pablo, Esteban-Medina, Marina, Licata, Luana, Iannuccelli, Marta, Sacco, Francesca, Nesterova, Anastasia, Yuryev, Anton, Waard, Anita de, Turei, Denes, Luna, Augustín, Babur, Ozgun, Peña-Chilet, María, Rian, Kinza, Helikar, Tomas, Lal Puniya, Bhanwar, Modos, Dezso, Treveil, Agatha, Olbe, Marton, Fobo, Gisela, De Meulder, Bertrand, Ballereau, Stephane, Dugourd, Aurelien, Naldi, Aurelien, Noël, Vincent, Calzone, Laurence, Sander, Chris, Demir, Emek, Korcsmaros, Tamas, Freeman, Tom C., Montrone, Corinna, Auge, Franck, Beckmann, Jacques S., Hasenauer, Jan, Wolkenhauer, Olaf, Wilighagen, Egon L ., Pico, Alexander R., Evelo, Chris T., Gillespie, Marc E., Stein, Lincoln D., Hermjakob, Henning, Brauner, Barbara, D’Eustachio, Peter, Sáez-Rodríguez, Julio, Dopazo, Joaquín, Valencia, Alfonso, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, Schneider, Reinhard, Frishman, Goar, Monraz Gómez, Luis Cristóbal, Somers, Julia, Hoch, Matti, Gupta, Shailendra Kumar, Scheel, Julia, Borlinghaus, Hanna, Czauderna, Tobias, Fonds National de la Recherche Luxembourg, European Commission, Federal Ministry of Education and Research (Germany), Ministry of Science, Research and Art Baden-Württemberg, German Center for Infection Research, Netherlands Organisation for Health Research and Development, National Institutes of Health (US), European Molecular Biology Laboratory, Ostaszewski, Marek, Niarakis, Anna, Mazein, Alexander, Kuperstein, Inna, Phair, Robert, Orta-Resendiz, Aurelio, Singh, Vidisha, Aghamiri, Sara Sadat, Acencio, Marcio Luis, Glaab, Enrico, Ruepp, Andreas, Schreiber, Falk, Montagud, Arnau, Ponce de León, Miguel, Funahashi, Akira, Hiki, Yusuke, Hiroi, Noriko, Yamada, Takahiro G., Dräger, Andreas, Renz, Alina, Naveez, Muhammad, Orlic-Milacic, Marija, Bocskei, Zsolt, Messina, Francesco, Börnigen, Daniela, Fergusson, Liam, Conti, Marta, Rameil, Marius, Nakonecnij, Vanessa, Vanhoefer, Jakob, Schmiester, Leonard, Wang, Muying, Senff Ribeiro, Andrea, Ackerman, Emily E., Shoemaker, Jason E., Zucker, Jeremy, Oxford, Kristie, Teuton, Jeremy, Kocakaya, Ebru, Summak, Gökçe Yagmu, Hanspers, Kristina, Kutmon, Martina, Coort, Susan, Rothfels, Karen, Eijssen, Lars, Ehrhart, Friederike, Arokia Balaya Rex, Devasahayam, Slenter, Denise, Martens, Marvin, Pham, Nhung, Haw, Robin, Jassal, Bijay, Matthews, Lisa, Shamovsky, Veronic, Stephan, Ralf, Sevilla, Cristoffer, Varusai, Thawfeek, Ravel, Jean-Marie, Fraser, Rupsha, Ortseifen, Vera, Soliman, Sylvain, Marchesi, Silvia, Gawron, Piotr, Smula, Ewa, Heirendt, Laurent, Satagopam, Venkata, Wu, Guanming, Riutta, Anders, Golebiewski, Martin, Owen, Stuart, Goble, Carole, Valdeolivas, Alberto, Hu, Xiaoming, Overall, Rupert W., Maier, Dieter, Bauch, Angela, Gyori, Benjamin M., Bachman, John A., Vega, Carlos, Groues, Valentin, Vázquez, Miguel, Porras, Pablo, Esteban-Medina, Marina, Licata, Luana, Iannuccelli, Marta, Sacco, Francesca, Nesterova, Anastasia, Yuryev, Anton, Waard, Anita de, Turei, Denes, Luna, Augustín, Babur, Ozgun, Peña-Chilet, María, Rian, Kinza, Helikar, Tomas, Lal Puniya, Bhanwar, Modos, Dezso, Treveil, Agatha, Olbe, Marton, Fobo, Gisela, De Meulder, Bertrand, Ballereau, Stephane, Dugourd, Aurelien, Naldi, Aurelien, Noël, Vincent, Calzone, Laurence, Sander, Chris, Demir, Emek, Korcsmaros, Tamas, Freeman, Tom C., Montrone, Corinna, Auge, Franck, Beckmann, Jacques S., Hasenauer, Jan, Wolkenhauer, Olaf, Wilighagen, Egon L ., Pico, Alexander R., Evelo, Chris T., Gillespie, Marc E., Stein, Lincoln D., Hermjakob, Henning, Brauner, Barbara, D’Eustachio, Peter, Sáez-Rodríguez, Julio, Dopazo, Joaquín, Valencia, Alfonso, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, Schneider, Reinhard, Frishman, Goar, Monraz Gómez, Luis Cristóbal, Somers, Julia, Hoch, Matti, Gupta, Shailendra Kumar, Scheel, Julia, Borlinghaus, Hanna, and Czauderna, Tobias

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

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

Author

Barcelona Supercomputing Center, Ostaszewski, Marek, Niarakis, Anna, Mazein, Alexander, Kuperstein, Inna, Phair, Robert, Montagud, Arnau, Ponce De Leon, Miguel, Vazquez, Miguel, Valencia, Alfonso, Barcelona Supercomputing Center, Ostaszewski, Marek, Niarakis, Anna, Mazein, Alexander, Kuperstein, Inna, Phair, Robert, Montagud, Arnau, Ponce De Leon, Miguel, Vazquez, Miguel, and Valencia, Alfonso

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., We would like to thank Andjela Tatarovic, architect, and Gina Crovetto, a researcher in the field of cancer, for their help with the design of the top-level view diagrams. We would like to acknowledge the Responsible and Reproducible Research (R3) team of the Luxembourg Centre for Systems Biomedicine for supporting the project and providing necessary communication and data sharing resources. The work presented in this paper was carried out using the ELIXIR Luxembourg tools and services. This study was supported by the Luxembourg National Research Fund (FNR) COVID-19 Fast-Track grant programme, grant COVID-19/2020-1/14715687/CovScreen (E. Glaab); European Commission, INFORE grant H2020-ICT-825070 (A. Montagud, M. Ponce de Leon, M. Vazques and A. Valencia); European Commission, PerMedCoE grant H2020-ICT-951773 (A. Montagud, M. Ponce de Leon, M. Vazques and A. Valencia) the Federal Ministry of Education and Research (BMBF, Germany) and the Baden-Württemberg Ministry of Science, the Excellence Strategy of the German Federal and State Governments (A. Renz); German Center for Infection Research (DZIF), grant no 8020708703 (A. Dräger); The Netherlands Organisation for Health Research and Development (ZonMw), grant no 10430012010015, (M. Kutmon, S. Coort, F. Ehrhart, N. Pham, E.L. Willighagen, C.T. Evelo); H2020 Marie Skłodowska-Curie Actions, grant number 765274 (J. Scheel); National Institutes of Health, USA (NIH), grant number U41 HG003751 (L.D. Stein). The development of Reactome is supported by grants from the US National Institutes of Health (U41 HG003751) and the European Molecular Biology Laboratory., Peer Reviewed, "Article signat per més de 50 autors/es: 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,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 Owen51,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 Sacco57, 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, Postprint (published version)

Published
2021

39. The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling

Author

Touré, Vasundra, Flobak, Åsmund, Niarakis, Anna, Vercruysse, Steven, Kuiper, Martin, Norwegian University of Science and Technology [Trondheim] (NTNU), Norwegian University of Science and Technology (NTNU), Laboratoire de recherche européen pour la polyarthrite rhumatoïde (GenHotel), Université d'Évry-Val-d'Essonne (UEVE)-Université Paris-Saclay, Computational systems biology and optimization (Lifeware), Inria Saclay - Ile de France, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
AcademicSubjects/SCI01060, Databases, Factual, databases, Computer science, 0206 medical engineering, Interoperability, Biological database, interoperability, 02 engineering and technology, External Data Representation, Models, Biological, 03 medical and health sciences, Resource (project management), computational biology, Data retrieval, Database Review, Computer Simulation, Molecular Biology, 030304 developmental biology, 0303 health sciences, logical modeling, causal interactions, Causality, Data science, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Variety (cybernetics), [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, Data exchange, data representation, Software, 020602 bioinformatics, Information Systems, biological pathway
Abstract

Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. Logical regulatory networks can be used to predict biological and cellular behaviors by system perturbations and in silico simulations. Today, broad sets of causal interactions are available in a variety of biological knowledge resources. However, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. It can therefore be challenging to efficiently explore various resources of causal interaction and maintain an overview of recorded contextual information that ensures valid use of the data. This review lists the different types of public resources with causal interactions, the different views on biological processes that they represent, the various data formats they use for data representation and storage, and the data exchange and conversion procedures that are available to extract and download these interactions. This may further raise awareness among the targeted audience, i.e. logical modelers and other scientists interested in molecular causal interactions, but also database managers and curators, about the abundance and variety of causal molecular interaction data, and the variety of tools and approaches to convert them into one interoperable resource.

Published
2020

43. PhysiBoSS-COVID: the Boolean modelling of COVID-19 signalling pathways in a multicellular simulation framework allows for the uncovering of mechanistic insights

Author

Noël, Vincent, Carbonell, Jose, Ponce de Leon, Miguel, Soliman, Sylvain, Niarakis, Anna, Calzone, Laurence, Barillot, Emmanuel, Valencia, Alfonso, and Montagud, Arnau

Subjects
Multiscale modelling, Systems Biology, Boolean modelling, Network to models, COVID-19 infection
Abstract

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has experienced an extremely fast worldwide spreading, producing an unprecedented pandemic situation. As a result, a strong response from the research community resulted in a vast amount of new COVID-19-related publications, covering different clinical and molecular aspects of the disease. In spite of all these efforts, most of the underlying molecular events that lead SARS-CoV-2 infection to the observed clinical traits of COVID-19 remain unclear, since multiple biological pathways, cell types and organs are involved through a set of complex interactions. In this context, COVID-19 Disease Map (C19DM) initiative[1] was launched in order to provide a systems perspective to the COVID-19 infection, by integrating and formalising mechanistic knowledge, using current systems biology standards (https://covid.pages.uni.lu/). This open-access collection of curated pathways is a valuable resource which allows the conversion into different mathematical models, which in turn, can provide interesting mechanistic insights by using an array of simulation methods. One of these methods, PhysiCell[2] is an agent-based flexible cell simulator that can be used to perform diverse multiscale modelling. In fact, PhysiCell has been adapted to the COVID-19 problem recently by incorporating some signalling models of the interactions of virus and human host cells. In this work we present, PhysiBoSS-COVID, an effort to integrate MaBoSS[3], a stochastic Boolean modelling software, into PhysiCell-COVID[6] to allow the leverage of cell- and pathway-specific Boolean models in this framework. To obtain these COVID-19-specific models, we have taken advantage of CaSQ[4] ability to convert all C19DM maps into SBML-qual files, that can subsequently be transformed to MaBoSS-format Boolean models, ready-to-use with PhysiBoSS-COVID. As a proof of concept, we have incorporated models from several immune cells, as well as human epithelial host cells infected by SARS-CoV-2 to our prototype and hereby present preliminary results. PhysiBoSS-COVID, which is based on our previous work PhysiBoSS[5], provides a framework that enables testing of combined genetic and environmental perturbations, and can offer mechanistic insights of SARS-CoV-2 infection and dissemination among human host cells. Finally, PhysiBoSS-COVID was incorporated as a use case into the European HPC/Exascale Centre of Excellence in Personalized Medicine (PerMedCoE, http://permedcoe.eu/), whose purpose is to adapt multiscale modelling to supercomputing environments and to provide an easy-to-use interface to systems biology end users.

Published
2020

44. Combining bottom-up and top-down systems biology methods to obtain an integrative, global RA-specific network

Author

MIAGOUX Quentin, de MEZQUITA Dereck, SING Vidisha, CHALABI Smahane, PETIT-TEIXEIRA Elisabeth, and NIARAKIS Anna

Subjects
Rheumatoid arthritis molecular map, Rheumatoid arthritis specific variants, Machine learning, Rheumatoid arthritis, Network inference, co-regulatory network
Abstract

Rheumatoid arthritis (RA) is a multifactorial autoimmune disease that causes chronic inflammation of the joints, with an aetiology still unclear. RA is a complex inflammatory disease that involves an interconnected array of genetic, environmental, and epigenetic factors. Recently, systems biology and network-based approaches have been proposed to study such complex diseases. Combining multiple data types allows us to discover new knowledge as integration can help compensate for missing or unreliable information. Moreover, if multiple sources of evidence point to the same outcome then it is less likely to obtain false positives. Among computational approaches, machine learning stands out as a promising field in bioinformatics, as it allows for integration of multi-omic biomedical datasets. In this work, we present our efforts to integrate different layers of biological data in the form of networks in order to obtain a global view of the disease. To do so, we make use of publicly available transcriptomic datasets (peripheral blood) relative to RA and a variety of bioinformatics analyses such as statistical analysis, differential expression analysis, and machine learning network inference (Nicolle et al., 2015) to infer an RA-specific TF co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using the state of the art RA map based on the functional overlaps between the two networks (Singh et al., 2020). Lastly, a list of RA specific variants available in DisGeNET (Piñero et al., 2020) will be used as an overlap to highlight key genes associated with known disease mutations. We combine three different biological layers (gene expression, signalling cascades, mutations), obtained by bottom-up prior knowledge-based (DisGenNET, RA map) and top-down data-driven (CoRegNet) methods to build an integrative, disease-specific network. The goal behind this endeavor is to unravel mechanisms governing the regulation of key genes identified as mutation carriers in RA and also derive patient-specific models to gain more insights into the disease heterogeneity. &nbsp

Published
2020
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45. Computational Modeling of the Metabolic Reprogramming in Rheumatoid Arthritis Synovial Fibroblasts and Cancer Associated Fibroblasts

Author

Aghakhani Sahar, Soliman Sylvain, and Niarakis Anna

Subjects
Rheumatoid Arthritis Synovial Fibroblast, Computational Modeling, Metabolic Reprogramming, Glycolytic Switch, Systems Biology, Cancer-Associated Fibroblast
Abstract

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease affecting 1% of the population. Its pathogenesis mainly revolves around synovial hyperplasia, consisting of fibroblast-like synoviocytes (RASFs) accumulation. After acquiring an aggressive phenotype, RASFs reduce contact inhibition, resist to apoptosis, migrate and invade periarticular tissues (e.g. bone and cartilage). Similarly in the Cancer field, the importance of the stromal microenvironment has been recognized for years. It is widely accepted that cancer develops as a result of genetic and epigenetic alterations in clonal cells, but that growth, survival, and metastasis are regulated by stromal cells, also known as Cancer Associated Fibroblasts (CAFs). Thus, proliferating and aggressive fibroblasts not only seem to be a key feature in RA but also in cancer. An increasing number of studies have shown obvious parallels between CAFs and RASFs, not considering them anymore as passive responders but key disease effectors. A critical property lies in CAFs’ and RAFs’ altered glucose metabolism: they both are prone to metabolic reprogramming leading to a glycolytic switch. This common feature could prove to be critical and identifying the molecular links between metabolic reprogramming and fibroblasts activation may represent a serious lead in the development of complementary therapies for these pathologies. To study similarities in RASFs and CAFs glucose metabolism regulation, detailed molecular maps will be used as knowledge bases about the associated phenotypes. Regarding RA, a state-of-the-art molecular map has been published (Singh et al., 2020) illustrating pathways involved and allowing to extract specific fibroblast information. Similarly, a molecular interaction map for CAFs is also available (Kuperstein et al., 2015). After both being greatly enriched with metabolic information (focusing on the glycolytic switch), these maps will represent templates for various stages of data visualization and bioinformatic analysis. Both up-to-date molecular maps will also be converted into cell-specific executable Boolean models of intracellular networks, using the CaSQ tool (Aghamiri et al., 2020). By conducting several in-silico perturbations, fibroblasts’ response to different conditions will be studied, allowing to decipher the input/output relationships at a cellular level. Based on the bioinformatic and dynamic analysis, the identification of a regulatory mechanism (common or not) explaining fibroblasts’ metabolic reprogramming in disease-specific conditions and the phenotypic changes leading to the pathogenic transformation in RASFs and CAFs is expected. This will lead to a better understanding of the mechanisms underlying RA and Cancer pathogenesis at a global systemic level, potentially leading to the identification of new therapeutic targets.

Published
2020
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46. Automated inference of Boolean models from molecular interaction maps using CaSQ

Author

Aghamiri, Sara Sadat, Singh, Vidisha, Naldi, Aurélien, Helikar, Tomáš, Soliman, Sylvain, Niarakis, Anna, Laboratoire de recherche européen pour la polyarthrite rhumatoïde (GenHotel), Université d'Évry-Val-d'Essonne (UEVE)-Université Paris-Saclay, Institut de biologie de l'ENS Paris (IBENS), 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)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), 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), University of Nebraska [Lincoln], University of Nebraska System, 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), 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 Nebraska–Lincoln, ANR-16-CE18-0029,BIOPSY,Système de programmation à base de réseaux biochimiques(2016), Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Département de Biologie - 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), and Soliman, Sylvain

Subjects
[INFO.INFO-PL]Computer Science [cs]/Programming Languages [cs.PL], AcademicSubjects/SCI01060, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, Systems Biology, Original Papers, Models, Biological, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Software, ComputingMilieux_MISCELLANEOUS, [INFO.INFO-PL] Computer Science [cs]/Programming Languages [cs.PL]
Abstract

Motivation Molecular interaction maps have emerged as a meaningful way of representing biological mechanisms in a comprehensive and systematic manner. However, their static nature provides limited insights to the emerging behaviour of the described biological system under different conditions. Computational modelling provides the means to study dynamic properties through in silico simulations and perturbations. We aim to bridge the gap between static and dynamic representations of biological systems with CaSQ, a software tool that infers Boolean rules based on the topology and semantics of molecular interaction maps built with CellDesigner. Results We developed CaSQ by defining conversion rules and logical formulas for inferred Boolean models according to the topology and the annotations of the starting molecular interaction maps. We used CaSQ to produce executable files of existing molecular maps that differ in size, complexity and the use of Systems Biology Graphical Notation (SBGN) standards. We also compared, where possible, the manually built logical models corresponding to a molecular map to the ones inferred by CaSQ. The tool is able to process large and complex maps built with CellDesigner (either following SBGN standards or not) and produce Boolean models in a standard output format, Systems Biology Marked Up Language-qualitative (SBML-qual), that can be further analyzed using popular modelling tools. References, annotations and layout of the CellDesigner molecular map are retained in the obtained model, facilitating interoperability and model reusability. Availability and implementation The present tool is available online: https://lifeware.inria.fr/∼soliman/post/casq/ and distributed as a Python package under the GNU GPLv3 license. The code can be accessed here: https://gitlab.inria.fr/soliman/casq. Supplementary information Supplementary data are available at Bioinformatics online.

Published
2020

50. Setting the basis of best practices and standards for curation and annotation of logical models in biology—highlights of the [BC]2 2019 CoLoMoTo/SysMod Workshop

Author

Niarakis, Anna, primary, Kuiper, Martin, additional, Ostaszewski, Marek, additional, Malik Sheriff, Rahuman S, additional, Casals-Casas, Cristina, additional, Thieffry, Denis, additional, Freeman, Tom C, additional, Thomas, Paul, additional, Touré, Vasundra, additional, Noël, Vincent, additional, Stoll, Gautier, additional, Saez-Rodriguez, Julio, additional, Naldi, Aurélien, additional, Oshurko, Eugenia, additional, Xenarios, Ioannis, additional, Soliman, Sylvain, additional, Chaouiya, Claudine, additional, Helikar, Tomáš, additional, and Calzone, Laurence, additional

Published
2020
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