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1. 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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2. 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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3. COVID-19 outbreak in Wuhan demonstrates the limitations of publicly available case numbers for epidemiological modeling

Author

Elba Raimúndez, Erika Dudkin, Jakob Vanhoefer, Emad Alamoudi, Simon Merkt, Lara Fuhrmann, Fan Bai, and Jan Hasenauer

Subjects
Compartment model, SEIRD, Parameter estimation, Model selection, Uncertainty analysis, Infectious and parasitic diseases, RC109-216
Abstract

Epidemiological models are widely used to analyze the spread of diseases such as the global COVID-19 pandemic caused by SARS-CoV-2. However, all models are based on simplifying assumptions and often on sparse data. This limits the reliability of parameter estimates and predictions.In this manuscript, we demonstrate the relevance of these limitations and the pitfalls associated with the use of overly simplistic models. We considered the data for the early phase of the COVID-19 outbreak in Wuhan, China, as an example, and perform parameter estimation, uncertainty analysis and model selection for a range of established epidemiological models. Amongst others, we employ Markov chain Monte Carlo sampling, parameter and prediction profile calculation algorithms.Our results show that parameter estimates and predictions obtained for several established models on the basis of reported case numbers can be subject to substantial uncertainty. More importantly, estimates were often unrealistic and the confidence/credibility intervals did not cover plausible values of critical parameters obtained using different approaches. These findings suggest, amongst others, that standard compartmental models can be overly simplistic and that the reported case numbers provide often insufficient information for obtaining reliable and realistic parameter values, and for forecasting the evolution of epidemics.

Published
2021
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7. COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host interaction mechanisms

Author

Dezso Modos, Julia Scheel, Ralf Stephan, Piotr Gawron, Kristina Hanspers, Jean-Marie Ravel, Alexander Mazein, Corinna Montrone, Alberto Valdeolivas, Marek Ostaszewski, Susan L. Coort, Hiroaki Kitano, Tom C. Freeman, Vera Ortseifen, Vanessa Nakonecnij, M Orlic-Milacic, Jan Hasenauer, Veronica Shamovsky, Venkata P. Satagopam, Goar Frishman, M. Vazquez, D A B Rex, Miguel Ponce de Leon, Sylvain Soliman, Luana Licata, Ebru Kocakaya, Kinza Rian, Yusuke Hiki, Marton Olbei, Martina Kutmon, Bijay Jassal, Akira Funahashi, Lisa Matthews, Xiaoming Hu, Marcio Luis Acencio, Zsolt Bocskei, Robin Haw, Joaquín Dopazo, Agatha Treveil, Andrea Senff-Ribeiro, Tomáš Helikar, Chris T. Evelo, John A. Bachman, Julio Saez-Rodriguez, Takahiro G. Yamada, Emmanuel Barillot, Anton Yuryev, Bertrand De Meulder, Matti Hoch, Guanming Wu, Daniela Börnigen, Rudi Balling, Karen Rothfels, Pablo Porras, Hanna Borlinghaus, Andreas Dräger, Aurélien Naldi, Marta Iannuccelli, Leonard Schmiester, Dieter Maier, Jason E. Shoemaker, Valentin Grouès, Jakob Vanhoefer, Marius Rameil, Sara Sadat Aghamiri, Tobias Czauderna, Inna Kuperstein, Nhung Pham, Reinhard Schneider, Marina Esteban-Medina, Carole Goble, Benjamin M. Gyori, Emily E. Ackerman, Anita de Waard, Bhanwar Lal Puniya, Olaf Wolkenhauer, Alfonso Valencia, Julia Somers, Ewa Smula, Gisela Fobo, Robert Phair, Chris Sander, Laurent Heirendt, Carlos Vega, Jeremy Teuton, Emek Demir, Alina Renz, Tamas Korcsmaros, Vidisha Singh, Anastasia P. Nesterova, Andreas Ruepp, Lars M. T. Eijssen, Thawfeek M. Varusai, Aurelien Dugourd, Silvia Marchesi, Cristoffer Sevilla, Falk Schreiber, Barbara Brauner, Noriko Hiroi, Augustin Luna, Muying Wang, Shailendra K. Gupta, Egon Willighagen, Charles Auffray, Denise Slenter, Kristie L. Oxford, Franck Augé, Friederike Ehrhart, Muhammad Naveez, Stuart Owen, Arnau Montagud, Liam Fergusson, Enrico Glaab, Martin Golebiewski, Laurence Calzone, Alexander R. Pico, Dénes Türei, Marvin Martens, Henning Hermjakob, Rupert W. Overall, Jeremy Zucker, Özgün Babur, Gokce Yagmur Summak, Lincoln Stein, Anders Riutta, Anna Niarakis, Aurelio Orta-Resendiz, Francesca Sacco, Maria Peña-Chilet, Marta Conti, Jacques S. Beckmann, Angela Bauch, and University of Luxembourg [Luxembourg]

Subjects
0303 health sciences, Coronavirus disease 2019 (COVID-19), Computer science, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Interoperability, Disease, Data science, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Domain (software engineering), 03 medical and health sciences, 0302 clinical medicine, 030220 oncology & carcinogenesis, Relevance (information retrieval), 030304 developmental biology
Abstract

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. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in the enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.; 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. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in the enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.

Published
2021

8. COVID-19 outbreak in Wuhan demonstrates the limitations of publicly available case numbers for epidemiological modelling

Author

Emad Alamoudi, Elba Raimúndez, Fan Bai, Jakob Vanhoefer, Simon Merkt, Erika Dudkin, Lara Fuhrmann, and Jan Hasenauer

Subjects
China, Computer science, Epidemiology, 030231 tropical medicine, Monte Carlo method, Model selection, 01 natural sciences, Microbiology, Article, Compartment Model, Model Selection, Parameter Estimation, Seird, Uncertainty Analysis, lcsh:Infectious and parasitic diseases, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Virology, Credibility, Econometrics, Parameter estimation, Humans, Relevance (information retrieval), lcsh:RC109-216, 030212 general & internal medicine, 0101 mathematics, Pandemics, Reliability (statistics), Uncertainty analysis, 030304 developmental biology, Sparse matrix, SEIRD, 0303 health sciences, Models, Statistical, Markov chain, Estimation theory, Uncertainty, Public Health, Environmental and Occupational Health, COVID-19, Reproducibility of Results, Markov Chains, 3. Good health, ddc, Compartment model, Range (mathematics), Infectious Diseases, Parasitology, Monte Carlo Method, Algorithms, Forecasting
Abstract

Epidemiological models are widely used to analyze the spread of diseases such as the global COVID-19 pandemic caused by SARS-CoV-2. However, all models are based on simplifying assumptions and often on sparse data. This limits the reliability of parameter estimates and predictions. In this manuscript, we demonstrate the relevance of these limitations and the pitfalls associated with the use of overly simplistic models. We considered the data for the early phase of the COVID-19 outbreak in Wuhan, China, as an example, and perform parameter estimation, uncertainty analysis and model selection for a range of established epidemiological models. Amongst others, we employ Markov chain Monte Carlo sampling, parameter and prediction profile calculation algorithms. Our results show that parameter estimates and predictions obtained for several established models on the basis of reported case numbers can be subject to substantial uncertainty. More importantly, estimates were often unrealistic and the confidence/credibility intervals did not cover plausible values of critical parameters obtained using different approaches. These findings suggest, amongst others, that standard compartmental models can be overly simplistic and that the reported case numbers provide often insufficient information for obtaining reliable and realistic parameter values, and for forecasting the evolution of epidemics.

Published
2020
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9. 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, Wilighagen, 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, 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, Wilighagen, 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, and Reinhard, Schneider

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., source:https://www.embopress.org/doi/full/10.15252/msb.202110387

Published
2021

10. Comparison of null models for combination drug therapy reveals Hand model as biochemically most plausible

Author

Mark Sinzger, Carolin Loos, Jan Hasenauer, and Jakob Vanhoefer

Subjects
0301 basic medicine, Multidisciplinary, media_common.quotation_subject, lcsh:R, lcsh:Medicine, Ambiguity, Common framework, Models, Theoretical, Combination drug therapy, Article, ddc, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Applied mathematics, lcsh:Q, Computer Simulation, Drug Interactions, Drug Therapy, Combination, Combination method, lcsh:Science, Practical implications, 030217 neurology & neurosurgery, Mathematics, media_common
Abstract

Null models for the effect of combination therapies are widely used to evaluate synergy and antagonism of drugs. Due to the relevance of null models, their suitability is continuously discussed. Here, we contribute to the discussion by investigating the properties of five null models. Our study includes the model proposed by David J. Hand, which we refer to as Hand model. The Hand model has been introduced almost 20 years ago but hardly was used and studied. We show that the Hand model generalizes the principle of dose equivalence compared to the Loewe model and resolves the ambiguity of the Tallarida model. This provides a solution to the persisting conflict about the compatibility of two essential model properties: the sham combination principle and the principle of dose equivalence. By embedding several null models into a common framework, we shed light in their biochemical validity and provide indications that the Hand model is biochemically most plausible. We illustrate the practical implications and differences between null models by examining differences of null models on published data.

Published
2019

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