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29 results on '"Naldi, Aurelien"'

1. BioSimulators: a central registry of simulation engines and services for recommending specific tools

Author

Shaikh, Bilal, Smith, Lucian P., Vasilescu, Dan, Marupilla, Gnaneswara, Wilson, Michael, Agmon, Eran, Agnew, Henry, Andrews, Steven S., Anwar, Azraf, Beber, Moritz E., Bergmann, Frank T., Brooks, David, Brusch, Lutz, Calzone, Laurence, Choi, Kiri, Cooper, Joshua, Detloff, John, Drawert, Brian, Dumontier, Michel, Ermentrout, G. Bard, Faeder, James R., Freiburger, Andrew P., Fröhlich, Fabian, Funahashi, Akira, Garny, Alan, Gennari, John H., Gleeson, Padraig, Goelzer, Anne, Haiman, Zachary, Hellerstein, Joseph L., Hoops, Stefan, Ison, Jon C., Jahn, Diego, Jakubowski, Henry V., Jordan, Ryann, Kalaš, Matúš, König, Matthias, Liebermeister, Wolfram, Mandal, Synchon, McDougal, Robert, Medley, J. Kyle, Mendes, Pedro, Müller, Robert, Myers, Chris J., Naldi, Aurelien, Nguyen, Tung V. N., Nickerson, David P., Olivier, Brett G., Patoliya, Drashti, Paulevé, Loïc, Petzold, Linda R., Priya, Ankita, Rampadarath, Anand K., Rohwer, Johann M., Saglam, Ali S., Singh, Dilawar, Sinha, Ankur, Snoep, Jacky, Sorby, Hugh, Spangler, Ryan, Starruß, Jörn, Thomas, Payton J., van Niekerk, David, Weindl, Daniel, Zhang, Fengkai, Zhukova, Anna, Goldberg, Arthur P., Blinov, Michael L., Sauro, Herbert M., Moraru, Ion I., and Karr, Jonathan R.

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Computational Engineering, Finance, and Science, Quantitative Biology - Molecular Networks
Abstract

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line, and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML, and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations., Comment: 6 pages, 2 figures

Published
2022

2. Linear cuts in Boolean networks

Author

Naldi, Aurélien, Richard, Adrien, and Tonello, Elisa

Subjects
Computer Science - Discrete Mathematics, Quantitative Biology - Molecular Networks, 94C99, 92B05, 06E30, 68Q10, 37B15
Abstract

Boolean networks are popular tools for the exploration of qualitative dynamical properties of biological systems. Several dynamical interpretations have been proposed based on the same logical structure that captures the interactions between Boolean components. They reproduce, in different degrees, the behaviours emerging in more quantitative models. In particular, regulatory conflicts can prevent the standard asynchronous dynamics from reproducing some trajectories that might be expected upon inspection of more detailed models. We introduce and study the class of networks with linear cuts, where linear components -- intermediates with a single regulator and a single target -- eliminate the aforementioned regulatory conflicts. The interaction graph of a Boolean network admits a linear cut when a linear component occurs in each cycle and in each path from components with multiple targets to components with multiple regulators. Under this structural condition the attractors are in one-to-one correspondence with the minimal trap spaces, and the reachability of attractors can also be easily characterized. Linear cuts provide the base for a new interpretation of the Boolean semantics that captures all behaviours of multi-valued refinements with regulatory thresholds that are uniquely defined for each interaction, and contribute a new approach for the investigation of behaviour of logical models.

Published
2022

4. LNetReduce: Tool for Reducing Linear Dynamic Networks with Separated Timescales

Author

Buffard, Marion, Desoeuvres, Aurélien, Naldi, Aurélien, Requilé, Clément, Zinovyev, Andrei, Radulescu, Ovidiu, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cinquemani, Eugenio, editor, and Paulevé, Loïc, editor

Published
2021
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5. Geometric analysis of pathways dynamics: application to versatility of TGF-{\beta} receptors

Author

Samal, Satya Swarup, Naldi, Aurélien, Grigoriev, Dima, Weber, Andreas, Théret, Nathalie, and Radulescu, Ovidiu

Subjects
Quantitative Biology - Molecular Networks
Abstract

We propose a new geometric approach to describe the qualitative dynamics of chemical reactions networks. By this method we identify metastable regimes, defined as low dimensional regions of the phase space close to which the dynamics is much slower compared to the rest of the phase space. Given the network topology and the orders of magnitude of kinetic parameters, the number of such metastable regimes is finite. The dynamics of the network can be described as a sequence of jumps from one metastable regime to another. We show that a geometrically computed connectivity graph restricts the set of possible jumps. We also provide finite state machine (Markov chain) models for such dynamic changes. Applied to signal transduction models, our approach unravels dynamical and functional capacities of signaling pathways, as well as parameters responsible for specificity of the pathway response. In particular, for a model of TGF$\beta$ signalling, we find that the ratio of TGFBR1 to TGFBR2 concentrations can be used to discriminate between metastable regimes. Using expression data from the NCI60 panel of human tumor cell lines, we show that aggressive and non-aggressive tumour cell lines function in different metastable regimes and can be distinguished by measuring the relative concentrations of receptors of the two types., Comment: arXiv admin note: text overlap with arXiv:1504.07833

Published
2015

6. Symbolic dynamics of biochemical pathways as finite states machines

Author

Radulescu, Ovidiu, Samal, Satya Swarup, Naldi, Aurélien, Grigoriev, Dima, and Weber, Andreas

Subjects
Quantitative Biology - Molecular Networks
Abstract

We discuss the symbolic dynamics of biochemical networks with separate timescales. We show that symbolic dynamics of monomolecular reaction networks with separated rate constants can be described by deterministic, acyclic automata with a number of states that is inferior to the number of biochemical species. For nonlinear pathways, we propose a general approach to approximate their dynamics by finite state machines working on the metastable states of the network (long life states where the system has slow dynamics). For networks with polynomial rate functions we propose to compute metastable states as solutions of the tropical equilibration problem. Tropical equilibrations are defined by the equality of at least two dominant monomials of opposite signs in the differential equations of each dynamic variable. In algebraic geometry, tropical equilibrations are tantamount to tropical prevarieties, that are finite intersections of tropical hypersurfaces.

Published
2015

7. SBML Qualitative Models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools

Author

Chaouiya, Claudine, Berenguier, Duncan, Keating, Sarah M, Naldi, Aurelien, van Iersel, Martijn P., Rodriguez, Nicolas, Dräger, Andreas, Büchel, Finja, Cokelaer, Thomas, Kowal, Bryan, Wicks, Benjamin, Gonçalves, Emanuel, Dorier, Julien, Page, Michel, Monteiro, Pedro T., von Kamp, Axel, Xenarios, Ioannis, de Jong, Hidde, Hucka, Michael, Klamt, Steffen, Thieffry, Denis, Novère, Nicolas Le, Saez-Rodriguez, Julio, and Helikar, Tomáš

Subjects
Quantitative Biology - Molecular Networks
Abstract

Background: Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing. Results: We present the System Biology Markup Language (SBML) Qualitative Models Package ("qual"), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the cooperative development of the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyze qualitative models. Conclusion: SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks., Comment: 29 pages, 7 figures

Published
2013

9. 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

12. Efficient Handling of Large Signalling-Regulatory Networks by Focusing on Their Core Control

Author

Naldi, Aurélien, Monteiro, Pedro T., Chaouiya, Claudine, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Istrail, Sorin, Series editor, Pevzner, Pavel, Series editor, Waterman, Michael S., Series editor, Gilbert, David, editor, and Heiner, Monika, editor

Published
2012
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13. 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

14. A Reduction of Logical Regulatory Graphs Preserving Essential Dynamical Properties

Author

Naldi, Aurélien, Remy, Elisabeth, Thieffry, Denis, Chaouiya, Claudine, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael S., editor, Degano, Pierpaolo, editor, and Gorrieri, Roberto, editor

Published
2009
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16. BioSimulators: a central registry of simulation engines and services for recommending specific tools

Author

Shaikh, Bilal, primary, Smith, Lucian P, additional, Vasilescu, Dan, additional, Marupilla, Gnaneswara, additional, Wilson, Michael, additional, Agmon, Eran, additional, Agnew, Henry, additional, Andrews, Steven S, additional, Anwar, Azraf, additional, Beber, Moritz E, additional, Bergmann, Frank T, additional, Brooks, David, additional, Brusch, Lutz, additional, Calzone, Laurence, additional, Choi, Kiri, additional, Cooper, Joshua, additional, Detloff, John, additional, Drawert, Brian, additional, Dumontier, Michel, additional, Ermentrout, G Bard, additional, Faeder, James R, additional, Freiburger, Andrew P, additional, Fröhlich, Fabian, additional, Funahashi, Akira, additional, Garny, Alan, additional, Gennari, John H, additional, Gleeson, Padraig, additional, Goelzer, Anne, additional, Haiman, Zachary, additional, Hasenauer, Jan, additional, Hellerstein, Joseph L, additional, Hermjakob, Henning, additional, Hoops, Stefan, additional, Ison, Jon C, additional, Jahn, Diego, additional, Jakubowski, Henry V, additional, Jordan, Ryann, additional, Kalaš, Matúš, additional, König, Matthias, additional, Liebermeister, Wolfram, additional, Sheriff, Rahuman S Malik, additional, Mandal, Synchon, additional, McDougal, Robert, additional, Medley, J Kyle, additional, Mendes, Pedro, additional, Müller, Robert, additional, Myers, Chris J, additional, Naldi, Aurelien, additional, Nguyen, Tung V N, additional, Nickerson, David P, additional, Olivier, Brett G, additional, Patoliya, Drashti, additional, Paulevé, Loïc, additional, Petzold, Linda R, additional, Priya, Ankita, additional, Rampadarath, Anand K, additional, Rohwer, Johann M, additional, Saglam, Ali S, additional, Singh, Dilawar, additional, Sinha, Ankur, additional, Snoep, Jacky, additional, Sorby, Hugh, additional, Spangler, Ryan, additional, Starruß, Jörn, additional, Thomas, Payton J, additional, van Niekerk, David, additional, Weindl, Daniel, additional, Zhang, Fengkai, additional, Zhukova, Anna, additional, Goldberg, Arthur P, additional, Schaff, James C, additional, Blinov, Michael L, additional, Sauro, Herbert M, additional, Moraru, Ion I, additional, and Karr, Jonathan R, additional

Published
2022
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18. 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, Gomez, Luis Cristobal Monraz, Somers, Julia, Hoch, Matti, Gupta, Shailendra Kumar, Scheel, Julia, Borlinghaus, Hanna, Czauderna, Tobias, Schreiber, Falk, Montagud, Arnau, de Leon, Miguel Ponce, Funahashi, Akira, Hiki, Yusuke, Hiroi, Noriko, Yamada, Takahiro G., Draeger, Andreas, Renz, Alina, Naveez, Muhammad, Bocskei, Zsolt, Messina, Francesco, Boernigen, 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, Gokce Yagmur, 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, Ribeiro, Andrea Senff, 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, Pena-Chilet, Maria, Rian, Kinza, Helikar, Tomas, Puniya, Bhanwar Lal, Modos, Dezso, Treveil, Agatha, Olbei, Marton, De Meulder, Bertrand, Ballereau, Stephane, Dugourd, Aurelien, Naldi, Aurelien, Noel, Vincent, Calzone, Laurence, Sander, Chris, Demir, Emek, Korcsmaros, Tamas, Freeman, Tom C., 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, D'Eustachio, Peter, Saez-Rodriguez, Julio, Dopazo, Joaquin, Valencia, Alfonso, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, Schneider, Reinhard, 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, Gomez, Luis Cristobal Monraz, Somers, Julia, Hoch, Matti, Gupta, Shailendra Kumar, Scheel, Julia, Borlinghaus, Hanna, Czauderna, Tobias, Schreiber, Falk, Montagud, Arnau, de Leon, Miguel Ponce, Funahashi, Akira, Hiki, Yusuke, Hiroi, Noriko, Yamada, Takahiro G., Draeger, Andreas, Renz, Alina, Naveez, Muhammad, Bocskei, Zsolt, Messina, Francesco, Boernigen, 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, Gokce Yagmur, 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, Ribeiro, Andrea Senff, 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, Pena-Chilet, Maria, Rian, Kinza, Helikar, Tomas, Puniya, Bhanwar Lal, Modos, Dezso, Treveil, Agatha, Olbei, Marton, De Meulder, Bertrand, Ballereau, Stephane, Dugourd, Aurelien, Naldi, Aurelien, Noel, Vincent, Calzone, Laurence, Sander, Chris, Demir, Emek, Korcsmaros, Tamas, Freeman, Tom C., 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, 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
Full Text
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19. 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

20. Dynamical Boolean Modeling of Immunogenic Cell Death

Author

Checcoli, Andrea, Pol, Jonathan, Naldi, Aurelien, Noël, Vincent, Barillot, Emmanuel, Kroemer, Guido, Thieffry, Denis, Calzone, Laurence, Stoll, Gautier, 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), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), Institut Gustave Roussy (IGR), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de 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 de Bioinformatique (CBIO), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Karolinska University Hospital [Stockholm], Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), Département de Biologie - 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)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and 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)

Subjects
logical modeling, Physiology, Physiology (medical), [SDV]Life Sciences [q-bio], immunogenic cell death, cytotoxic CD8+ T lymphocytes, dendritic cells, antitumor immune response, Original Research
Abstract

International audience; As opposed to the standard tolerogenic apoptosis, immunogenic cell death (ICD) constitutes a type of cellular demise that elicits an adaptive immune response. ICD has been characterized in malignant cells following cytotoxic interventions, such as chemotherapy or radiotherapy. Briefly, ICD of cancer cells releases some stress/danger signals that attract and activate dendritic cells (DCs). The latter can then engulf and cross-present tumor antigens to T lymphocytes, thus priming a cancer-specific immunity. This series of reactions works as a positive feedback loop where the antitumor immunity further improves the therapeutic efficacy by targeting cancer cells spared by the cytotoxic agent. However, not all chemotherapeutic drugs currently approved for cancer treatment are able to stimulate bona fide ICD: some commonly used agents, such as cisplatin or 5-fluorouracil, are unable to activate all features of ICD. Therefore, a better characterization of the process could help identify some gene or protein candidates to target pharmacologically and suggest combinations of drugs that would favor/increase antitumor immune response. To this end, we have built a mathematical model of the major cell types that intervene in ICD, namely cancer cells, DCs, CD8+ and CD4+ T cells. Our model not only integrates intracellular mechanisms within each individual cell entity, but also incorporates intercellular communications between them. The resulting cell population model recapitulates key features of the dynamics of ICD after an initial treatment, in particular the time-dependent size of the different cell types. The model is based on a discrete Boolean formalism and is simulated by means of a software tool, UPMaBoSS, which performs stochastic simulations with continuous time, considering the dynamics of the system at the cell population level with appropriate timing of events, and accounting for death and division of each cell type. With this model, the time scales of some of the processes involved in ICD, which are challenging to measure experimentally, have been predicted. In addition, our model analysis led to the identification of actionable targets for boosting ICD-induced antitumor response. All computational analyses and results are compiled in interactive notebooks which cover the presentation of the network structure, model simulations, and parameter sensitivity analyses.

Published
2020
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21. PTPN13 induces cell junction stabilization and inhibits mammary tumor invasiveness

Author

Hamyeh, Mohamed, Bernex, Florence, Larive, Romain M., Naldi, Aurelien, Urbach, Serge, Simony-Lafontaine, Joelle, Hendriks, W.J.A.J., Freiss, Gilles, Hamyeh, Mohamed, Bernex, Florence, Larive, Romain M., Naldi, Aurelien, Urbach, Serge, Simony-Lafontaine, Joelle, Hendriks, W.J.A.J., and Freiss, Gilles

Abstract

Contains fulltext : 214494.pdf (publisher's version ) (Open Access)

Published
2020

22. SBML Level 3: an extensible format for the exchange and reuse of biological models

Author

Computer Science, Keating, Sarah M., Waltemath, Dagmar, Koenig, Matthias, Zhang, Fengkai, Draeger, Andreas, Chaouiya, Claudine, Bergmann, Frank T., Finney, Andrew, Gillespie, Colin S., Helikar, Tomas, Hoops, Stefan, Malik-Sheriff, Rahuman S., Moodie, Stuart L., Moraru, Ion I., Myers, Chris J., Naldi, Aurelien, Olivier, Brett G., Sahle, Sven, Schaff, James C., Smith, Lucian P., Swat, Maciej J., Thieffry, Denis, Watanabe, Leandro, Wilkinson, Darren J., Blinov, Michael L., Begley, Kimberly, Faeder, James R., Gomez, Harold F., Hamm, Thomas M., Inagaki, Yuichiro, Liebermeister, Wolfram, Lister, Allyson L., Lucio, Daniel, Mjolsness, Eric, Proctor, Carole J., Raman, Karthik, Rodriguez, Nicolas, Shaffer, Clifford A., Shapiro, Bruce E., Stelling, Joerg, Swainston, Neil, Tanimura, Naoki, Wagner, John, Meier-Schellersheim, Martin, Sauro, Herbert M., Palsson, Bernhard, Bolouri, Hamid, Kitano, Hiroaki, Funahashi, Akira, Hermjakob, Henning, Doyle, John C., Hucka, Michael, Computer Science, Keating, Sarah M., Waltemath, Dagmar, Koenig, Matthias, Zhang, Fengkai, Draeger, Andreas, Chaouiya, Claudine, Bergmann, Frank T., Finney, Andrew, Gillespie, Colin S., Helikar, Tomas, Hoops, Stefan, Malik-Sheriff, Rahuman S., Moodie, Stuart L., Moraru, Ion I., Myers, Chris J., Naldi, Aurelien, Olivier, Brett G., Sahle, Sven, Schaff, James C., Smith, Lucian P., Swat, Maciej J., Thieffry, Denis, Watanabe, Leandro, Wilkinson, Darren J., Blinov, Michael L., Begley, Kimberly, Faeder, James R., Gomez, Harold F., Hamm, Thomas M., Inagaki, Yuichiro, Liebermeister, Wolfram, Lister, Allyson L., Lucio, Daniel, Mjolsness, Eric, Proctor, Carole J., Raman, Karthik, Rodriguez, Nicolas, Shaffer, Clifford A., Shapiro, Bruce E., Stelling, Joerg, Swainston, Neil, Tanimura, Naoki, Wagner, John, Meier-Schellersheim, Martin, Sauro, Herbert M., Palsson, Bernhard, Bolouri, Hamid, Kitano, Hiroaki, Funahashi, Akira, Hermjakob, Henning, Doyle, John C., and Hucka, Michael

Abstract

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developedSBMLLevel 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades ofSBMLand a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and howSBMLLevel 3 provides the foundation needed to support this evolution.

Published
2020

24. Maboss 2.0: An Environment For Stochastic Boolean Modeling

Author

Stoll, Gautier, Caron, Barthelemy, Viara, Eric, Dugourd, Aurelien, Zinovyev, Andrei, Naldi, Aurelien, and Kroemer, Guido

Abstract

Modeling of signaling pathways is an important step towards the understanding and the treatment of diseases such as cancers, HIV or auto-immune diseases. MaBoSS is a software that allows to simulate populations of cells and to model stochastically the intracellular mechanisms that are deregulated in diseases. MaBoSS provides an output of a Boolean model in the form of time-dependent probabilities, for all biological entities (genes, proteins, phenotypes, etc.) of the model.

Published
2017
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26. Differential regulation of RNA polymerase III genes during liver regeneration

Author

Yeganeh, Meghdad, Praz, Viviane, Carmeli, Cristian, Villeneuve, Dominic, Rib, Leonor, Guex, Nicolas, Herr, Winship, Delorenzi, Mauro, Hernandez, Nouria, Deplancke, Bart, Desvergne, Beatrice, Naef, Felix, Rougemont, Jacques, Schibler, Ueli, Andersin, Teemu, Cousin, Pascal, Gilardi, Federica, Gos, Pascal, Lammers, Fabienne, Lopes, Maykel, Mange, Francois, Minocha, Shilpi, Raghav, Sunil, Fabbretti, Roberto, Vlegel, Volker, Xenarios, Ioannis, Migliavacca, Eugenia, David, Fabrice, Jarosz, Yohan, Kuznetsov, Dmitry, Liechti, Robin, Martin, Olivier, Delafontaine, Julien, Cajan, Julia, Gustafson, Kyle, Krier, Irina, Leleu, Marion, Molina, Nacho, Naldi, Aurelien, Sobel, Jonathan, Symul, Laura, Bounova, Gergana, and Jacquet, Philippe

Subjects
viruses, proliferation, expression, cancer, transcription, pol ii
Abstract

Mouse liver regeneration after partial hepatectomy involves cells in the remaining tissue synchronously entering the cell division cycle. We have used this system and H3K4me3, Pol II and Pol III profiling to characterize adaptations in Pol III transcription. Our results broadly define a class of genes close to H3K4me3 and Pol II peaks, whose Pol III occupancy is high and stable, and another class, distant from Pol II peaks, whose Pol III occupancy strongly increases after partial hepatectomy. Pol III regulation in the liver thus entails both highly expressed housekeeping genes and genes whose expression can adapt to increased demand.

27. Maboss 2.0: An Environment For Stochastic Boolean Modeling

Author

Stoll, Gautier, Caron, Barthelemy, Viara, Eric, Dugourd, Aurelien, Zinovyev, Andrei, Naldi, Aurelien, and Kroemer, Guido

Subjects
3. Good health
Abstract

Modeling of signaling pathways is an important step towards the understanding and the treatment of diseases such as cancers, HIV or auto-immune diseases. MaBoSS is a software that allows to simulate populations of cells and to model stochastically the intracellular mechanisms that are deregulated in diseases. MaBoSS provides an output of a Boolean model in the form of time-dependent probabilities, for all biological entities (genes, proteins, phenotypes, etc.) of the model.

28. MaBoSS 2.0: an environment for stochastic Boolean modeling

Author

Stoll, Gautier, Caron, Barthelemy, Viara, Eric, Dugourd, Aurelien, Zinovyev, Andrei, Naldi, Aurelien, and Kroemer, Guido

Subjects
3. Good health
Abstract

Modeling of signaling pathways is an important step towards the understanding and the treatment of diseases such as cancers, HIV or auto-immune diseases. MaBoSS is a software that allows to simulate populations of cells and to model stochastically the intracellular mechanisms that are deregulated in diseases. MaBoSS provides an output of a Boolean model in the form of time-dependent probabilities, for all biological entities (genes, proteins, phenotypes, etc.) of the model.

29. Dynamical Boolean Modeling of Immunogenic Cell Death.

Author

Checcoli A, Pol JG, Naldi A, Noel V, Barillot E, Kroemer G, Thieffry D, Calzone L, and Stoll G

Abstract

As opposed to the standard tolerogenic apoptosis, immunogenic cell death (ICD) constitutes a type of cellular demise that elicits an adaptive immune response. ICD has been characterized in malignant cells following cytotoxic interventions, such as chemotherapy or radiotherapy. Briefly, ICD of cancer cells releases some stress/danger signals that attract and activate dendritic cells (DCs). The latter can then engulf and cross-present tumor antigens to T lymphocytes, thus priming a cancer-specific immunity. This series of reactions works as a positive feedback loop where the antitumor immunity further improves the therapeutic efficacy by targeting cancer cells spared by the cytotoxic agent. However, not all chemotherapeutic drugs currently approved for cancer treatment are able to stimulate bona fide ICD: some commonly used agents, such as cisplatin or 5-fluorouracil, are unable to activate all features of ICD. Therefore, a better characterization of the process could help identify some gene or protein candidates to target pharmacologically and suggest combinations of drugs that would favor/increase antitumor immune response. To this end, we have built a mathematical model of the major cell types that intervene in ICD, namely cancer cells, DCs, CD8 + and CD4 + T cells. Our model not only integrates intracellular mechanisms within each individual cell entity, but also incorporates intercellular communications between them. The resulting cell population model recapitulates key features of the dynamics of ICD after an initial treatment, in particular the time-dependent size of the different cell types. The model is based on a discrete Boolean formalism and is simulated by means of a software tool, UPMaBoSS, which performs stochastic simulations with continuous time, considering the dynamics of the system at the cell population level with appropriate timing of events, and accounting for death and division of each cell type. With this model, the time scales of some of the processes involved in ICD, which are challenging to measure experimentally, have been predicted. In addition, our model analysis led to the identification of actionable targets for boosting ICD-induced antitumor response. All computational analyses and results are compiled in interactive notebooks which cover the presentation of the network structure, model simulations, and parameter sensitivity analyses., (Copyright © 2020 Checcoli, Pol, Naldi, Noel, Barillot, Kroemer, Thieffry, Calzone and Stoll.)

Published
2020
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