Search

Your search keyword '"Helikar, Tomas"' showing total 33 results
Start Over Author "Helikar, Tomas"
33 results on '"Helikar, Tomas"'

1. Forum on immune digital twins: a meeting report

Author

Laubenbacher, Reinhard, Adler, Fred, An, Gary, Castiglione, Filippo, Eubank, Stephen, Fonseca, Luis L., Glazier, James, Helikar, Tomas, Jett-Tilton, Marti, Kirschner, Denise, Macklin, Paul, Mehrad, Borna, Moore, Beth, Pasour, Virginia, Shmulevich, Ilya, Smith, Amber, Voigt, Isabel, Yankeelov, Thomas E., and Ziemssen, Tjalf

Subjects
Quantitative Biology - Other Quantitative Biology
Abstract

Medical digital twins are computational models of human biology relevant to a given medical condition, which can be tailored to an individual patient, thereby predicting the course of disease and individualized treatments, an important goal of personalized medicine. The immune system, which has a central role in many diseases, is highly heterogeneous between individuals, and thus poses a major challenge for this technology. If medical digital twins are to faithfully capture the characteristics of a patient's immune system, we need to answer many questions, such as: What do we need to know about the immune system to build mathematical models that reflect features of an individual? What data do we need to collect across the different scales of immune system action? What are the right modeling paradigms to properly capture immune system complexity? In February 2023, an international group of experts convened in Lake Nona, FL for two days to discuss these and other questions related to digital twins of the immune system. The group consisted of clinicians, immunologists, biologists, and mathematical modelers, representative of the interdisciplinary nature of medical digital twin development. A video recording of the entire event is available. This paper presents a synopsis of the discussions, brief descriptions of ongoing digital twin projects at different stages of progress. It also proposes a 5-year action plan for further developing this technology. The main recommendations are to identify and pursue a small number of promising use cases, to develop stimulation-specific assays of immune function in a clinical setting, and to develop a database of existing computational immune models, as well as advanced modeling technology and infrastructure.

Published
2023
Full Text
View/download PDF

2. Forum on immune digital twins: a meeting report

Author

Laubenbacher, Reinhard, Adler, Fred, An, Gary, Castiglione, Filippo, Eubank, Stephen, Fonseca, Luis L., Glazier, James, Helikar, Tomas, Jett-Tilton, Marti, Kirschner, Denise, Macklin, Paul, Mehrad, Borna, Moore, Beth, Pasour, Virginia, Shmulevich, Ilya, Smith, Amber, Voigt, Isabel, Yankeelov, Thomas E., and Ziemssen, Tjalf

Published
2024
Full Text
View/download PDF

3. COMO: A Pipeline for Multi-Omics Data Integration in Metabolic Modeling and Drug Discovery

Author

Bessell, Brandt, Loecker, Josh, Zhao, Zhongyuan, Aghamiri, Sara Sadat, Mohanty, Sabyasachi, Amin, Rada, Helikar, Tomáš, and Puniya, Bhanwar Lal

Subjects
Quantitative Biology - Molecular Networks
Abstract

Identifying potential drug targets using metabolic modeling requires integrating multiple modeling methods and heterogenous biological datasets, which can be challenging without sophisticated tools. We developed COMO, a user-friendly pipeline that integrates multi-omics data processing, context-specific metabolic model development, simulations, drug databases, and disease data to aid drug discovery. COMO can be installed as a Docker image and includes intuitive instructions within a Jupyter Lab environment. It provides a comprehensive solution for multi-omics integration of bulk and single-cell RNA-seq, microarrays, and proteomics to develop context-specific metabolic models. Using public databases, open-source solutions for model construction, and a streamlined approach for predicting repurposable drugs, COMO empowers researchers to investigate low-cost alternatives and novel disease treatments. As a case study, we used the pipeline to construct metabolic models of B cells, which simulate and analyze them to predict 25 and 23 metabolic drug targets for rheumatoid arthritis and systemic lupus erythematosus, respectively. COMO can be used to construct models for any cell or tissue type and identify drugs for any human disease. The pipeline has the potential to improve the health of the global community cost-effectively by providing high-confidence targets to pursue in preclinical and clinical studies.

Published
2020

5. Supporting University Student Learning of Complex Systems: An Example of Teaching the Interactive Processes That Constitute Photosynthesis

Author

Dauer, Joseph, Dauer, Jenny, Lucas, Lyrica, Helikar, Tomáš, Long, Tammy, Ergazaki, Marida, Series Editor, Kampourakis, Kostas, Series Editor, Grace, Marcus, Editorial Board Member, Knippels, Marie Christine, Editorial Board Member, Zabel, Jörg, Editorial Board Member, Korfiatis, Constantinos, Editorial Board Member, Jimenez Aleixandre, Maria Pilar, Editorial Board Member, Yarden, Anat, Editorial Board Member, Hammann, Marcus, Editorial Board Member, Harms, Ute, Editorial Board Member, Reiss, Michael, Editorial Board Member, Gericke, Niklas, Editorial Board Member, El-Hani, Charbel Nino, Editorial Board Member, Dawson, Vaille, Editorial Board Member, Nehm, Ross, Editorial Board Member, McComas, William, Editorial Board Member, Passmore, Cynthia, Editorial Board Member, Ben Zvi Assaraf, Orit, editor, and Knippels, Marie-Christine P. J., editor

Published
2022
Full Text
View/download PDF

7. Toward mechanistic medical digital twins: some use cases in immunology

Author

Laubenbacher, Reinhard, primary, Adler, Fred, additional, An, Gary, additional, Castiglione, Filippo, additional, Eubank, Stephen, additional, Fonseca, Luis L., additional, Glazier, James, additional, Helikar, Tomas, additional, Jett-Tilton, Marti, additional, Kirschner, Denise, additional, Macklin, Paul, additional, Mehrad, Borna, additional, Moore, Beth, additional, Pasour, Virginia, additional, Shmulevich, Ilya, additional, Smith, Amber, additional, Voigt, Isabel, additional, Yankeelov, Thomas E., additional, and Ziemssen, Tjalf, additional

Published
2024
Full Text
View/download PDF

8. Systems Perturbation Analysis of a Large Scale Signal Transduction Model Reveals Potentially Influential Candidates for Cancer Therapeutics

Author

Puniya, Bhanwar Lal, Allen, Laura, Hochfelder, Colleen, Majumder, Mahbubul, and Helikar, Tomáš

Subjects
Quantitative Biology - Molecular Networks
Abstract

Dysregulation in signal transduction pathways can lead to a variety of complex disorders, including cancer. Computational approaches such as network analysis are important tools to understand system dynamics as well as to identify critical components that could be further explored as therapeutic targets. Here, we performed perturbation analysis of a large-scale signal transduction model in extracellular environments that stimulate cell death, growth, motility, and quiescence. Each of the model's components was perturbed under both loss-of-function and gain-of-function mutations. We identified the most and least influential components based on the magnitude of their influence on the rest of the system. Based on the premise that the most influential components might serve as better drug targets, we characterized them for biological functions, housekeeping genes, essential genes, and druggable proteins. Moreover, known cancer drug targets were also classified in influential components based on the affected components in the network. Additionally, the systemic perturbation analysis of the model revealed a network motif of most influential components which affect each other. Furthermore, our analysis predicted novel combinations of cancer drug targets with various effects on other most influential components. We found that the combinatorial perturbation consisting of PI3K inactivation and overactivation of IP3R1 can lead to increased activity levels of apoptosis-related components and tumor suppressor genes, suggesting that this combinatorial perturbation may lead to a better target for decreasing cell proliferation and inducing apoptosis. Lastly, our results suggest that systematic perturbation analyses of large-scale computational models may serve as an approach to prioritize and assess signal transduction components in order to identify novel drug targets in complex disorders., Comment: 24 pages, 9 Figures

Published
2015

10. 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

14. 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

16. 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

20. 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
View/download PDF

21. 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

23. Changes in students’ mental models from computational modeling of gene regulatory networks

Author

Dauer, Jenny, Bergan-Roller, Heather E., King, Gretchen P., Kjose, McKenzie, Galt, Nicholas, Helikar, Tomas, Dauer, Jenny, Bergan-Roller, Heather E., King, Gretchen P., Kjose, McKenzie, Galt, Nicholas, and Helikar, Tomas

Abstract

Background: Computational modeling is an increasingly common practice for disciplinary experts and therefore necessitates integration into science curricula. Computational models afford an opportunity for students to investigate the dynamics of biological systems, but there is significant gap in our knowledge of how these activities impact student knowledge of the structures, relationships, and dynamics of the system. We investigated how a computational modeling activity affected introductory biology students’ mental models of a prokaryotic gene regulatory system (lac operon) by analyzing conceptual models created before and after the activity. Results: Students’ pre-lesson conceptual models consisted of provided, system-general structures (e.g., activator, repressor) connected with predominantly incorrect relationships, representing an incomplete mental model of gene regulation. Students’ post-lesson conceptual models included more context-specific structures (e.g., cAMP, lac repressor) and increased in total number of structures and relationships. Student conceptual models also included higher quality relationships among structures, indicating they learned about these context-specific structures through integration with their expanding mental model rather than in isolation. Conclusions: Student mental models meshed structures in a manner indicative of knowledge accretion while they were productively re-constructing their understanding of gene regulation. Conceptual models can inform instructors about how students are relating system structures and whether students are developing more sophisticated models of system-general and system-specific dynamics.

Published
2020

24. 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

26. Two heads are better than one: current landscape of integrating QSP and machine learning: An ISoP QSP SIG white paper by the working group on the integration of quantitative systems pharmacology and machine learning.

Author

Zhang, Tongli, Androulakis, Ioannis P., Bonate, Peter, Cheng, Limei, Helikar, Tomáš, Parikh, Jaimit, Rackauckas, Christopher, Subramanian, Kalyanasundaram, Cho, Carolyn R., on behalf of the Working Group, Borisov, Ivan, Broderick, Gordon, Damian, Valeriu, Dariolli, Rafael, Demin, Oleg, Ellinwood, Nicholas, Fey, Dirk, Gulati, Abhishek, Helikar, Tomas, and Jordie, Eric

Abstract

Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer 'omics' data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP + ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

28. Cell Collective

Author

Galt, Nick and Helikar, Tomas

Subjects
ComputingMilieux_COMPUTERSANDEDUCATION
Abstract

Session presentation on an easy-to-use software, that makes this computational modeling approach accessible to any student and instructor

Published
2018
Full Text
View/download PDF

30. Cooperative development of logical modelling standards and tools with CoLoMoTo

Author

Naldi, Aurélien, Monteiro, Pedro T., Müssel, Christoph, Kestler, Hans A., Thieffry, Denis, Xenarios, Ioannis, Saez-Rodriguez, Julio, Helikar, Tomas, Chaouiya, Claudine, Naldi, Aurélien, Monteiro, Pedro T., Müssel, Christoph, Kestler, Hans A., Thieffry, Denis, Xenarios, Ioannis, Saez-Rodriguez, Julio, Helikar, Tomas, and Chaouiya, Claudine

Abstract

The identification of large regulatory and signalling networks involved in the control of crucial cellular processes calls for proper modelling approaches. Indeed, models can help elucidate properties of these networks, understand their behaviour and provide (testable) predictions by performing in silico experiments. In this context, qualitative, logical frameworks have emerged as relevant approaches, as demonstrated by a growing number of published models, along with new methodologies and software tools. This productive activity now requires a concerted effort to ensure model reusability and interoperability between tools. Following an outline of the logical modelling framework, we present the most important achievements of the Consortium for Logical Models and Tools, along with future objectives. Our aim is to advertise this open community, which welcomes contributions from all researchers interested in logical modelling or in related mathematical and computational developments. Contact: contact@colomoto.org

Published
2017

31. Integrating Interactive Computational Modeling in Biology Curricula

Author

Helikar, Tomas and Cutucache, Christine E.

Abstract

While the use of computer tools to simulate complex processes such as computer circuits is normal practice in fields like engineering, the majority of life sciences/biological sciences courses continue to rely on the traditional textbook and memorization approach. To address this issue, we explored the use of the Cell Collective platform as a novel, interactive, and evolving pedagogical tool to foster student engagement, creativity, and higher-level thinking. Cell Collective is a Web-based platform used to create and simulate dynamical models of various biological processes. Students can create models of cells, diseases, or pathways themselves or explore existing models. This technology was implemented in both undergraduate and graduate courses as a pilot study to determine the feasibility of such software at the university level. First, a new (In Silico Biology) class was developed to enable students to learn biology by ‰ÛÏbuilding and breaking it‰Û? via computer models and their simulations. This class and technology also provide a non-intimidating way to incorporate mathematical and computational concepts into a class with students who have a limited mathematical . Second, we used the technology to mediate the use of simulations and modeling modules as a learning tool for traditional biological concepts, such as T cell differentiation or cell cycle regulation, in existing biology courses. Results of this pilot application suggest that there is promise in the use of computational modeling and software tools such as Cell Collective to provide new teaching methods in biology and contribute to the implementation of the ‰ÛÏVision and Change‰Û? call to action in undergraduate biology education by providing a hands-on approach to biology.

Published
2015

32. Cooperative development of logical modelling standards and tools with CoLoMoTo.

Author

Naldi, Aurélien, Monteiro, Pedro T., Müssel, Christoph, Kestler, Hans A., Thieffry, Denis, Xenarios, Ioannis, Saez-Rodriguez, Julio, Helikar, Tomas, and Chaouiya, Claudine

Abstract

The identification of large regulatory and signalling networks involved in the control of crucial cellular processes calls for proper modelling approaches. Indeed, models can help elucidate properties of these networks, understand their behaviour and provide (testable) predictions by performing in silico experiments. In this context, qualitative, logical frameworks have emerged as relevant approaches, as demonstrated by a growing number of published models, along with new methodologies and software tools. This productive activity now requires a concerted effort to ensure model reusability and interoperability between tools. Following an outline of the logical modelling framework, we present the most important achievements of the Consortium for Logical Models and Tools, along with future objectives. Our aim is to advertise this open community, which welcomes contributions from all researchers interested in logical modelling or in related mathematical and computational developments. [ABSTRACT FROM AUTHOR]

Published
2015
Full Text
View/download PDF

33. Cooperative development of logical modelling standards and tools with CoLoMoTo

Author

Naldi, Aurélien, Monteiro, Pedro T., Müssel, Christoph, Kestler, Hans A., Thieffry, Denis, Xenarios, Ioannis, Saez-Rodriguez, Julio, Helikar, Tomas, Chaouiya, Claudine, Naldi, Aurélien, Monteiro, Pedro T., Müssel, Christoph, Kestler, Hans A., Thieffry, Denis, Xenarios, Ioannis, Saez-Rodriguez, Julio, Helikar, Tomas, and Chaouiya, Claudine

Abstract

The identification of large regulatory and signalling networks involved in the control of crucial cellular processes calls for proper modelling approaches. Indeed, models can help elucidate properties of these networks, understand their behaviour and provide (testable) predictions by performing in silico experiments. In this context, qualitative, logical frameworks have emerged as relevant approaches, as demonstrated by a growing number of published models, along with new methodologies and software tools. This productive activity now requires a concerted effort to ensure model reusability and interoperability between tools. Following an outline of the logical modelling framework, we present the most important achievements of the Consortium for Logical Models and Tools, along with future objectives. Our aim is to advertise this open community, which welcomes contributions from all researchers interested in logical modelling or in related mathematical and computational developments. Contact: contact@colomoto.org

Catalog

Books, media, physical & digital resources
See catalog results