The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks
Analysing models of biological networks typically relies on workflows in which different software tools with sensitive parameters are chained together, many times with additional manual steps. The accessibility and reproducibility of such workflows is challenging, as publications often overlook anal...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-06-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fphys.2018.00680/full |
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DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Aurélien Naldi Céline Hernandez Nicolas Levy Nicolas Levy Gautier Stoll Gautier Stoll Gautier Stoll Gautier Stoll Gautier Stoll Pedro T. Monteiro Claudine Chaouiya Tomáš Helikar Andrei Zinovyev Andrei Zinovyev Andrei Zinovyev Andrei Zinovyev Laurence Calzone Laurence Calzone Laurence Calzone Sarah Cohen-Boulakia Denis Thieffry Loïc Paulevé |
| spellingShingle |
Aurélien Naldi Céline Hernandez Nicolas Levy Nicolas Levy Gautier Stoll Gautier Stoll Gautier Stoll Gautier Stoll Gautier Stoll Pedro T. Monteiro Claudine Chaouiya Tomáš Helikar Andrei Zinovyev Andrei Zinovyev Andrei Zinovyev Andrei Zinovyev Laurence Calzone Laurence Calzone Laurence Calzone Sarah Cohen-Boulakia Denis Thieffry Loïc Paulevé The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks Frontiers in Physiology computational systems biology reproducibility model analysis Boolean networks Python programming language |
| author_facet |
Aurélien Naldi Céline Hernandez Nicolas Levy Nicolas Levy Gautier Stoll Gautier Stoll Gautier Stoll Gautier Stoll Gautier Stoll Pedro T. Monteiro Claudine Chaouiya Tomáš Helikar Andrei Zinovyev Andrei Zinovyev Andrei Zinovyev Andrei Zinovyev Laurence Calzone Laurence Calzone Laurence Calzone Sarah Cohen-Boulakia Denis Thieffry Loïc Paulevé |
| author_sort |
Aurélien Naldi |
| title |
The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks |
| title_short |
The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks |
| title_full |
The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks |
| title_fullStr |
The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks |
| title_full_unstemmed |
The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks |
| title_sort |
colomoto interactive notebook: accessible and reproducible computational analyses for qualitative biological networks |
| publisher |
Frontiers Media S.A. |
| series |
Frontiers in Physiology |
| issn |
1664-042X |
| publishDate |
2018-06-01 |
| description |
Analysing models of biological networks typically relies on workflows in which different software tools with sensitive parameters are chained together, many times with additional manual steps. The accessibility and reproducibility of such workflows is challenging, as publications often overlook analysis details, and because some of these tools may be difficult to install, and/or have a steep learning curve. The CoLoMoTo Interactive Notebook provides a unified environment to edit, execute, share, and reproduce analyses of qualitative models of biological networks. This framework combines the power of different technologies to ensure repeatability and to reduce users' learning curve of these technologies. The framework is distributed as a Docker image with the tools ready to be run without any installation step besides Docker, and is available on Linux, macOS, and Microsoft Windows. The embedded computational workflows are edited with a Jupyter web interface, enabling the inclusion of textual annotations, along with the explicit code to execute, as well as the visualization of the results. The resulting notebook files can then be shared and re-executed in the same environment. To date, the CoLoMoTo Interactive Notebook provides access to the software tools GINsim, BioLQM, Pint, MaBoSS, and Cell Collective, for the modeling and analysis of Boolean and multi-valued networks. More tools will be included in the future. We developed a Python interface for each of these tools to offer a seamless integration in the Jupyter web interface and ease the chaining of complementary analyses. |
| topic |
computational systems biology reproducibility model analysis Boolean networks Python programming language |
| url |
https://www.frontiersin.org/article/10.3389/fphys.2018.00680/full |
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doaj-9a2d4dac80934cea9cb3c36fd1316bdf2020-11-25T00:29:51ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2018-06-01910.3389/fphys.2018.00680370901The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological NetworksAurélien Naldi0Céline Hernandez1Nicolas Levy2Nicolas Levy3Gautier Stoll4Gautier Stoll5Gautier Stoll6Gautier Stoll7Gautier Stoll8Pedro T. Monteiro9Claudine Chaouiya10Tomáš Helikar11Andrei Zinovyev12Andrei Zinovyev13Andrei Zinovyev14Andrei Zinovyev15Laurence Calzone16Laurence Calzone17Laurence Calzone18Sarah Cohen-Boulakia19Denis Thieffry20Loïc Paulevé21Computational Systems Biology Team, Institut de Biologie de I'Ecole Normale Supérieure, Centre National de la Recherche Scientifique UMR8197, Institut National de la Santé et de la Recherche Médicale U1024, École Normale Supérieure, PSL Université, Paris, FranceComputational Systems Biology Team, Institut de Biologie de I'Ecole Normale Supérieure, Centre National de la Recherche Scientifique UMR8197, Institut National de la Santé et de la Recherche Médicale U1024, École Normale Supérieure, PSL Université, Paris, FranceLaboratoire de Recherche en Informatique UMR8623, Université Paris-Sud, Centre National de la Recherche Scientifique, Université Paris-Saclay, Orsay, FranceÉcole Normale Supérieure de Lyon, Lyon, FranceUniversité Paris Descartes/Paris V, Sorbonne Paris Cité, Paris, FranceÉquipe 11 Labellisée Ligue Nationale Contre le Cancer, Centre de Recherche des Cordeliers, Paris, FranceInstitut National de la Santé et de la Recherche Médicale, U1138, Paris, FranceUniversité Pierre et Marie Curie, Paris, FranceMetabolomics and Cell Biology Platforms, Gustave Roussy Cancer, Villejuif, FranceINESC-ID/Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal0Instituto Gulbenkian de Ciência, Oeiras, Portugal1Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, United States2Institut Curie, PSL Research University, Paris, France3Institut National de la Santé et de la Recherche Médicale, U900, Paris, France4MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France5Lobachevsky University, Nizhni Novgorod, Russia2Institut Curie, PSL Research University, Paris, France3Institut National de la Santé et de la Recherche Médicale, U900, Paris, France4MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, FranceLaboratoire de Recherche en Informatique UMR8623, Université Paris-Sud, Centre National de la Recherche Scientifique, Université Paris-Saclay, Orsay, FranceComputational Systems Biology Team, Institut de Biologie de I'Ecole Normale Supérieure, Centre National de la Recherche Scientifique UMR8197, Institut National de la Santé et de la Recherche Médicale U1024, École Normale Supérieure, PSL Université, Paris, FranceLaboratoire de Recherche en Informatique UMR8623, Université Paris-Sud, Centre National de la Recherche Scientifique, Université Paris-Saclay, Orsay, FranceAnalysing models of biological networks typically relies on workflows in which different software tools with sensitive parameters are chained together, many times with additional manual steps. The accessibility and reproducibility of such workflows is challenging, as publications often overlook analysis details, and because some of these tools may be difficult to install, and/or have a steep learning curve. The CoLoMoTo Interactive Notebook provides a unified environment to edit, execute, share, and reproduce analyses of qualitative models of biological networks. This framework combines the power of different technologies to ensure repeatability and to reduce users' learning curve of these technologies. The framework is distributed as a Docker image with the tools ready to be run without any installation step besides Docker, and is available on Linux, macOS, and Microsoft Windows. The embedded computational workflows are edited with a Jupyter web interface, enabling the inclusion of textual annotations, along with the explicit code to execute, as well as the visualization of the results. The resulting notebook files can then be shared and re-executed in the same environment. To date, the CoLoMoTo Interactive Notebook provides access to the software tools GINsim, BioLQM, Pint, MaBoSS, and Cell Collective, for the modeling and analysis of Boolean and multi-valued networks. More tools will be included in the future. We developed a Python interface for each of these tools to offer a seamless integration in the Jupyter web interface and ease the chaining of complementary analyses.https://www.frontiersin.org/article/10.3389/fphys.2018.00680/fullcomputational systems biologyreproducibilitymodel analysisBoolean networksPython programming language |