The CoLoMoTo interactive notebook

Accessible and reproducible computational analyses for qualitative biological networks

Aurélien Naldi, Céline Hernandez, Nicolas Levy, Gautier Stoll, Pedro T. Monteiro, Claudine Chaouiya, Tomas Helikar, Andrei Zinovyev, Laurence Calzone, Sarah Cohen-Boulakia, Denis Thieffry, Loïc Paulevé

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Article number680
JournalFrontiers in Physiology
Volume9
Issue numberJUN
DOIs
StatePublished - Jun 19 2018

Fingerprint

Workflow
Biological Models
Learning Curve
Software
Boidae
Technology
Publications

Keywords

  • Boolean networks
  • Computational systems biology
  • Model analysis
  • Python programming language
  • Reproducibility

ASJC Scopus subject areas

  • Physiology
  • Physiology (medical)

Cite this

The CoLoMoTo interactive notebook : Accessible and reproducible computational analyses for qualitative biological networks. / Naldi, Aurélien; Hernandez, Céline; Levy, Nicolas; Stoll, Gautier; Monteiro, Pedro T.; Chaouiya, Claudine; Helikar, Tomas; Zinovyev, Andrei; Calzone, Laurence; Cohen-Boulakia, Sarah; Thieffry, Denis; Paulevé, Loïc.

In: Frontiers in Physiology, Vol. 9, No. JUN, 680, 19.06.2018.

Research output: Contribution to journalArticle

Naldi, A, Hernandez, C, Levy, N, Stoll, G, Monteiro, PT, Chaouiya, C, Helikar, T, Zinovyev, A, Calzone, L, Cohen-Boulakia, S, Thieffry, D & Paulevé, L 2018, 'The CoLoMoTo interactive notebook: Accessible and reproducible computational analyses for qualitative biological networks', Frontiers in Physiology, vol. 9, no. JUN, 680. https://doi.org/10.3389/fphys.2018.00680
Naldi, Aurélien ; Hernandez, Céline ; Levy, Nicolas ; Stoll, Gautier ; Monteiro, Pedro T. ; Chaouiya, Claudine ; Helikar, Tomas ; Zinovyev, Andrei ; Calzone, Laurence ; Cohen-Boulakia, Sarah ; Thieffry, Denis ; Paulevé, Loïc. / The CoLoMoTo interactive notebook : Accessible and reproducible computational analyses for qualitative biological networks. In: Frontiers in Physiology. 2018 ; Vol. 9, No. JUN.
@article{bd34e70094054fdfbdad6c793726f916,
title = "The CoLoMoTo interactive notebook: Accessible and reproducible computational analyses for qualitative biological networks",
abstract = "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.",
keywords = "Boolean networks, Computational systems biology, Model analysis, Python programming language, Reproducibility",
author = "Aur{\'e}lien Naldi and C{\'e}line Hernandez and Nicolas Levy and Gautier Stoll and Monteiro, {Pedro T.} and Claudine Chaouiya and Tomas Helikar and Andrei Zinovyev and Laurence Calzone and Sarah Cohen-Boulakia and Denis Thieffry and Lo{\"i}c Paulev{\'e}",
year = "2018",
month = "6",
day = "19",
doi = "10.3389/fphys.2018.00680",
language = "English (US)",
volume = "9",
journal = "Frontiers in Physiology",
issn = "1664-042X",
publisher = "Frontiers Research Foundation",
number = "JUN",

}

TY - JOUR

T1 - The CoLoMoTo interactive notebook

T2 - Accessible and reproducible computational analyses for qualitative biological networks

AU - Naldi, Aurélien

AU - Hernandez, Céline

AU - Levy, Nicolas

AU - Stoll, Gautier

AU - Monteiro, Pedro T.

AU - Chaouiya, Claudine

AU - Helikar, Tomas

AU - Zinovyev, Andrei

AU - Calzone, Laurence

AU - Cohen-Boulakia, Sarah

AU - Thieffry, Denis

AU - Paulevé, Loïc

PY - 2018/6/19

Y1 - 2018/6/19

N2 - 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.

AB - 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.

KW - Boolean networks

KW - Computational systems biology

KW - Model analysis

KW - Python programming language

KW - Reproducibility

UR - http://www.scopus.com/inward/record.url?scp=85048681681&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85048681681&partnerID=8YFLogxK

U2 - 10.3389/fphys.2018.00680

DO - 10.3389/fphys.2018.00680

M3 - Article

VL - 9

JO - Frontiers in Physiology

JF - Frontiers in Physiology

SN - 1664-042X

IS - JUN

M1 - 680

ER -