The CoLoMo To Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks

被引:47
作者
Naldi, Aurelien [1 ]
Hernandez, Celine [1 ]
Levy, Nicolas [2 ,3 ]
Stoll, Gautier [4 ,5 ,6 ,7 ,8 ]
Monteiro, Pedro T. [9 ]
Chaouiya, Claudine [10 ]
Helikar, Tomas [11 ]
Zinovyev, Andrei [12 ,13 ,14 ,15 ]
Calzone, Laurence [12 ,13 ,14 ]
Cohen-Boulakia, Sarah [2 ]
Thieffry, Denis [1 ]
Pauleve, Loic [2 ]
机构
[1] PSL Univ, INSERM, Inst Biol,U1024,UMR8197, CNRS,Ecole Normale Super,Computat Syst Biol Team, Paris, France
[2] Univ Paris Saclay, Univ Paris Sud, CNRS, Lab Rech Informat,UMR8623, Orsay, France
[3] Ecole Normale Super Lyon, Lyon, France
[4] Univ Paris Descartes Paris V, Sorbonne Paris Cite, Paris, France
[5] Ctr Rech Cordeliers, Equipe Labellisee Ligue Natl Canc 11, Paris, France
[6] INSERM, U1138, Paris, France
[7] Univ Paris 06, Paris, France
[8] Gustave Roussy Canc, Metabol & Cell Biol Platforms, Villejuif, France
[9] Univ Lisbon, INESC ID Inst Super Tecn, Lisbon, Portugal
[10] Inst Gulbenkian Ciencias, Oeiras, Portugal
[11] Univ Nebraska, Dept Biochem, Lincoln, NE 68583 USA
[12] PSL Res Univ, Inst Curie, Paris, France
[13] INSERM, U900, Paris, France
[14] PSL Res Univ, CBIO Ctr Computat Biol, MINES ParisTech, Paris, France
[15] Lobachevsky Univ, Nizhnii Novgorod, Russia
来源
FRONTIERS IN PHYSIOLOGY | 2018年 / 9卷
基金
美国国家卫生研究院;
关键词
computational systems biology; reproducibility; model analysis; Boolean networks; !text type='Python']Python[!/text] programming language; REGULATORY NETWORKS; MODELS; REPRESENTATION; GENERATION; STANDARDS; PACKAGE; SBML;
D O I
10.3389/fphys.2018.00680
中图分类号
Q4 [生理学];
学科分类号
071003 ;
摘要
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 andmulti-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.
引用
收藏
页数:13
相关论文
共 56 条
  • [1] Logical Modeling and Dynamical Analysis of Cellular Networks
    Abou-Jaoude, Wassim
    Traynard, Pauline
    Monteiro, PedroT.
    Saez-Rodriguez, Julio
    Helikar, Tomas
    Thieffry, Denis
    Chaouiya, Claudine
    [J]. FRONTIERS IN GENETICS, 2016, 7
  • [2] Model checking to assess T-helper cell plasticity
    Abou-Jaoude, Wassim
    Monteiro, Pedro T.
    Naldi, Aurelien
    Grandclaudon, Maximilien
    Soumelis, Vassili
    Chaouiya, Claudine
    Thieffry, Denis
    [J]. FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, 2015, 2
  • [3] Boolean network simulations for life scientists
    Albert, Istvan
    Thakar, Juilee
    Li, Song
    Zhang, Ranran
    Albert, Reka
    [J]. SOURCE CODE FOR BIOLOGY AND MEDICINE, 2008, 3 (01):
  • [4] [Anonymous], 2014, AGU FALL M
  • [5] [Anonymous], 2009, P EV METH MACH LEARN
  • [6] Baker M, 2016, NATURE, V533, P452, DOI 10.1038/533452a
  • [7] Computational Modeling, Formal Analysis, and Tools for Systems Biology
    Bartocci, Ezio
    Lio, Pietro
    [J]. PLOS COMPUTATIONAL BIOLOGY, 2016, 12 (01)
  • [8] Validation of qualitative models of genetic regulatory networks by model checking:: analysis of the nutritional stress response in Escherichia coli
    Batt, G
    Ropers, D
    de Jong, H
    Geiselmann, J
    Mateescu, R
    Page, M
    Schneider, D
    [J]. BIOINFORMATICS, 2005, 21 : I19 - I28
  • [9] Reproducibility in Science Improving the Standard for Basic and Preclinical Research
    Begley, C. Glenn
    Ioannidis, John P. A.
    [J]. CIRCULATION RESEARCH, 2015, 116 (01) : 116 - 126
  • [10] Raise standards for preclinical cancer research
    Begley, C. Glenn
    Ellis, Lee M.
    [J]. NATURE, 2012, 483 (7391) : 531 - 533