Naldi Aurélien, Hernandez Céline, Levy Nicolas, Stoll Gautier, Monteiro Pedro T, Chaouiya Claudine, Helikar Tomáš, Zinovyev Andrei, Calzone Laurence, Cohen-Boulakia Sarah, Thieffry Denis, Paulevé Loïc
Computational 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, France.
Laboratoire de Recherche en Informatique UMR8623, Université Paris-Sud, Centre National de la Recherche Scientifique, Université Paris-Saclay, Orsay, France.
Front Physiol. 2018 Jun 19;9:680. doi: 10.3389/fphys.2018.00680. eCollection 2018.
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.
分析生物网络模型通常依赖于工作流程,在这些流程中,具有敏感参数的不同软件工具被链接在一起,很多时候还需要额外的手动步骤。此类工作流程的可访问性和可重复性具有挑战性,这是因为出版物常常忽略分析细节,还因为其中一些工具可能难以安装,和/或学习曲线很陡。CoLoMoTo交互式笔记本提供了一个统一的环境,用于编辑、执行、共享和重现生物网络定性模型的分析。该框架结合了不同技术的力量,以确保可重复性并降低用户对这些技术的学习曲线。该框架以Docker镜像的形式分发,除了Docker之外无需任何安装步骤即可运行其中的工具,并且可在Linux、macOS和Microsoft Windows上使用。嵌入式计算工作流程通过Jupyter Web界面进行编辑,能够包含文本注释、要执行的显式代码以及结果的可视化。然后可以在同一环境中共享并重新执行生成的笔记本文件。到目前为止,CoLoMoTo交互式笔记本提供了对软件工具GINsim、BioLQM、Pint、MaBoSS和Cell Collective的访问,用于布尔网络和多值网络的建模与分析。未来还将纳入更多工具。我们为这些工具中的每一个都开发了一个Python接口,以便在Jupyter Web界面中实现无缝集成,并简化互补分析的链接。