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.
Highlights
The scientific community has been increasingly concerned about difficulties in reproducing already published results
1International conference on Very Large Data Bases. 2ACM’s Special Interest Group on Management Of Data. 3http://co.mbine.org 4http://colomoto.org this manuscript, we report the phase of the CoLoMoTo efforts in the area of reproducibility in computational systems biology: The CoLoMoTo Interactive Notebook, which provides an easy-to-use environment to edit, execute, share, and reproduce analyses of qualitative models of biological networks by seamlessly integrating various logical modeling software tools
We have developed a Python interface for each of the tools embedded in the CoLoMoTo Docker image, which greatly ease the execution of different tool functionalities, fetch the results, and use these as input for other executions
Summary
The scientific community has been increasingly concerned about difficulties in reproducing already published results. Several initiatives have been launched by the community to address reproducibility issues for computational modeling of biochemical networks These include guidelines for model annotations (MIRIAM, Le Novère et al, 2005) and simulation descriptions (MIASE, Waltemath et al, 2011a), as well as standards for model exchange (SBML, Hucka et al, 2003) and simulation parametrizations (SED-ML, Waltemath et al, 2011b). They are involved in the development of novel computational methods and models This method article presents a collective effort to provide the community with a reproducibility-oriented framework combining software tools related to logical modeling. This framework combines the power of different approaches to ensure repeatability and to reduce the requirement of technical knowledge from users.
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