The CoLoMoTo Interactive Notebook

The CoLoMoTo Interactive Notebook relies on Docker and Jupyter technologies to provide a unified environment to edit, execute, share, and reproduce analyses of qualitative models of biological networks.

Quick usage guide

Without any installation

Visit mybinder.org/v2/gh/colomoto/colomoto-docker/mybinder/latest to launch the most recent CoLoMoTo Notebook environment without any installation thanks to Binder services. You can replace latest with a specific image tag.

Note that the computing resources are limited and the storage is not persistent.

With Python Helper Script

You need Docker and Python. We support GNU/Linux, macOS, and Windows.

sudo pip install -U colomoto-docker   # only once; you may have to use pip3
colomoto-docker

The container can be stopped by pressing Ctrl+C keys.

By default, the script will fetch the most recent colomoto/colomoto-docker tag. A specific tag can be specified using the -V option; or use -V same to use the most recently fetched image. For example:

colomoto-docker                 # uses the most recently fetched image
colomoto-docker -V latest       # fetches the latest published image
colomoto-docker -V 2018-05-29   # fetches a specific image

Warning: by default, the files within the Docker container are isolated from the running host computer, therefore files are deleted after stopping the container, except the files within the persistent directory.

To have access to the files of your current directory you can use the --bind option:

colomoto-docker --bind .

If you want to have the tutorial notebooks alongside your local files, you can do the following:

mkdir notebooks
colomoto-docker -v notebooks:local-notebooks

in the Jupyter browser, you will see a local-notebooks directory which is bound to your notebooks directory.

See

colomoto-docker --help

for other options.

Having issues? have a look at our Troubleshooting page, or open an issue.

Python-less usage

You need Docker.

First, pick an image version among colomoto/colomoto-docker tags. Fetch the image with

docker pull colomoto/colomoto-docker:TAG

The image can be ran using

docker run -it --rm -p 8888:8888 colomoto/colomoto-docker:TAG

then, open your browser and go to http://localhost:8888 for the Jupyter notebook web interface (note: when using Docker Toolbox, replace localhost with the result of docker-machine ip default command).

Available software tools with Python API

• ActoNet – Abduction-based control of fixed points of Boolean networks
  Python module actonet
• AEON.py – Symbolic analysis (attractors, reachability) of (partially specified) Boolean networks
  Python module biodivine_aeon
• bioLQM – Logical Qualitative Modelling toolkit
  Python module biolqm
• BNS – Identification of synchronous attractors
  Python module bns
• BooleanNet – Simulation of Boolean regulatory networks
  Python module boolean2
• boolSim – Attractors and reachable sets in synchronous and asynchronous Boolean networks
  Python module boolsim
• CABEAN – A Software Tool for the Control of Asynchronous Boolean Networks
  Python module cabean
• Caspo – Reasoning on the response of logical signaling networks with Answer Set Programming
  Python module caspo and caspo_control
• CaSQ – Convert static interaction maps into executable models
  Python module casq
• CellCollective – Model repository and knowledge base
  Python module cellcollective
• GINsim – Boolean and multi-valued network modelling
  Python module ginsim
• MaBoSS – Markovian Boolean Stochastic Simulator
  Python module maboss
• mpbn – Most Permissive Boolean Networks
  Python module mpbn
• NORDic – Network Oriented Repurposing of Drugs
  Python module NORDic
• NuSMV – Symbolic model-checker
  Python module nusmv
• Pint – Static analyzer for dynamics of Automata Networks
  Python module pypint
• PyBoolNet – Generation, modification and analysis of Boolean networks
  Python module PyBoolNet
• R-BoolNet – Analysis and reconstruction of Boolean networks dynamics
  Generic RPY2 python interface
• scBoolSeq – scRNA-Seq data binarisation and synthetic generation from Boolean dynamics
  Python module scboolseq
• pyStableMotifs – Target-control of Boolean networks
  Python module pystablemotifs

Tutorials and use cases

From the links below, you can download the notebook .ipynb files to upload and re-execute them in your running CoLoMoTo Jupyter web interface.

Extending the CoLoMoTo Docker

You can easily extend the CoLoMoTo Docker image to integrate your own tool and distribute it as its own Docker image, using a Dockerfile skeleton like this:

FROM colomoto/colomoto-docker:next

USER root
RUN <insert installation instructions>

USER user

You may want to replace next with any other suitable colomoto/colomoto-docker tag. You should also consider using a persistent tagging policy.

The Dockerfile can be built using the command

docker build -t your-docker-image:your-tag .

The script colomoto-docker can then be used to run your own Docker image:

colomoto-docker --image your-docker-image -V your-tag

Contribute

Contributions are very welcome, being for adding new software or improving the user experience.

Consider opening an issue on GitHub and reading the CONTRIBUTING guidelines.

Citation

Aurélien Naldi, Céline Hernandez, Nicolas Levy, Gautier Stoll, Pedro T Monteiro, Claudine Chaouiya, Tomáš Helikar, Andrei Zinovyev, 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 9:680, 2018. | doi: 10.3389/fphys.2018.00680

Contact

Questions? Bugs? Requests? Open an issue on GitHub.

You can also reach us at colomoto.org.

The CoLoMoTo Consortium – Consortium for Logical Models and Tools