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 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

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.


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

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.

Short tutorials for individual tools
Executable paper showing combination of GINsim, bioLQM, MaBoSS, and Pint (doi:10.3389/fphys.2018.00787) colomoto-docker:2018-05-29

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


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.


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


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

You can also reach us at

The CoLoMoTo Consortium – Consortium for Logical Models and Tools