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
To have access to the files of your current directory you can use the
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
for other options.
Having issues? have a look at our Troubleshooting page, or open an issue.
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
- AEON.py – Symbolic analysis (attractors, reachability) of (partially specified) Boolean networks
- bioLQM – Logical Qualitative Modelling toolkit
- BNS – Identification of synchronous attractors
- BooleanNet – Simulation of Boolean regulatory networks
- boolSim – Attractors and reachable sets in synchronous and asynchronous Boolean networks
- CABEAN – A Software Tool for the Control of Asynchronous Boolean Networks
- Caspo – Reasoning on the response of logical signaling networks with Answer Set Programming
- CaSQ – Convert static interaction maps into executable models
- CellCollective – Model repository and knowledge base
- GINsim – Boolean and multi-valued network modelling
- MaBoSS – Markovian Boolean Stochastic Simulator
- mpbn – Most Permissive Boolean Networks
- NuSMV – Symbolic model-checker
- Pint – Static analyzer for dynamics of Automata Networks
- PyBoolNet – Generation, modification and analysis of Boolean networks
- R-BoolNet – Analysis and reconstruction of Boolean networks dynamics
Generic RPY2 python interface
- pyStableMotifs – Target-control of Boolean networks