Memory, Parallelization improvements

• Improved memory management
• Logarithmic cumulator merging algorithm
• Bugfixes in cmaboss object structure
• Automatic generation of compiled binaries and libraries for linux, mac, windows.

Introducing SBML-qual compatibility

MaBoSS is now compatible with SBML-qual model files.

Usage: MaBoSS [--use-sbml-names] [-c|--config CONF_FILE] -o|--output OUTPUT BOOLEAN_NETWORK_FILE

Where :

• CONF_FILE is a classic CFG file.
• OUTPUT is the prefix of the output files.
• BOOLEAN_NETWORK_FILE is the network file, in BND, SBML-Qual or BNet format.
• --use-sbml-names indicate that MaBoSS should use SBML names for naming nodes instead of SBML ids.

To compile it with SBML-qual compatibility, install libsbml and then use :
make install SBML_COMPAT=1

Version of MaBoSS available on Conda is compatible, with a new dependency on libsbml.
Version of cmaboss available on Conda and PyPi is compatible.

Windows compatibility

• Cygwin & MingW64 compatibility
• Fix packaging
• Updated binaries
• Memory improvements for ensemble simulation

Fixing initial conditions in ensemble model simulations

v2.3.3

Fixing macosx conda package

Fix to generate cfg templates which are backward compatible

The generated templates (option -t) were only compatible with version > 2.3.0, this fix makes it compatible.

New Engines, Continuous integration for testing and deploying, More PRNGs, cmaboss

More portables PRNGs :

• Own implementation of rand48 and glibc rand, added Mersenne twister from

New engines :

• FinalStateSimulationEngine : Only export final state of the simulation
• StochasticSimulationEngine : Only simulate one trajectory, and return only final state

First version of the Python bindings using Python C API

• Simple simulations, with optional outputs
• MaBEstEngine and FinalStateSimulationEngine are available

New objects structure

• Different network, config can be loaded in memory, at the same time

Continuous integration for testing and deploying

• Testing classic and ensemble engine

• Testing server

• Testing with > 128 nodes

• Testing different PRNGs

• Testing server in Docker and Singularity

• Testing on linux and macosx

• Deploying server docker to DockerHub

• Deploying conda libraries

• Deploying cmaboss to pypi

Initial conditions are now a property of a network

Initial conditions are now a property of a network.
This allows to simulate an ensemble of model, where each model has its own initial conditions.