sysbiocurie/maboss repository overview

⁠MaBoSS : Markovian Boolean Stochastic Simulator

MaBoSS is a C++ software for simulating continuous/discrete time Markov processes, applied on a Boolean network.

MaBoSS uses a specific language for associating transition rates to each node. Given some initial conditions, MaBoSS applies Monte-Carlo kinetic algorithm (or Gillespie algorithm) to the network to produce time trajectories. Time evolution of probabilities are estimated. In addition, global and semi-global characterizations of the whole system are computed.

⁠References

Stoll, G., Viara, E., Barillot, E., & Calzone, L. (2012). Continuous time Boolean modeling for biological signaling: application of Gillespie algorithm. BMC systems biology, 6(1), 1-18. DOI : 10.1186/1752-0509-6-116⁠

Stoll, G., Caron, B., Viara, E., Dugourd, A., Zinovyev, A., Naldi, A., ... & Calzone, L. (2017). MaBoSS 2.0: an environment for stochastic Boolean modeling. Bioinformatics, 33(14), 2226-2228. DOI : 10.1093/bioinformatics/btx123⁠

⁠Tutorials

The directory tutorial contains two tutorials:

• MaBoSS 2.0 Tutorial⁠, describing MaBoSS environment tools usage on model describing DNA damage effects on p53 pathway.
• MaBoSS 2.5.0 Tutorial⁠, describing usage of MaBoSS command line, pyMaBoSS (python bindings) and WebMaBoSS (web interface) on a prostate cancer model.

⁠Conda repository

MaBoSS is available as a conda package for Linux and MacOSX in the CoLoMoTo repository⁠.

To install it, run

conda install -c colomoto maboss

⁠Python bindings

MaBoSS is accessible via pyMaBoSS, its python bindings, which are available on the GitHub of the CoLoMoTo organisation⁠.

⁠Web interface

MaBoSS can also be used via WebMaBoSS, a web interface, at https://maboss.curie.fr/webmaboss/⁠.

⁠Package Contents

MaBoSS is composed of:

• MaBoSS engine 2.5.5: C++ core program simulating continuous/discrete time Markov processes, applied on a Boolean network.
• MaBoSS tools 2.0: perl and python scripts using MaBoSS engine 2.0
• cMaBoSS 1.0.0b20: python bindings using Python C API.
• PopMaBoSS engine 0.0.1: Simulating continuous/discrete time Markov processes, applied on a Population of Boolean network state.

⁠MaBoSS Engine Usage

cd engine/pub

./MaBoSS --version
MaBoSS version 2.5.5 [networks up to 64 nodes]

./MaBoSS_100n --version
MaBoSS version 2.5.5 [networks up to 100 nodes]

The usage is described in the reference card⁠.

To use the engine compiled with MPI capability :

mpirun -np 2 ./MaBoSS.MPI --version

⁠PopMaBoSS Engine usage

cd engine/pub

./PopMaBoSS --version
PopMaBoSS version 0.0.1 [networks up to 64 nodes]

This is a simple check which returns the version of the engine

./PopMaBoSS -c ../examples/popmaboss/Toy.cfg ../examples/popmaboss/Toy.pbnd -o res

This will simulate a toy example available in the engine/example/popmaboss directory, and generate results files :

• res_fp.csv : list of fixed points of the boolean network encountered during the simulation
• res_pop_probtraj.csv : Population states probability distribution trajectories (as with MaBoSS, but this time on populations of boolean states)
• res_simple_pop_probtraj.csv : Simplified population output, with an average population size for every boolean state.

⁠Contact

Institut Curie

26 rue d'Ulm 75248 PARIS CEDEX 05

Contact: [email protected]⁠

Web Site: https://maboss.curie.fr⁠

⁠Copyright

BSD 3-Clause License (see https://opensource.org/licenses/BSD-3-Clause⁠)

Copyright (c) 2011-2022 Institut Curie, 26 rue d'Ulm, Paris, France
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright
   notice, this list of conditions and the following disclaimer in the
   documentation and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its
   contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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⁠Acknowledgements

The development was partially supported by European Union's Horizon 2020 Programme under agreement no. 951773 (PerMedCoE project).