Software development

"La simplicité est la sophistication suprême." Léonard de Vinci

  • Biocham the Biochemical Abstract Machine for modeling, analyzing and synthesizing chemical reaction networks (CRN) in both perspectives of systems biology and synthetic biology. Biocham 4 is a complete rewriting of Biocham 3 for easier maintenance.
© Inria / Photo C. Morel

Biocham-4 includes

  • new features for
    • compiling mathematical functions or programs in chemical reaction networks (CRN)
    • influence systems with forces
  • in addition to Biocham features for
    • differential simulation, stochastic simulation, Boolean model-checking and Boolean attractors
    • specifying semi-qualitative semi-quantitative behaviors in quantitative temporal logic
    • verifying such behaviors, estimating parameter sensitivity and robustness
    • searching and optimizing parameter values in high dimension
    • detecting model reductions by subgraph epimorphisms
    • various static analyses: dimension analysis, multi-stability, tropicalisation ...

    • Laurence Calzone, François Fages, Sylvain Soliman. BIOCHAM: An Environment for Modeling Biological Systems and Formalizing Experimental Knowledge. Bioinformatics, 22(14):1805–1807, 2006. [ preprint ]

    • François Fages, Sylvain Soliman. Formal Cell Biology in BIOCHAM. In 8th Int. School on Formal Methods for the Design of Computer, Communication and Software Systems: Computational Systems Biology SFM'08, pages 54–80, volume 5016 of LNCS. Springer-Verlag, 2008. [ preprint ]

No longer maintained

  • BIOCHAM 3.7 is a previous version of the Biochemical Abstract Machine software for modelilng and analysing biochemical reaction networks. BIOCHAM-web allows you to try it on-line through a web service. Beyond making simulations of different kinds, BIOCHAM provides unique features for
    • importing and exporting biochemical reaction networks (CRNs) s in different formalisms and formats,
    • performing various static analyses of CRNs,
    • specifying behaviors in (quantitative) temporal logic,
    • verifying tempoal properties by model-checking, and estimating parameter sensitivity and robustness,
    • searching parameter values under temporal logic constraints.
  • FO-CTL is a constraint solver for full First-Order Computation Tree Logic with linear arithmetic over the reals, FO-CTL(ℝlin), in Constrained Transition Systems.

  • ClpZinc a Horn clause front-end for the MiniZInc modelling language for expressing search strategies by constraints

  • MiniZinc-CMAES a stochastic optimization backend for the MiniZinc modelling language

  • Rules2CP a general purpose rule-based modelling language for constraint solving.


  • CLPGUI a generic graphical user interface for constraint logic programming.

Public data, models, notebooks and benchmarks related to publications.

TCS 23
Companion notebook to upload on BIOCHAM Jupyter notebook (alternatively see result in pdf,html or upload Biocham bc files)

Thibault Greugny, Eléa, Fages, François and Radulescu, Ovidiu, Szmolyan, Peter, Stamatas, Georgios. A Skin Microbiome Model with AMP interactions and Analysis of Quasi-Stability vs Stability in Population Dynamics. Theoretical Computer Science, 983:114294, 2023. [ preprint ]

CMSB 2022
Companion notebook to upload on BIOCHAM Jupyter notebook (alternatively see result in html or upload biocham bc files)

Mathieu Hemery, François Fages. Algebraic Biochemistry: a Framework for Analog Online Computation in Cells. In CMSB'22: Proceedings of the twentieth international conference on Computational Methods in Systems Biology, volume 13447 of LNCS. Springer-Verlag, 2022. [ preprint ] [ slides ] [ video ]

CMSB 2022b
Companion notebook to upload on BIOCHAM Jupyter notebook (alternatively see result in pdf,html or upload Biocham bc files)

Eléa Thibault Greugny, Georgios N Stamatas and François Fages. Stability versus Meta-stability in a Skin Microbiome Model. In CMSB'22: Proceedings of the twentieth international conference on Computational Methods in Systems Biology, volume 13447 of LNCS. Springer-Verlag, 2022. [ preprint ] [ slides ]

CMSB 2021
Companion notebook to save as local file and upload on BIOCHAM Jupyter notebook with v4.5.14 kernel

Mathieu Hemery, François Fages, Sylvain Soliman. Compiling Elementary Mathematical Functions into Finite Chemical Reaction Networks via a Polynomialization Algorithm for ODEs. In CMSB'21: Proceedings of the nineteenth international conference on Computational Methods in Systems Biology, volume 12881 of LNCS. Springer-Verlag, 2021. [ preprint ]

CMSB 2020a
Benchmark (zip file) and BIOCHAM notebooks for minimizing either number of species or the number of reactions

Mathieu Hemery, François Fages, Sylvain Soliman. On the Complexity of Quadratization for Polynomial Differential Equations. In CMSB'20: Proceedings of the eighteenth international conference on Computational Methods in Systems Biology, LNCS. Springer-Verlag, 2020. [ preprint ]

CMSB 2020b
Benchmark and Examples as a BIOCHAM notebook

Elisabeth Degrand, François Fages, Sylvain Soliman. Graphical Conditions for Rate Independence in Chemical Reaction Networks. In CMSB'20: Proceedings of the eighteenth international conference on Computational Methods in Systems Biology, volume 12314 of LNCS. Springer-Verlag, 2020. [ preprint ] [ slides ] [ video ]

ICML workshop CB 2019
source code
used for the implementation of

Julien Martinelli, Jeremy Grignard, Sylvain Soliman, François Fages. A Statistical Unsupervised Learning Algorithm for Inferring Reaction Networks from Time Series Data. In ICML Workshop on Computational Biology, 2019. [ preprint ]

CMSB 2019a
source code
used for the implementation of

Julien Martinelli, Jeremy Grignard, Sylvain Soliman, François Fages. On Inferring Reactions from Data Time Series by a Statistical Learning Greedy Heuristics. In CMSB'19: Proceedings of the seventeenth international conference on Computational Methods in Systems Biology, volume 11773 of LNCS. Springer-Verlag, 2019. [ preprint ]

CMSB 2019b
Biocham-4 notebook of examples of evolved CRNs
to upload on http://lifeware.inria.fr/biocham4/online/

Elisabeth Degrand, Mathieu Hemery, François Fages. On Chemical Reaction Network Design by a Nested Evolution Algorithm. In CMSB'19: Proceedings of the seventeenth international conference on Computational Methods in Systems Biology, volume 11773 of LNCS. Springer-Verlag, 2019. [ preprint ]

JTB 2018
source code
used for the implementation of

Adrien Baudier, François Fages, Sylvain Soliman. Graphical Requirements for Multistationarity in Reaction Networks and their Verification in BioModels. Journal of Theoretical Biology, 459:79–89, 2018. [ preprint ]

CMSB 2018
companion BIOCHAM-4 notebooks online

Fages, François, Soliman, Sylvain. On Robustness Computation and Optimization in BIOCHAM-4. In CMSB'18: Proceedings of the sixteenth international conference on Computational Methods in Systems Biology, LNCS. Springer-Verlag, 2018. [ preprint ]

CMSB 2017a
companion BIOCHAM-4 notebooks online (zip file of models and notebooks for Biocham v4.0)

Fages, François, Le Guludec, Guillaume and Bournez, Olivier, Pouly, Amaury. Strong Turing Completeness of Continuous Chemical Reaction Networks and Compilation of Mixed Analog-Digital Programs. In CMSB'17: Proceedings of the fiveteen international conference on Computational Methods in Systems Biology, pages 108–127, volume 10545 of LNCS. Springer-Verlag, 2017. [ preprint ]

CMSB 2017b
companion BIOCHAM-4 notebooks online (original Python code and examples implementing trace generation and PAC learning, the corresponding biocham commands can also be used directly in Biocham v4)

Carcano, Arthur, Fages, François, Soliman, Sylvain. Probably Approximately Correct Learning of Regulatory Networks from Time-Series Data. In CMSB'17: Proceedings of the fifteenth international conference on Computational Methods in Systems Biology, pages 74–90, volume 10545 of , 2017. [ preprint ]

TCBB 2018
Models in Biocham v4.0
companion notebooks Lotka-Volterra.ipynb, Circadian-Clock.ipynb, p53-Mdm2.ipynb and MAPK.ipynb for Biocham v4.0

François Fages, Thierry Martinez, David Rosenblueth, Sylvain Soliman. Influence Networks compared with Reaction Networks: Semantics, Expressivity and Attractors. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2018. [ preprint ]

Biosystems 2016
Files containing models and behavior specification in quantitative temporal logic with BIOCHAM 3.7.5commands for parameter sensitivity and search

Pauline Traynard, Céline Feillet, Sylvain Soliman, Franck Delaunay, François Fages. Model-based Investigation of the Circadian Clock and Cell Cycle Coupling in Mouse Embryonic Fibroblasts: Prediction of RevErb-alpha Up-Regulation during Mitosis. Biosystems, 149:59–69, 2016. [ preprint ]

TCS 2015, CMSB 2012
Automatic rewriting in SBML of the curated part of biomodels.net from their ODE semantics, and associated graphs.

François Fages, Steven Gay, Sylvain Soliman. Inferring Reaction Systems from Ordinary Differential Equations. Theoretical Computer Science, 599:64–78, 2015. [ preprint ]

Constraints 2015, CP 2012
source code.

Faten Nabli, François Fages, Thierry Martinez, Sylvain Soliman. A Boolean Model for Enumerating Minimal Siphons and Traps in Petri-nets. In Proceedings of CP'2012, 18th International Conference on Principles and Practice of Constraint Programming, pages 798–814, volume 7514 of LNCS. Springer-Verlag, 2012. [ preprint ] [ slides ] [ poster ]

No longer maintained:

  • CellStar image tracker as part of the Yeast Image Toolkit a collective effort for automatic tracking of yeast cells in time-lapse microscopy.

  • STSE Spatio-Temporal Simulation environment: a platform facilitating spatial, microscopy-based simulation in biology

  • CHRat a modular version of Constraint Handling Rules with ask and tell

  • Rules2CP a modelling language for constraint programming

  • CLPGUI is a graphical user interface for constraint logic programming.

  • GNU Prolog RH is a version of GNU Prolog extended with attributed variables, coroutines and CLP(R) constraints.

  • TCLP is a type checker for constraint logic programming in Prolog.