Abstract
The software Pint is devoted to the scalable analysis of the traces of automata networks, which encompass Boolean and discrete networks. Pint implements formal approximations of transient reachability-related properties, including mutation prediction and model reduction.
Pint is distributed with command line tools, as well as a Python module pypint. The latter provides a seamless integration with the Jupyter IPython notebook web interface, which allows to easily save, reuse, reproduce, and share workflows of model analysis.
Pint can address networks with hundreds to thousands interacting components, which are typically intractable with standard approaches.
This work was supported by ANR-FNR project “AlgoReCell” (ANR-16-CE12-0034) and by CNRS PEPS INS2I 2017 project “FoRCe”.
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Abou-Jaoudé, W., Monteiro, P.T., Naldi, A., Grandclaudon, M., Soumelis, V., Chaouiya, C., Thieffry, D.: Model checking to assess t-helper cell plasticity. In: Front. Bioeng. Biotechnol. 2, January 2015
Antao, T.: Bioinformatics with Python cookbook. Packt Publishing Ltd., Birmingham (2015)
Baral, C.: Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, New York (2003)
Calzone, L., Fages, F., Soliman, S.: Biocham: an environment for modeling biological systems and formalizing experimental knowledge. Bioinformatics 22(14), 1805–1807 (2006)
Cimatti, A., Clarke, E., Giunchiglia, E., Giunchiglia, F., Pistore, M., Roveri, M., Sebastiani, R., Tacchella, A.: NuSMV 2: an opensource tool for symbolic model checking. In: Brinksma, E., Larsen, K.G. (eds.) CAV 2002. LNCS, vol. 2404, pp. 359–364. Springer, Heidelberg (2002). doi:10.1007/3-540-45657-0_29
Cock, P.J.A., Antao, T., Chang, J.T., Chapman, B.A., Cox, C.J., Dalke, A., Friedberg, I., Hamelryck, T., Kauff, F., Wilczynski, B., de Hoon, M.J.L.: Biopython: freely available python tools for computational molecular biology and bioinformatics. Bioinformatics 25(11), 1422–1423 (2009)
Fages, F., Martinez, T., Rosenblueth, D.A., Soliman, S.: Influence systems vs reaction systems. In: Bartocci, E., Lio, P., Paoletti, N. (eds.) CMSB 2016. LNCS, vol. 9859, pp. 98–115. Springer, Cham (2016). doi:10.1007/978-3-319-45177-0_7
Fitime, L.F., Roux, O., Guziolowski, C., Paulevé, L.: Identification of bifurcations in biological regulatory networks using answer-set programming. In: Constraint-Based Methods for Bioinformatics Workshop (2016)
Folschette, M., Paulevé, L., Magnin, M., Roux, O.: Sufficient conditions for reachability in automata networks with priorities. Theor. Comput. Sci. 608, 66–83 (2015). Part 1, From Computer Science to Biology and Back
Gonzalez, A.G., Naldi, A., Sánchez, L., Thieffry, D., Chaouiya, C.: Ginsim: A software suite for the qualitative modelling, simulation and analysis of regulatory networks. Biosystems 84(2), 91–100 (2006). Dynamical Modeling of Biological Regulatory Networks
Grieco, L., Calzone, L., Bernard-Pierrot, I., Radvanyi, F., Kahn-Perlès, B., Thieffry, D.: Integrative modelling of the influence of MAPK network on cancer cell fate decision. PLoS Comput. Biol. 9(10), e1003286 (2013)
Grunberg, R., Nilges, M., Leckner, J.: Biskit – a software platform for structural bioinformatics. Bioinformatics 23(6), 769–770 (2007)
Helikar, T., Kowal, B., McClenathan, S., Bruckner, M., Rowley, T., Madrahimov, A., Wicks, B., Shrestha, M., Limbu, K., Rogers, J.A.: The cell collective: toward an open and collaborative approach to systems biology. BMC Syst. Biol. 6(1), 96 (2012)
Klarner, H., Streck, A., Siebert, H.: PyBoolNet: a python package for the generation, analysis and visualization of boolean networks. Bioinformatics 33, 770–772 (2016)
Leprevost, F.V., et al.: Biocontainers: an open-source and community-driven framework for software standardization. Bioinform. (Oxford Engl.) 33, 2580–2582 (2017)
LIP6/Move. Its tools. http://ddd.lip6.fr/itstools.php
MacNamara, A., Terfve, C., Henriques, D., Bernabé, B.P., Saez-Rodriguez, J.: State-time spectrum of signal transduction logic models. Phys. Biol. 9(4), 45003 (2012)
Mussel, C., Hopfensitz, M., Kestler, H.A.: BoolNet - an R package for generation, reconstruction and analysis of boolean networks. Bioinformatics 26(10), 1378–1380 (2010)
Paulevé, L.: Goal-oriented reduction of automata networks. In: Bartocci, E., Lio, P., Paoletti, N. (eds.) CMSB 2016. LNCS, vol. 9859, pp. 252–272. Springer, Cham (2016). doi:10.1007/978-3-319-45177-0_16
Paulevé, L., Andrieux, G., Koeppl, H.: Under-approximating cut sets for reachability in large scale automata networks. In: Sharygina, N., Veith, H. (eds.) Computer Aided Verification. LNCS, vol. 8044, pp. 69–84. Springer, Heidelberg (2013)
Paulevé, L., Magnin, M., Roux, O.: Static analysis of biological regulatory networks dynamics using abstract interpretation. Math. Struct. Comput.Sci. 22(4), 651–685 (2012)
Rougny, A., Froidevaux, C., Calzone, L., Paulevé, L.: Qualitative dynamics semantics for SBGN process description. BMC Syst. Biol. 10(1), 1–24 (2016)
Samaga, R., Saez-Rodriguez, J., Alexopoulos, L.G., Sorger, P.K., Klamt, S.: The logic of EGFR/ERBB signaling: theoretical properties and analysis of high-throughput data. PLoS Comput. Biol. 5(8), e1000438 (2009)
Schoeberl, B., Eichler-Jonsson, C., Gilles, E.D., Müller, G.: Computational modeling of the dynamics of the map kinase cascade activated by surface and internalized egf receptors. Nature Biotechnol. 20(4), 370–375 (2002)
Schwoon, S.: Mole. http://www.lsv.ens-cachan.fr/~schwoon/tools/mole/
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Paulevé, L. (2017). Pint: A Static Analyzer for Transient Dynamics of Qualitative Networks with IPython Interface. In: Feret, J., Koeppl, H. (eds) Computational Methods in Systems Biology. CMSB 2017. Lecture Notes in Computer Science(), vol 10545. Springer, Cham. https://doi.org/10.1007/978-3-319-67471-1_20
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