MaBoSS 2.0: an environment for stochastic Boolean modeling

Stoll, Gautier; Caron, Barthélémy; Viara, Éric; Dugourd, Aurélien; Zinovyev, Andrei; Naldi, Aurélien; Kroemer, Guido; Barillot, Emmanuel; Calzone, Laurence

doi:10.1093/bioinformatics/btx123

Cited by 111 publications

(143 citation statements)

References 10 publications

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“…Taking advantage of the multiple export formats supported by GINsim, it is also possible to use complementary tools, including stochastic simulations software (e.g. MaBoSS, see Stoll et al [42]), model checking tools (e.g. NuSMV, see Abou-Jaoudé et al [1,3], Traynard et al [48], or yet various graph visualisation and analysis packages (see Note 5 for a list of export options).…”

Section: Discussionmentioning

confidence: 99%

Logical modelling and analysis of cellular regulatory networks with GINsim 3.0

Naldi

Hernandez

Abou-Jaoudé

et al. 2018

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The logical formalism is well adapted to model large cellular networks, for which detailed kinetic data are scarce. This tutorial focuses on this well-established qualitative framework. Relying on GINsim (release 3.0), a software implementing this formalism, we guide the reader step by step towards the definition, the analysis and the simulation of a four-node model of the mammalian p53-Mdm2 network.

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Section: Discussionmentioning

confidence: 99%

Logical modelling and analysis of cellular regulatory networks with GINsim 3.0

Naldi

Hernandez

Abou-Jaoudé

et al. 2018

Preprint

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“…This modeling framework does not require the knowledge of biochemical constants that are often unavailable for most cellular processes. An extension of asynchronously (stochastically) updated, discrete time logical models emerged in recent years (Stoll et al, 2017(Stoll et al, , 2012 using timescale parameters (transition rates) for the model's variables to generate continuous time Monte Carlo simulations. This approach produces continuous values (probabilities of activation) for a model's variables, enabling more quantitative analysis and comparison with continuous biological data.…”

Section: Resultsmentioning

confidence: 99%

“…In recent years, efforts were made to bridge the gap between qualitative and quantitative modeling by a continuous time stochastic version of Boolean modeling (Stoll et al, 2012(Stoll et al, , 2017. With this approach, the temporal evolution of a system is described as a continuous time Markov process on a Boolean state space.…”

Section: Introductionmentioning

confidence: 99%

Exact calculation of stationary solution and parameter sensitivity analysis of stochastic continuous time Boolean models

Koltai

Noël

Zinovyev

et al. 2019

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Motivation: Solutions to stochastic Boolean models are usually estimated by Monte Carlo simulations, but as the state space of these models can be enormous, there is an inherent uncertainty about the accuracy of Monte Carlo estimates and whether simulations have reached all asymptotic solutions. Moreover, these models have timescale parameters (transition rates) that the probability values of stationary solutions depend on in complex ways that have not been analyzed yet in the literature. These two fundamental uncertainties call for an exact calculation method for this class of models. Results: We show that the stationary probability values of the attractors of stochastic (asynchronous) continuous time Boolean models can be exactly calculated. The calculation does not require Monte Carlo simulations, instead it uses an exact matrix calculation method previously applied in the context of chemical kinetics. Using this approach, we also analyze the under-explored question of the effect of transition rates on the stationary solutions and show the latter can be sensitive to parameter changes. The analysis distinguishes processes that are robust or, alternatively, sensitive to parameter values, providing both methodological and biological insights.

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“…MaBoSS [13] is a C++ software that performs stochastic simulations of a Boolean network by translating it into a continuous time Markov processes. Each node activation and inactivation is associated with an up and a down rate, which specify the propensity of the corresponding transition.…”

Section: Assessing the Probabilities To Reach Alternative Attractors mentioning

confidence: 99%

“…To support the development and analysis of logical models, a series of software tools have been proposed, often with specific assets [7,9,12,13].…”

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Prediction of Mutations to Control Pathways Enabling Tumour Cell Invasion with the CoLoMoTo Interactive Notebook (Tutorial)

Levy

Naldi

Hernandez

et al. 2018

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Boolean and multi-valued logical formalisms are increasingly used to model complex cellular networks. To ease the development and analysis of logical models, a series of software tools have been proposed, often with specific assets. However, combining these tools typically implies a series of cumbersome software installation and model conversion steps. In this respect, the CoLoMoTo Interactive Notebook provides a joint distribution of several logical modelling software tools, along with an interactive web Python interface easing the chaining of complementary analyses. In this protocol, we demonstrate the assets of this approach through the analysis of a computational model of biological network. Our computational workflow combines (1) the importation of a GINsim model and its display, (2) its format conversion using the Java library BioLQM, (3) the formal prediction of mutations using the OCaml software Pint, (4) the model checking using the C++ software NuSMV, (5) quantitative stochastic simulations using the C++ software MaBoSS, and (6) the visualisation of results using the Python library matplotlib. Starting with a recent Boolean model of the signalling network controlling tumour cell invasion and migration, our model analysis culminates with the prediction of sets of mutations presumably involved in a metastatic phenotype.

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MaBoSS 2.0: an environment for stochastic Boolean modeling

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 111 publications

References 10 publications

Logical modelling and analysis of cellular regulatory networks with GINsim 3.0

Logical modelling and analysis of cellular regulatory networks with GINsim 3.0

Exact calculation of stationary solution and parameter sensitivity analysis of stochastic continuous time Boolean models

Prediction of Mutations to Control Pathways Enabling Tumour Cell Invasion with the CoLoMoTo Interactive Notebook (Tutorial)

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