Pint: A Static Analyzer for Transient Dynamics of Qualitative Networks with IPython Interface

Paulevé, Loı̈c

doi:10.1007/978-3-319-67471-1_20

Cited by 29 publications

(43 citation statements)

References 23 publications

(20 reference statements)

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“…In [12]: wt_sim.update_parameters(discrete_time=0, use_physrandgen=0, seed_pseudorandom=100, sample_count=50000, max_time=50, time_tick=0.1, thread_count=4, statdist_traj_count=100, statdist_cluster_threshold=0.9)…”

Section: Simulation Parametersmentioning

confidence: 99%

“…Therefore, one may want to formally verify whether the loss of reachable stable apoptosis state is total or not. First, we show how to use Pint [12] to predict combinations of mutations which are guaranteed to prevent the activation of apoptosis. Next, we use the software NuSMV [2] to evaluate formally the Notch++/p53-double mutant.…”

Section: Formal Analysis With Pint and Nusmvmentioning

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].…”

mentioning

confidence: 99%

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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

Preprint

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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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Section: Simulation Parametersmentioning

confidence: 99%

Section: Formal Analysis With Pint and Nusmvmentioning

confidence: 99%

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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

Preprint

Self Cite

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“…The increasing use of qualitative models to study biological systems led to the development of various software tools for the logical formalism [2,12,19,25,36] and related qualitative approaches [3,28,35]. Many software tools use their own file format for the definition of models, hindering the delineation of analysis workflows combining complementary software tools.…”

Section: Loading and Converting Logical Qualitative Modelsmentioning

confidence: 99%

“…BioLQM includes this implementation, using decision diagrams to manipulate stability conditions, and introduces an alternative implementation based on the clingo ASP solver [13], which tends to be slower for small models, but can scale better in some cases. Similar methods are also available in the GNA and Pint tools [3,28].…”

Section: Identification Of Attractorsmentioning

confidence: 99%

bioLQM: a java library for the manipulation and conversion of Logical Qualitative Models of biological networks

Naldi

2018

Preprint

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Here we introduce bioLQM, a new Java software toolkit for the conversion, modification, and analysis of Logical Qualitative Models of biological regulatory networks, aiming to foster the development of novel complementary tools by providing core modelling operations. Based on the definition of multi-valued logical models, it implements import and export facilities, notably for the recent SBML-qual exchange format, as well as for formats used by several popular tools, facilitating the design of workflows combining these tools. Model modifications enable the definition of various perturbations, as well as model reduction, easing the analysis of large models. Another modification enables the study of multi-valued models with tools limited to the Boolean case. Finally, bioLQM provides a framework for the development of novel analysis tools. The current version implements the usual updating modes for model simulation (notably synchronous, asynchronous, and random asynchronous), as well as some static analysis features for the identification of attractors. The bioLQM software can be integrated into analysis workflows through command line and scripting interfaces. As a Java library, it further provides core data structures to the GINsim and EpiLog interactive tools, which supply graphical interfaces and additional analysis methods for cellular and multi-cellular qualitative models.

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Boolean Networks and Their Dynamics: The Impact of Updates

Paulevé

Sené

2022

Systems Biology Modelling and Analysis

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Pint: A Static Analyzer for Transient Dynamics of Qualitative Networks with IPython Interface

Cited by 29 publications

References 23 publications

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

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

bioLQM: a java library for the manipulation and conversion of Logical Qualitative Models of biological networks

Boolean Networks and Their Dynamics: The Impact of Updates

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