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Pleione: statistical and multi-objective strategies to calibrate rule-based models

Project description

# pleione

Pleione is a python3 package that implement methods that are common to traditional modeling frameworks, and apply them to analyze Rule-Based Models.

Here you’ll find the necessary documentation to install and use the methods in Pleione. At the moment, Pleione parameterizes Rule-Based Models written either in BioNetGen (https://www.csb.pitt.edu/Faculty/Faeder/?page_id=409) or kappa language (https://www.kappalanguage.org/). Models are simulated with BNG2 (https://github.com/RuleWorld/bionetgen, PMID 27402907), NFsim (https://github.com/RuleWorld/nfsim, PMID 26556387), KaSim (https://github.com/Kappa-Dev/KaSim, PMID 29950016), or PISKaS (https://github.com/DLab/PISKaS, PMID 29175206). Please contact us or write an issue to include your favorite stochastic simulator to Pleione (https://github.com/glucksfall/pleione/issues).

Pleione implements a Genetic Algorithm with elitism, on the contrary to BioNetFit (https://github.com/RuleWorld/BioNetFit, PMID 26556387) that implements a parents selection within a distribution probability that is inverse to the rank. Nonetheless, Pleione’s methods to parameterize Rule-Based Models include both, a uniform or inverse to the rank probability to select models from within an elite or all models.

Examples to run Pleione are located in https://github.com/glucksfall/pleione/tree/master/example and in the python distribution wheel. The table with the U-test critical values is located at the same folder and in subfolders.

The plan to add methods into Pleiades (https://github.com/glucksfall/pleiades) includes a sensitivity analysis and a parameterization employing a Particle Swarm Optimization protocol. You could write us if you wish to add methods into pleione or aid in the development of them.

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