PEITH(Θ): perfecting experiments with information theory in Python with GPU support

Abstract Motivation Different experiments provide differing levels of information about a biological system. This makes it difficult, a priori, to select one of them beyond mere speculation and/or belief, especially when resources are limited. With the increasing diversity of experimental approaches...

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Bibliographic Details
Published in:Bioinformatics 2018-04, Vol.34 (7), p.1249-1250
Main Authors: Dony, Leander, Mackerodt, Jonas, Ward, Scott, Filippi, Sarah, Stumpf, Michael P H, Liepe, Juliane
Format: Article
Language:English
Subjects:
Applications Notes
Bayes Theorem
Information Theory
Software
Systems Biology - methods
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Summary:Abstract Motivation Different experiments provide differing levels of information about a biological system. This makes it difficult, a priori, to select one of them beyond mere speculation and/or belief, especially when resources are limited. With the increasing diversity of experimental approaches and general advances in quantitative systems biology, methods that inform us about the information content that a given experiment carries about the question we want to answer, become crucial. Results PEITH(Θ) is a general purpose, Python framework for experimental design in systems biology. PEITH(Θ) uses Bayesian inference and information theory in order to derive which experiments are most informative in order to estimate all model parameters and/or perform model predictions. Availability and implementation https://github.com/MichaelPHStumpf/Peitho
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btx776