PEITH(Θ) - perfecting experiments with information theory in python with GPU 
support.

Dony, L.; Mackerodt, J.; Ward, S.; Filippi, S.; Stumpf, M. P. H.; Liepe, J.

doi:10.1093/bioinformatics/btx776

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PEITH(Θ) - perfecting experiments with information theory in python with GPU support.

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Liepe, J.
Research Group of Quantitative and System Biology, MPI for Biophysical Chemistry, Max Planck Society;

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Citation

Dony, L., Mackerodt, J., Ward, S., Filippi, S., Stumpf, M. P. H., & Liepe, J. (2018). PEITH(Θ) - perfecting experiments with information theory in python with GPU support. Bioinformatics, 34(7), 1249-1250. doi:10.1093/bioinformatics/btx776.

Cite as: https://hdl.handle.net/11858/00-001M-0000-002E-89C7-7

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.