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

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. [ABSTRACT FROM AUTHOR]