Tomasz Jetka1, Agata Charzyńska, Anna Gambin, Michael P H Stumpf, Michał Komorowski. 1. Institute of Fundamental Technological Research, Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland, Faculty of Mathematics Informatics and Mechanics, Institute of Informatics, University of Warsaw, Warsaw, Poland and Division of Molecular Biosciences, Imperial College London, London, UK.
Abstract
MOTIVATION: Stochasticity is an indispensable aspect of biochemical processes at the cellular level. Studies on how the noise enters and propagates in biochemical systems provided us with non-trivial insights into the origins of stochasticity, in total, however, they constitute a patchwork of different theoretical analyses. RESULTS: Here we present a flexible and widely applicable noise decomposition tool that allows us to calculate contributions of individual reactions to the total variability of a system's output. With the package it is, therefore, possible to quantify how the noise enters and propagates in biochemical systems. We also demonstrate and exemplify using the JAK-STAT signalling pathway that the noise contributions resulting from individual reactions can be inferred from data experimental data along with Bayesian parameter inference. The method is based on the linear noise approximation, which is assumed to provide a reasonable representation of analyzed systems. AVAILABILITY AND IMPLEMENTATION: http://sourceforge.net/p/stochdecomp/
MOTIVATION: Stochasticity is an indispensable aspect of biochemical processes at the cellular level. Studies on how the noise enters and propagates in biochemical systems provided us with non-trivial insights into the origins of stochasticity, in total, however, they constitute a patchwork of different theoretical analyses. RESULTS: Here we present a flexible and widely applicable noise decomposition tool that allows us to calculate contributions of individual reactions to the total variability of a system's output. With the package it is, therefore, possible to quantify how the noise enters and propagates in biochemical systems. We also demonstrate and exemplify using the JAK-STAT signalling pathway that the noise contributions resulting from individual reactions can be inferred from data experimental data along with Bayesian parameter inference. The method is based on the linear noise approximation, which is assumed to provide a reasonable representation of analyzed systems. AVAILABILITY AND IMPLEMENTATION: http://sourceforge.net/p/stochdecomp/