Automatising the analysis of stochastic biochemical time-series. Issue 9 (December 2015)

• Record Type: Journal Article
• Authors: Caravagna, Giulio; De Sano, Luca; Antoniotti, Marco
• Is Part Of: BMC bioinformatics. Volume 16:Issue 9(2015)
• Page Start: 1
• Page End: 7
• Publication Date: 2015-12
• Subjects: time-series analysis -- stochastic models -- Python; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/1471-2105-16-S9-S8
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background Mathematical and computational modelling of biochemical systems has seen a lot of effort devoted to the definition and implementation of high-performance mechanistic simulation frameworks. Within these frameworks it is possible to analyse complex models under a variety of configurations, eventually selecting the best setting of, e.g., parameters for a target system. Motivation This operational pipeline relies on the ability to interpret the predictions of a model, often represented as simulation time-series. Thus, an efficient data analysis pipeline is crucial to automatise time-series analyses, bearing in mind that errors in this phase might mislead the modeller's conclusions. Results For this reason we have developed an intuitive framework-independent Python tool to automate analyses common to a variety of modelling approaches. These include assessment of useful non-trivial statistics for simulation ensembles, e.g., estimation of master equations. Intuitive and domain-independent batch scripts will allow the researcher to automatically prepare reports, thus speeding up the usual model-definition, testing and refinement pipeline.