Automatising the analysis of stochastic biochemical time-series.

Giulio Caravagna, Luca De Sano, Marco Antoniotti

Research output: Contribution to journalArticlepeer-review

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. Keywords: time-series analysis; stochastic models; Python
Original languageEnglish
Number of pages7
JournalBMC Bioinformatics
Volume16
Issue numberS-9
DOIs
Publication statusPublished - 1 Jun 2015

Keywords

  • dblp

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