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.

Caravagna, G., De Sano, L., Antoniotti, M. (2015). Automatising the analysis of stochastic biochemical time-series. BMC BIOINFORMATICS, 16(9) [10.1186/1471-2105-16-S9-S8].

Automatising the analysis of stochastic biochemical time-series

CARAVAGNA, GIULIO
;
ANTONIOTTI, MARCO
Ultimo
2015

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.
Articolo in rivista - Articolo scientifico
time-series analysis; stochastic models; Python
English
Caravagna, G., De Sano, L., Antoniotti, M. (2015). Automatising the analysis of stochastic biochemical time-series. BMC BIOINFORMATICS, 16(9) [10.1186/1471-2105-16-S9-S8].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/83936
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