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Automatising the analysis of stochastic biochemical time-series

Overview of attention for article published in BMC Bioinformatics, June 2015
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Automatising the analysis of stochastic biochemical time-series
Published in
BMC Bioinformatics, June 2015
DOI 10.1186/1471-2105-16-s9-s8
Pubmed ID

Giulio Caravagna, Luca De Sano, Marco Antoniotti


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. 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. 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.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 31%
Student > Ph. D. Student 3 23%
Student > Master 2 15%
Student > Bachelor 1 8%
Lecturer > Senior Lecturer 1 8%
Other 2 15%
Readers by discipline Count As %
Computer Science 6 46%
Agricultural and Biological Sciences 3 23%
Medicine and Dentistry 2 15%
Biochemistry, Genetics and Molecular Biology 1 8%
Engineering 1 8%
Other 0 0%