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Automatising the analysis of stochastic biochemical time-series
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,...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4464019/ https://www.ncbi.nlm.nih.gov/pubmed/26051821 http://dx.doi.org/10.1186/1471-2105-16-S9-S8 |
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author | Caravagna, Giulio De Sano, Luca Antoniotti, Marco |
author_facet | Caravagna, Giulio De Sano, Luca Antoniotti, Marco |
author_sort | Caravagna, Giulio |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4464019 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-44640192015-06-29 Automatising the analysis of stochastic biochemical time-series Caravagna, Giulio De Sano, Luca Antoniotti, Marco BMC Bioinformatics Research 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. BioMed Central 2015-06-01 /pmc/articles/PMC4464019/ /pubmed/26051821 http://dx.doi.org/10.1186/1471-2105-16-S9-S8 Text en Copyright © 2015 Caravagna et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Caravagna, Giulio De Sano, Luca Antoniotti, Marco Automatising the analysis of stochastic biochemical time-series |
title | Automatising the analysis of stochastic biochemical time-series |
title_full | Automatising the analysis of stochastic biochemical time-series |
title_fullStr | Automatising the analysis of stochastic biochemical time-series |
title_full_unstemmed | Automatising the analysis of stochastic biochemical time-series |
title_short | Automatising the analysis of stochastic biochemical time-series |
title_sort | automatising the analysis of stochastic biochemical time-series |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4464019/ https://www.ncbi.nlm.nih.gov/pubmed/26051821 http://dx.doi.org/10.1186/1471-2105-16-S9-S8 |
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