Unknown

Dataset Information

0

Automatising the analysis of stochastic biochemical time-series.


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.

SUBMITTER: Caravagna G 

PROVIDER: S-EPMC4464019 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

altmetric image

Publications

Automatising the analysis of stochastic biochemical time-series.

Caravagna Giulio G   De Sano Luca L   Antoniotti Marco M  

BMC bioinformatics 20150601


<h4>Background</h4>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.<h4>Motivation</h4>This operational pipeline relies on the ability to interpret the predictions of a model, often  ...[more]

Similar Datasets

| S-EPMC5347016 | biostudies-literature
| S-EPMC3994026 | biostudies-literature
| S-EPMC7410486 | biostudies-literature
| PRJEB35519 | ENA
| S-EPMC3491022 | biostudies-literature
| S-EPMC2694766 | biostudies-literature
| PRJEB87091 | ENA
| PRJEB8347 | ENA
| S-EPMC4995396 | biostudies-literature
| S-EPMC5444866 | biostudies-literature