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 Pyt

SUBMITTER: Caravagna G 

PROVIDER: S-EPMC4464019 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature