title: 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.
  
  
  BACKGROUND
  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.
authors.forename: Giulio
authors.initials: G
authors.lastname: Caravagna
authors.forename: Luca
authors.initials: L
authors.lastname: De Sano
authors.forename: Marco
authors.initials: M
authors.lastname: Antoniotti
mesh_terms: D000818:Animals; D001331:Automation; D001419:Bacteria / Q000235:genetics; D019295:Computational Biology; D003198:Computer Simulation; D008954:Models, Biological; D015233:Models, Statistical; D011235:Predatory Behavior; D012984:Software; D013269:Stochastic Processes