Automatising the analysis of stochastic biochemical time-series

BMC Bioinformatics

Table 1 Data-processing PYTSA functions

Function	Synopsis
timeseries	load a time-series from a single file (output of a single simulation)
dataset	load a dataset of time-series (output of repeated simulations)
splot	plot plain time-series (without any processing)
aplot, sdplot, asdplot	plot average, standard-deviation and both of them for a dataset
phspace2d, phspace3d	plot 2D/3D phase-spaces for plain time-series
aphspace2d, aphspace3d	plot 2D/3D phase-spaces for the average of a dataset
pdf, pdf3d	plot the probability density of a model variable at one or more (3D) time-points (requires a dataset)
meq2d, meq3d	estimates the master equation solution for a model variable in the form of time-varying probability density for a time-interval (heatmap 2D or surface 3D, requires a dataset)

These functions (plus others unreported here which allow the user to customise the running environment) are natively implemented within PYTSA and can be combined in scripts for batch processing, or interpreted in any Python interactive environment. Each of these functions has a complex set of input parameters, whose meaning and usage is documented in the tool manual, where examples are provided, see [36]. A simple explanatory script which makes use of some of these commands is reported and commented in Figure 2.

ISSN: 1471-2105