  Gene regulatory networks, as a powerful abstraction for describing complex
biological interactions between genes through their expression products within
a cell, are often regarded as virtually deterministic dynamical systems.
However, this view is now being challenged by the fundamentally stochastic,
'bursty' nature of gene expression revealed at the single cell level. We
present a Python package called Harissa which is dedicated to simulation and
inference of such networks, based upon an underlying stochastic dynamical model
driven by the transcriptional bursting phenomenon. As part of this tool,
network inference can be interpreted as a calibration procedure for a
mechanistic model: once calibrated, the model is able to capture the typical
variability of single-cell data without requiring ad hoc external noise, unlike
ordinary or even stochastic differential equations frequently used in this
context. Therefore, Harissa can be used both as an inference tool, to
reconstruct biologically relevant networks from time-course scRNA-seq data, and
as a simulation tool, to generate quantitative gene expression profiles in a
non-trivial way through gene interactions.
