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Gene regulatory networks, as a powerful abstraction for describing complexbiological interactions between genes through their expression products withina 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. Wepresent a Python package called Harissa which is dedicated to simulation andinference of such networks, based upon an underlying stochastic dynamical modeldriven by the transcriptional bursting phenomenon. As part of this tool,network inference can be interpreted as a calibration procedure for amechanistic model: once calibrated, the model is able to capture the typicalvariability of single-cell data without requiring ad hoc external noise, unlikeordinary or even stochastic differential equations frequently used in thiscontext. Therefore, Harissa can be used both as an inference tool, toreconstruct biologically relevant networks from time-course scRNA-seq data, andas a simulation tool, to generate quantitative gene expression profiles in anon-trivial way through gene interactions.

Harissa: stochastic simulation and inference of gene regulatory networks based on transcriptional bursting