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

TL;DR

Harissa is a Python package that models gene regulatory networks as stochastic systems driven by transcriptional bursting, enabling more accurate inference and simulation of single-cell gene expression data.

Contribution

It introduces a mechanistic stochastic model and a Python tool for network inference and simulation based on transcriptional bursting phenomena.

Findings

Captures variability in single-cell data without external noise

Enables network reconstruction from time-course scRNA-seq data

Simulates gene expression profiles considering gene interactions

Abstract

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.