A multiscale compartment-based model of stochastic gene regulatory networks using hitting-time analysis

Spatial stochastic models of single cell kinetics are capable of capturing both fluctuations in molecular numbers and the spatial dependencies of the key steps of intracellular regulatory networks. The spatial stochastic model is straightforward to formulate and can be simulated using existing software, but due to its computationally cost it quickly becomes prohibitively expensive. This limits it use in applications that requires repeated simulation of the model, such as when embedded in multicellular simulations, for parameter inference, and in robustness analysis, model exploration and model checking. We here propose a multiscale model where a compartment-based model approximates a detailed spatial stochastic model. The compartment model is constructed via a first-exit times analysis on the spatial model, thus capturing spatial aspects of the fine-grained simulations. We apply the approach to a model of negative feedback gene regulation and evaluate the approximation accuracy over a wide range of parameters, assessing the situations in which a detailed spatial representation can be replaced by the computationally much cheaper compartment multiscale model.

How to use this repository

This repository is organized as follows:

• The scripts used to generate the data are gathered in the src folder.
• All the data necessary to generate the figures is compressed in the zip files from the data directory and must be extracted before the figures can be reproduced.
• All the figures from the manuscript can be regenerated using the Jupyter notebooks.

All the computations were performed on resources provided by SNIC through Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) under Project SNIC 2019/8-227.