Abstract
Time scales for biological processes span many orders of magnitude, forcing modelers to tackle coupled processes that have large time scale gaps. This results in rare events, which take longer to occur than the fastest processes in the model. Efficient generation of rare events has been a focus of modelers for a long time and multiple software packages implement various rare event sampling algorithms. However, these packages frequently require expertise to get started with, making it harder for researchers to start using them. WEBNG (short for Weighted Ensemble–BioNetGen) is an open source software framwework that bridges the open source software packages WESTPA, which implements the weighted ensemble method for sampling rare events, and BioNetGen, which facilitates the specification and simulation of biochemical reaction network models following a rule-based approach. WEBNG simplifies rare event sampling in simulations of rule-based models by taking a model specified in the BioNetGen language (BNGL) and generating a WESTPA simulation folder ready to simulate with default parameters selected to match model observables. WEBNG is written in Python with dependencies only on proven, open-source packages that are in active development, which makes WEBNG easy to install and maintain. Here, we describe the architecture and features of WEBNG and demonstrate its capabilities through application to a two-gene model of cell fate transitions.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Updated the description and references for the clustering algorithm used by WEBNG.
