Abstract: Inference of complete genetic regulatory networks is a central problem in modern bioinformatics. However, because good biological data is still relatively rare, it is hard to evaluate new machine learning techniques for network inference. In this report we describe GreenSim, a modular, customisable and extensible genetic regulatory network simulator. It accurately models motifs, non-linear regulatory functions and can generate networks ranging in size from N = 100 to N = 104 genes. Code is released under the GPL and available online here and from the authors. Bibtex entry:
@TechTeport{fogelbergVasile2008b,
author = {Christopher Fogelberg and Vasile Palade},
title = {{GreenSim}: A Genetic Regulatory Network Simulator},
institution = {Computing Laboratory, Oxford University},
year = 2008,
number = {PRG-RR-08-07},
address = {Wolfson Building, Parks Road, Oxford, OX1-3QD},
month = {May},
url = {http://syntilect.com/cgf/pubs:greensimtr}
}
The technical report is available here and here.