Abstract
Summary Kinetic modeling is essential in understanding the dynamic behavior of biochemical networks, such as metabolic and signal transduction pathways. However, parameter estimation remains a major bottleneck in the development of kinetic models. We present RCGAToolbox, software for real-coded genetic algorithms (RCGAs), which accelerates the parameter estimation of kinetic models. RCGAToolbox provides two RCGAs: the unimodal normal distribution crossover with minimal generation gap (UNDX/MGG) and real-coded ensemble crossover star with just generation gap (REXstar/JGG), using the stochastic ranking method. The RCGAToolbox also provides user-friendly graphical user interfaces.
Availability and implementation RCGAToolbox is available from https://github.com/kmaeda16/RCGAToolbox under GNU GPLv3, with application examples. The user guide is provided in the Supplementary Material. RCGAToolbox runs on MATLAB in Windows, Linux, and macOS.
Contact kmaeda{at}bio.kyutech.ac.jp
Supplementary information Supplementary Material is available at Bioinformatics online.
Competing Interest Statement
The authors have declared no competing interest.