RCGAToolbox

RCGAToolbox: Kinetic Model Parameter Estimation via Real-Coded Genetic Algorithms

RCGAToolbox implements real-coded genetic algorithms (RCGAs) in MATLAB to estimate parameters of kinetic models describing biochemical networks, including metabolic pathways and signal transduction pathways.

Key Features:

• Real-Coded Genetic Algorithms (RCGAs): Implements Unimodal Normal Distribution Crossover with Minimal Generation Gap (UNDX/MGG) for unimodal parameter spaces and Real-Coded Ensemble Crossover Star with Just Generation Gap (REXstar/JGG) for multimodal parameter spaces.
• Stochastic Ranking: Applies a stochastic ranking method to handle constrained optimization problems within the genetic algorithm framework.

Scientific Applications:

• Systems Biology Modeling: Estimates parameters in kinetic models to analyze dynamic behavior of biochemical networks and improve predictive modeling of metabolic and signal transduction pathways.

Methodology:

Applies UNDX/MGG and REXstar/JGG real-coded genetic algorithms to optimize parameter sets in kinetic models, integrating stochastic ranking to enforce constraints and balance exploration and exploitation of the parameter space.