Differential simulated annealing: a robust and efficient global optimization algorithm for parameter estimation of biological networks

Abstract

Ordinary differential equations (ODEs) are widely used to model the dynamic properties of biological networks. Due to the complexity of biological networks and limited quantitative experimental data available, estimating kinetic parameters for these models remains challenging. We present a novel global optimization algorithm, differential simulated annealing (DSA), for estimating kinetic parameters for biological network models robustly and efficiently. DSA was tested on 95 models sizing from a few to several hundreds of parameters from the BioModels database and compared with other five widely used algorithms for parameter estimation, including both deterministic and stochastic optimization algorithms. Our study showed that DSA gave the highest success rate in the whole dataset and performed especially well for large models. Further analysis revealed that DSA outperformed the five algorithms compared in both accuracy and efficiency.