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.
%0 Journal Article
%1 Dai2014Differential
%A Dai, Ziwei
%A Lai, Luhua
%D 2014
%I The Royal Society of Chemistry
%J Mol. BioSyst.
%K optimisation parameter-estimation simulated-annealing
%R 10.1039/c4mb00100a
%T Differential simulated annealing: a robust and efficient global optimization algorithm for parameter estimation of biological networks
%U http://dx.doi.org/10.1039/c4mb00100a
%X 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.
@article{Dai2014Differential,
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.},
added-at = {2018-12-02T16:09:07.000+0100},
author = {Dai, Ziwei and Lai, Luhua},
biburl = {https://www.bibsonomy.org/bibtex/2fee1ac4ae74ab3f31447e26d5b874500/karthikraman},
citeulike-article-id = {13137777},
citeulike-linkout-0 = {http://dx.doi.org/10.1039/c4mb00100a},
citeulike-linkout-1 = {http://www.rsc.org/Publishing/Journals/article.asp?doi=C4MB00100A},
doi = {10.1039/c4mb00100a},
interhash = {8384a5545717ae7cb0870d236c0cff27},
intrahash = {fee1ac4ae74ab3f31447e26d5b874500},
journal = {Mol. BioSyst.},
keywords = {optimisation parameter-estimation simulated-annealing},
posted-at = {2014-04-14 11:30:54},
priority = {2},
publisher = {The Royal Society of Chemistry},
timestamp = {2018-12-02T16:09:07.000+0100},
title = {Differential simulated annealing: a robust and efficient global optimization algorithm for parameter estimation of biological networks},
url = {http://dx.doi.org/10.1039/c4mb00100a},
year = 2014
}