Differential simulated annealing: a robust and efficient global optimization algorithm for parameter estimation of biological networks
Differential simulated annealing: a robust and efficient global optimization algorithm for parameter estimation of biological networks
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DOI：
10.1039/c4mb00100a
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发表时间：
2014-01-01
影响因子：
--
通讯作者：
Lai, Luhua
中科院分区：
文献类型：
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作者：
Dai, Ziwei;Lai, Luhua
常微分方程被广泛地用于模拟生物网络的动力学特性。由于生物网络的复杂性和有限的定量实验数据，估计这些模型的动力学参数仍然具有挑战性。提出了一种新的全局优化算法-差分模拟退火算法（DSA），用于鲁棒有效地估计生物网络模型的动力学参数。DSA进行了测试，从几个到几百个参数的BioModels数据库中的95个模型，并与其他五个广泛使用的参数估计算法，包括确定性和随机优化算法进行比较。我们的研究表明，DSA在整个数据集中的成功率最高，并且对于大型模型表现尤其出色。进一步的分析表明，DSA优于五种算法相比，在准确性和效率。
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.