Parameter estimation in biochemical pathways: A comparison of global optimization methods
Parameter estimation in biochemical pathways: A comparison of global optimization methods
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DOI：
10.1101/gr.1262503
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发表时间：
2003-11-01
期刊：
影响因子：
7
通讯作者：
Banga, JR
中科院分区：
文献类型：
--
作者：
Moles, CG;Mendes, P;Banga, JR
本文主要研究非线性动态生化途径的参数估计问题（逆问题）。该问题被描述为一个非线性微分代数约束下的非线性规划问题。众所周知，这些问题往往是病态的和多模态的。因此，传统的（基于梯度的）局部优化方法无法得到令人满意的解。为了克服这一限制，探索了几种最先进的确定性和随机全局优化方法的使用。以一个考虑36个参数估计的非线性生化动力学模型为基准进行了实例研究。只有某种类型的随机算法，进化策略（ES），能够成功地解决这个问题。虽然这些随机方法不能保证全局最优的确定性，但它们的鲁棒性，加上在逆问题中它们有一个已知的成本函数的下界，使它们成为可用的最佳候选者。
Here we address the problem of parameter estimation (inverse problem) of nonlinear dynamic biochemical pathways. This problem is stated as a nonlinear programming (NLP) problem subject to nonlinear differential-algebraic constraints. These problems are known to be frequently ill-conditioned and multimodal. Thus, traditional (gradient-based) local optimization methods fall to arrive at satisfactory solutions. To surmount this limitation, the use of several state-of-the-art deterministic and stochastic global optimization methods is explored. A case Study considering the estimation of 36 parameters of a nonlinear biochemical dynamic model is taken as a benchmark. Only a certain type of stochastic algorithm, evolution strategies (ES), is able to solve this problem successfully. Although these stochastic methods cannot guarantee global optimality with certainty, their robustness, plus the fact that in inverse problems they have a known lower bound for the cost function, make them the best available candidates.