Moles Carmen G, Mendes Pedro, Banga Julio R
Process Engineering Group, Instituto de Investigaciones Marinas (CSIC), 36208 Vigo, Spain.
Genome Res. 2003 Nov;13(11):2467-74. doi: 10.1101/gr.1262503. Epub 2003 Oct 14.
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 fail 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.
在此，我们探讨非线性动态生化途径的参数估计问题（反问题）。该问题被表述为一个受非线性微分代数约束的非线性规划（NLP）问题。众所周知，这些问题常常病态且多峰。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。为克服这一局限性，我们探索使用几种最先进的确定性和随机性全局优化方法。以一个考虑估计非线性生化动力学模型36个参数的案例研究作为基准。只有某一类随机算法，即进化策略（ES），能够成功解决这个问题。尽管这些随机方法不能确定地保证全局最优性，但它们的鲁棒性，加上在反问题中成本函数有已知下界这一事实，使它们成为最佳的可用候选方法。
