Identifying effective evolutionary strategies-based protocol for uncovering reaction kinetic parameters under the effect of measurement noises.

IF 4.4 1区 生物学 Q1 BIOLOGY
Hock Chuan Yeo, Varsheni Vijay, Kumar Selvarajoo
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引用次数: 0

Abstract

Background: The transition from explanative modeling of fitted data to the predictive modeling of unseen data for systems biology endeavors necessitates the effective recovery of reaction parameters. Yet, the relative efficacy of optimization algorithms in doing so remains under-studied, as to the specific reaction kinetics and the effect of measurement noises. To this end, we simulate the reactions of an artificial pathway using 4 kinetic formulations: generalized mass action (GMA), Michaelis-Menten, linear-logarithmic, and convenience kinetics. We then compare the effectiveness of 5 evolutionary algorithms (CMAES, DE, SRES, ISRES, G3PCX) for objective function optimization in kinetic parameter hyperspace to determine the corresponding estimated parameters.

Results: We quickly dropped the DE algorithm due to its poor performance. Baring measurement noise, we find the CMAES algorithm to only require a fraction of the computational cost incurred by other EAs for both GMA and linear-logarithmic kinetics yet performing as well by other criteria. However, with increasing noise, SRES and ISRES perform more reliably for GMA kinetics, but at considerably higher computational cost. Conversely, G3PCX is among the most efficacious for estimating Michaelis-Menten parameters regardless of noise, while achieving numerous folds saving in computational cost. Cost aside, we find SRES to be versatilely applicable across GMA, Michaelis-Menten, and linear-logarithmic kinetics, with good resilience to noise. Nonetheless, we could not identify the parameters of convenience kinetics using any algorithm.

Conclusions: Altogether, we identify a protocol for predicting reaction parameters under marked measurement noise, as a step towards predictive modeling for systems biology endeavors.

识别基于进化策略的有效协议,以揭示测量噪声影响下的反应动力学参数。
背景:在系统生物学研究中,要从拟合数据的解释性建模过渡到未见数据的预测性建模,就必须有效地恢复反应参数。然而,对于具体的反应动力学和测量噪声的影响,优化算法在这方面的相对功效仍未得到充分研究。为此,我们使用 4 种动力学公式模拟了人工途径的反应:广义质量作用(GMA)、迈克尔-门顿(Michaelis-Menten)、线性对数和方便动力学。然后,我们比较了 5 种进化算法(CMAES、DE、SRES、ISRES、G3PCX)在动力学参数超空间中进行目标函数优化的有效性,以确定相应的估计参数:由于 DE 算法性能不佳,我们很快就放弃了它。在不考虑测量噪声的情况下,我们发现 CMAES 算法在 GMA 和线性对数动力学方面所需的计算成本仅为其他 EA 算法的一小部分,但在其他标准方面表现同样出色。然而,随着噪声的增加,SRES 和 ISRES 在 GMA 动力学方面的表现更为可靠,但计算成本却高得多。相反,G3PCX 是估算 Michaelis-Menten 参数最有效的方法之一,不受噪声影响,同时还能节省数倍的计算成本。撇开成本不谈,我们发现 SRES 可广泛应用于 GMA、Michaelis-Menten 和线性对数动力学,对噪声有很好的适应性。然而,我们无法使用任何算法确定方便动力学的参数:总之,我们确定了在明显测量噪声条件下预测反应参数的方案,为系统生物学工作的预测建模迈出了一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Biology
BMC Biology 生物-生物学
CiteScore
7.80
自引率
1.90%
发文量
260
审稿时长
3 months
期刊介绍: BMC Biology is a broad scope journal covering all areas of biology. Our content includes research articles, new methods and tools. BMC Biology also publishes reviews, Q&A, and commentaries.
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