Department of Mathematics and Computer Science, Clark University, Worcester, MA, USA; Department of Mathematics, University of Connecticut, Storrs, CT, USA.
Biology Department, Clark University, Worcester, MA, USA.
Math Biosci. 2021 Dec;342:108716. doi: 10.1016/j.mbs.2021.108716. Epub 2021 Oct 21.
A detailed comprehension of transcriptional regulation is critical to understanding the genetic control of development and disease across many different organisms. To more fully investigate the complex molecular interactions controlling the precise expression of genes, many groups have constructed mathematical models to complement their experimental approaches. A critical step in such studies is choosing the most appropriate parameter estimation algorithm to enable detailed analysis of the parameters that contribute to the models. In this study, we develop a novel set of evolutionary algorithms that use a pseudo-random Sobol Set to construct the initial population and incorporate parameter sensitivities into the adaptation of mutation rates, using local, global, and hybrid strategies. Comparison of the performance of these new algorithms to a number of current state-of-the-art global parameter estimation algorithms on a range of continuous test functions, as well as synthetic biological data representing models of gene regulatory systems, reveals improved performance of the new algorithms in terms of runtime, error and reproducibility. In addition, by analyzing the ability of these algorithms to fit datasets of varying quality, we provide the experimentalist with a guide to how the algorithms perform across a range of noisy data. These results demonstrate the improved performance of the new set of parameter estimation algorithms and facilitate meaningful integration of model parameters and predictions in our understanding of the molecular mechanisms of gene regulation.
深入理解转录调控对于理解许多不同生物体的发育和疾病的遗传控制至关重要。为了更全面地研究控制基因精确表达的复杂分子相互作用，许多研究小组构建了数学模型来补充他们的实验方法。在这些研究中，一个关键步骤是选择最合适的参数估计算法，以实现对有助于模型的参数的详细分析。在这项研究中，我们开发了一套新的进化算法，该算法使用伪随机 Sobol 集来构建初始种群，并将参数敏感性纳入突变率的适应过程中，使用局部、全局和混合策略。将这些新算法与一系列连续测试函数以及代表基因调控系统模型的合成生物学数据上的许多当前最先进的全局参数估计算法的性能进行比较，结果表明新算法在运行时间、误差和可重复性方面的性能得到了提高。此外，通过分析这些算法拟合不同质量数据集的能力，我们为实验人员提供了一个指南，说明这些算法在一系列噪声数据中的性能如何。这些结果表明了新的参数估计算法集的改进性能，并有助于在我们对基因调控的分子机制的理解中对模型参数和预测进行有意义的整合。
