Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan.
Department of Automation Engineering, National Formosa University, Yunlin 632, Taiwan.
Bioinformatics. 2020 Jun 1;36(12):3833-3840. doi: 10.1093/bioinformatics/btaa267.
Non-linear ordinary differential equation (ODE) models that contain numerous parameters are suitable for inferring an emulated gene regulatory network (eGRN). However, the number of experimental measurements is usually far smaller than the number of parameters of the eGRN model that leads to an underdetermined problem. There is no unique solution to the inference problem for an eGRN using insufficient measurements.
This work proposes an evolutionary modelling algorithm (EMA) that is based on evolutionary intelligence to cope with the underdetermined problem. EMA uses an intelligent genetic algorithm to solve the large-scale parameter optimization problem. An EMA-based method, GREMA, infers a novel type of gene regulatory network with confidence levels for every inferred regulation. The higher the confidence level is, the more accurate the inferred regulation is. GREMA gradually determines the regulations of an eGRN with confidence levels in descending order using either an S-system or a Hill function-based ODE model. The experimental results showed that the regulations with high-confidence levels are more accurate and robust than regulations with low-confidence levels. Evolutionary intelligence enhanced the mean accuracy of GREMA by 19.2% when using the S-system model with benchmark datasets. An increase in the number of experimental measurements may increase the mean confidence level of the inferred regulations. GREMA performed well compared with existing methods that have been previously applied to the same S-system, DREAM4 challenge and SOS DNA repair benchmark datasets.
All of the datasets that were used and the GREMA-based tool are freely available at https://nctuiclab.github.io/GREMA.
Supplementary data are available at Bioinformatics online.
包含众多参数的非线性常微分方程 (ODE) 模型适合推断模拟基因调控网络 (eGRN)。然而，实验测量的数量通常远小于 eGRN 模型的参数数量，这导致了一个欠定问题。使用不足的测量值对 eGRN 进行推断会导致没有唯一的解。
这项工作提出了一种基于进化智能的进化建模算法 (EMA) 来应对欠定问题。EMA 使用智能遗传算法来解决大规模参数优化问题。基于 EMA 的方法 GREMA 基于置信水平推断新型基因调控网络。置信水平越高，推断的调控就越准确。GREMA 使用 S 系统或 Hill 函数基于 ODE 模型，以置信水平递减的顺序逐步确定 eGRN 的调控。实验结果表明，置信水平较高的调控比置信水平较低的调控更准确和稳健。进化智能使用基准数据集时，通过 S 系统模型将 GREMA 的平均准确率提高了 19.2%。实验测量数量的增加可能会提高推断调控的平均置信水平。与之前应用于相同 S 系统、DREAM4 挑战和 SOS DNA 修复基准数据集的现有方法相比，GREMA 表现良好。
所有使用的数据集和基于 GREMA 的工具都可以在 https://nctuiclab.github.io/GREMA 上免费获得。
补充数据可在生物信息学在线获得。
