Dong Heng, Ma Baoshan, Meng Yangyang, Wu Yiming, Liu Yongjing, Zeng Tao, Huang Jinyan
School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China.
School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China.
Comput Biol Chem. 2024 Dec;113:108223. doi: 10.1016/j.compbiolchem.2024.108223. Epub 2024 Sep 23.
The reconstruction of gene regulatory networks (GRNs) stands as a vital approach in deciphering complex biological processes. The application of nonlinear ordinary differential equations (ODEs) models has demonstrated considerable efficacy in predicting GRNs. Notably, the decay rate and time delay are pivotal in authentic gene regulation, yet their systematic determination in ODEs models remains underexplored. The development of a comprehensive optimization framework for the effective estimation of these key parameters is essential for accurate GRN inference.
This study introduces GRNMOPT, an innovative methodology for inferring GRNs from time-series and steady-state data. GRNMOPT employs a combined use of decay rate and time delay in constructing ODEs models to authentically represent gene regulatory processes. It incorporates a multi-objective optimization approach, optimizing decay rate and time delay concurrently to derive Pareto optimal sets for these factors, thereby maximizing accuracy metrics such as AUROC (Area Under the Receiver Operating Characteristic curve) and AUPR (Area Under the Precision-Recall curve). Additionally, the use of XGBoost for calculating feature importance aids in identifying potential regulatory gene links.
Comprehensive experimental evaluations on two simulated datasets from DREAM4 and three real gene expression datasets (Yeast, In vivo Reverse-engineering and Modeling Assessment [IRMA], and Escherichia coli [E. coli]) reveal that GRNMOPT performs commendably across varying network scales. Furthermore, cross-validation experiments substantiate the robustness of GRNMOPT.
We propose a novel approach called GRNMOPT to infer GRNs based on a multi-objective optimization framework, which effectively improves inference accuracy and provides a powerful tool for GRNs inference.
基因调控网络（GRN）的重建是解析复杂生物过程的重要方法。非线性常微分方程（ODE）模型在预测GRN方面已显示出相当的功效。值得注意的是，衰减率和时间延迟在真实的基因调控中至关重要，然而它们在ODE模型中的系统确定仍未得到充分探索。开发一个全面的优化框架以有效估计这些关键参数对于准确的GRN推断至关重要。
本研究引入了GRNMOPT，这是一种从时间序列和稳态数据推断GRN的创新方法。GRNMOPT在构建ODE模型时联合使用衰减率和时间延迟，以真实地表示基因调控过程。它采用多目标优化方法，同时优化衰减率和时间延迟，以得出这些因素的帕累托最优集，从而最大化诸如AUROC（受试者工作特征曲线下面积）和AUPR（精确召回率曲线下面积）等准确性指标。此外，使用XGBoost计算特征重要性有助于识别潜在的调控基因联系。
对来自DREAM4的两个模拟数据集以及三个真实基因表达数据集（酵母、体内逆向工程和建模评估[IRMA]以及大肠杆菌[E. coli]）进行的综合实验评估表明，GRNMOPT在不同网络规模上均表现出色。此外，交叉验证实验证实了GRNMOPT的稳健性。
我们提出了一种名为GRNMOPT的基于多目标优化框架推断GRN的新方法，该方法有效提高了推断准确性，并为GRN推断提供了强大工具。
