School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran 19395-5746, Iran.
Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA.
Bioinformatics. 2023 Feb 3;39(2). doi: 10.1093/bioinformatics/btad072.
The gene regulatory process resembles a logic system in which a target gene is regulated by a logic gate among its regulators. While various computational techniques are developed for a gene regulatory network (GRN) reconstruction, the study of logical relationships has received little attention. Here, we propose a novel tool called wpLogicNet that simultaneously infers both the directed GRN structures and logic gates among genes or transcription factors (TFs) that regulate their target genes, based on continuous steady-state gene expressions.
wpLogicNet proposes a framework to infer the logic gates among any number of regulators, with a low time-complexity. This distinguishes wpLogicNet from the existing logic-based models that are limited to inferring the gate between two genes or TFs. Our method applies a Bayesian mixture model to estimate the likelihood of the target gene profile and to infer the logic gate a posteriori. Furthermore, in structure-aware mode, wpLogicNet reconstructs the logic gates in TF-gene or gene-gene interaction networks with known structures. The predicted logic gates are validated on simulated datasets of TF-gene interaction networks from Escherichia coli. For the directed-edge inference, the method is validated on datasets from E.coli and DREAM project. The results show that compared to other well-known methods, wpLogicNet is more precise in reconstructing the network and logical relationships among genes.
The datasets and R package of wpLogicNet are available in the github repository, https://github.com/CompBioIPM/wpLogicNet.
Supplementary data are available at Bioinformatics online.
基因调控过程类似于一个逻辑系统，其中靶基因由其调控因子中的逻辑门调控。虽然已经开发了各种计算技术来进行基因调控网络 (GRN) 重建，但逻辑关系的研究却很少受到关注。在这里，我们提出了一种名为 wpLogicNet 的新工具，它可以根据连续的稳态基因表达，同时推断出靶基因的有向 GRN 结构和基因或转录因子 (TF) 之间的逻辑门。
wpLogicNet 提出了一种框架，可以推断任意数量的调控因子之间的逻辑门，时间复杂度低。这使 wpLogicNet 区别于现有的基于逻辑的模型，后者仅限于推断两个基因或 TF 之间的门。我们的方法应用贝叶斯混合模型来估计目标基因谱的可能性，并推断逻辑门的后验概率。此外，在结构感知模式下，wpLogicNet 在具有已知结构的 TF-基因或基因-基因相互作用网络中重建逻辑门。预测的逻辑门在来自大肠杆菌的 TF-基因相互作用网络的模拟数据集上进行验证。对于有向边推断，该方法在来自大肠杆菌和 DREAM 项目的数据集上进行验证。结果表明，与其他知名方法相比，wpLogicNet 在重建网络和基因之间的逻辑关系方面更加精确。
wpLogicNet 的数据集和 R 包可在 github 存储库中获得，网址为 https://github.com/CompBioIPM/wpLogicNet。
补充数据可在生物信息学在线获得。
