Decoding yeast transcriptional regulation via a data-and mechanism-driven distributed large-scale network model

IF 4.4 2区 生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Xingcun Fan , Guangming Xiang , Wenbin Liao , Luchi Xiao , Siwei He , Na Luo , Hongzhong Lu , Xuefeng Yan
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Abstract

The complex transcriptional regulatory relationships among genes influence gene expression levels and play a crucial role in determining cellular phenotypes. In this study, we propose a novel, distributed, large-scale transcriptional regulatory neural network model (DLTRNM), which integrates prior knowledge into the reconstruction of pre-trained machine learning models, followed by fine-tuning. Using Saccharomyces cerevisiae as a case study, the curated transcriptional regulatory relationships are used to define the interactions between transcription factors (TFs) and their target genes (TGs). Subsequently, DLTRNM is pre-trained on pan-transcriptomic data and fine-tuned with time-series data, enabling it to accurately predict regulatory correlations. Additionally, DLTRNM can help identify potential key TFs, thereby simplifying the complex and interrelated transcriptional regulatory networks (TRNs). It can also complement previously reported transcriptional regulatory subnetworks. DLTRNM provides a powerful tool for studying transcriptional regulation with reduced computational demands and enhanced interpretability. Thus, this study marks a significant advancement in systems biology for understanding the complex transcriptional regulation within cells.
通过数据和机制驱动的分布式大规模网络模型解码酵母转录调控
基因间复杂的转录调控关系影响着基因表达水平,在决定细胞表型中起着至关重要的作用。在这项研究中,我们提出了一种新的、分布式的、大规模的转录调节神经网络模型(DLTRNM),该模型将先验知识整合到预训练机器学习模型的重建中,然后进行微调。以酿酒酵母(Saccharomyces cerevisiae)为例,利用调控的转录调控关系来定义转录因子(tf)与其靶基因(TGs)之间的相互作用。随后,DLTRNM在泛转录组学数据上进行预训练,并使用时间序列数据进行微调,使其能够准确预测调控相关性。此外,DLTRNM可以帮助识别潜在的关键tf,从而简化复杂且相互关联的转录调控网络(trn)。它还可以补充先前报道的转录调控子网络。DLTRNM为研究转录调控提供了一个强大的工具,减少了计算需求,增强了可解释性。因此,这项研究标志着系统生物学在理解细胞内复杂的转录调控方面取得了重大进展。
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来源期刊
Synthetic and Systems Biotechnology
Synthetic and Systems Biotechnology BIOTECHNOLOGY & APPLIED MICROBIOLOGY-
CiteScore
6.90
自引率
12.50%
发文量
90
审稿时长
67 days
期刊介绍: Synthetic and Systems Biotechnology aims to promote the communication of original research in synthetic and systems biology, with strong emphasis on applications towards biotechnology. This journal is a quarterly peer-reviewed journal led by Editor-in-Chief Lixin Zhang. The journal publishes high-quality research; focusing on integrative approaches to enable the understanding and design of biological systems, and research to develop the application of systems and synthetic biology to natural systems. This journal will publish Articles, Short notes, Methods, Mini Reviews, Commentary and Conference reviews.
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