Xingcun Fan , Guangming Xiang , Wenbin Liao , Luchi Xiao , Siwei He , Na Luo , Hongzhong Lu , Xuefeng Yan
{"title":"Decoding yeast transcriptional regulation via a data-and mechanism-driven distributed large-scale network model","authors":"Xingcun Fan , Guangming Xiang , Wenbin Liao , Luchi Xiao , Siwei He , Na Luo , Hongzhong Lu , Xuefeng Yan","doi":"10.1016/j.synbio.2025.06.005","DOIUrl":null,"url":null,"abstract":"<div><div>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 <em>Saccharomyces cerevisiae</em> 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.</div></div>","PeriodicalId":22148,"journal":{"name":"Synthetic and Systems Biotechnology","volume":"10 4","pages":"Pages 1140-1149"},"PeriodicalIF":4.4000,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Synthetic and Systems Biotechnology","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2405805X25000900","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0
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.
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.