文献类型：期刊文献

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

作者：Fan, Xingcun[1,2];Xiang, Guangming[2];Liao, Wenbin[1,2];Xiao, Luchi[2];He, Siwei[2];Luo, Na[1];Lu, Hongzhong[2];Yan, Xuefeng[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Sch Life Sci & Biotechnol, State Key Lab Microbial Metab, 800 Dongchuan Rd, Shanghai 200240, Peoples R China

年份：2025

卷号：10

期号：4

起止页码：1140

外文期刊名：SYNTHETIC AND SYSTEMS BIOTECHNOLOGY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001533905100001)】；

基金：Funding This work was financially supported by the National key research and development program of China (2020YFA0908300) and Shanghai Municipal Science and Technology Major Project.

语种：英文

外文关键词：S. cerevisiae; Distributed large-scale neural network; TRN; Mechanistic and data-driven; Transfer learning

摘要：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 pantranscriptomic 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.