A Multi‐Omics, Machine Learning‐Aware, Genome‐Wide Metabolic Model of Bacillus Subtilis Refines the Gene Expression and Cell Growth Prediction
Abstract: Given the extensive heterogeneity and variability, understanding cellular functions and regulatory mechanisms through the analysis of multi‐omics datasets becomes extremely challenging. Here, a comprehensive modeling framework of multi‐omics machine learning and metabolic network models are proposed that covers various cellular biological processes across multiple scales. This model on an extensive normalized compendium of Bacillus subtilis is validated, which encompasses gene expression data from environmental perturbations, transcriptional regulation, signal transduction, protein translation, and growth measurements. Comparison with high‐throughput experimental data shows that EM_iBsu1209‐ME, constructed on this basis, can accurately predict the expression of 605 genes and the synthesis of 23 metabolites under different conditions. This study paves the way for the construction of comprehensive biological databases and high‐performance multi‐omics metabolic models to achieve accurate predictive analysis in exploring complex mechanisms of cell genotypes and phenotypes.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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A Multi‐Omics, Machine Learning‐Aware, Genome‐Wide Metabolic Model of Bacillus Subtilis Refines the Gene Expression and Cell Growth Prediction ; day:17 ; month:09 ; year:2024 ; extent:12
Advanced science ; (17.09.2024) (gesamt 12)
- Urheber
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Bi, Xinyu
Cheng, Yang
Lv, Xueqin
Liu, Yanfeng
Li, Jianghua
Du, Guocheng
Chen, Jian
Liu, Long
- DOI
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10.1002/advs.202408705
- URN
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urn:nbn:de:101:1-2409171425575.735531444085
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:20 MESZ
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Beteiligte
- Bi, Xinyu
- Cheng, Yang
- Lv, Xueqin
- Liu, Yanfeng
- Li, Jianghua
- Du, Guocheng
- Chen, Jian
- Liu, Long
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