Data-driven multi-omics analyses and modelling for bioprocesses.
- Author:
Yan ZHU
1
;
Zhidan ZHANG
1
;
Peibin QIN
1
;
Jie SHEN
1
;
Jibin SUN
1
Author Information
- Publication Type:English Abstract
- Keywords: bioprocess; data science; digital cell model; integrative analysis; multi-omics
- MeSH: Fermentation; Genomics/methods*; Biotechnology/methods*; Proteomics/methods*; Models, Biological; Metabolomics/methods*; Bioreactors; Multiomics
- From: Chinese Journal of Biotechnology 2025;41(3):1152-1178
- CountryChina
- Language:Chinese
- Abstract: Biomanufacturing has emerged as a crucial driving force for efficient material conversion through engineered cells or cell-free systems. However, the intrinsic spatiotemporal heterogeneity, complexity, and dynamic characteristics of these processes pose significant challenges to systematic understanding, optimization, and regulation. This review summarizes essential methodologies for multi-omics data acquisition and analyses for bioprocesses and outlines modelling approaches based on multi-omics data. Furthermore, we explore practical applications of multi-omics and modelling in fine-tuning process parameters, improving fermentation control, elucidating stress response mechanisms, optimizing nutrient supplementation, and enabling real-time monitoring and adaptive adjustment. The substantial potential offered by integrating multi-omics with computational modelling for precision bioprocessing is also discussed. Finally, we identify current challenges in bioprocess optimization and propose the possible solutions, the implementation of which will significantly deepen understanding and enhance control of complex bioprocesses, ultimately driving the rapid advancement of biomanufacturing.
