Metabolic Objectives and Trade-Offs: Inference and Applications DOI Creative Commons
Da-Wei Lin,

Saanjh Khattar,

Sriram Chandrasekaran

и другие.

Metabolites, Год журнала: 2025, Номер 15(2), С. 101 - 101

Опубликована: Фев. 6, 2025

Background/Objectives: Determining appropriate cellular objectives is crucial for the system-scale modeling of biological networks metabolic engineering, reprogramming, and drug discovery applications. The mathematical representation can describe how cells manage limited resources to achieve goals within mechanistic environmental constraints. While rapidly proliferating like tumors are often assumed prioritize biomass production, mammalian cell types exhibit beyond growth, such as supporting tissue functions, developmental processes, redox homeostasis. Methods: This review addresses challenge determining trade-offs from multiomics data. Results: Recent advances in single-cell omics, modeling, machine/deep learning methods have enabled inference at both transcriptomic levels, bridging gene expression patterns with phenotypes. Conclusions: These silico models provide insights into adapt changing environments, treatments, genetic manipulations. We further explore potential application incorporating personalized medicine, discovery, systems biology.

Язык: Английский

Metabolic Objectives and Trade-Offs: Inference and Applications DOI Creative Commons
Da-Wei Lin,

Saanjh Khattar,

Sriram Chandrasekaran

и другие.

Metabolites, Год журнала: 2025, Номер 15(2), С. 101 - 101

Опубликована: Фев. 6, 2025

Background/Objectives: Determining appropriate cellular objectives is crucial for the system-scale modeling of biological networks metabolic engineering, reprogramming, and drug discovery applications. The mathematical representation can describe how cells manage limited resources to achieve goals within mechanistic environmental constraints. While rapidly proliferating like tumors are often assumed prioritize biomass production, mammalian cell types exhibit beyond growth, such as supporting tissue functions, developmental processes, redox homeostasis. Methods: This review addresses challenge determining trade-offs from multiomics data. Results: Recent advances in single-cell omics, modeling, machine/deep learning methods have enabled inference at both transcriptomic levels, bridging gene expression patterns with phenotypes. Conclusions: These silico models provide insights into adapt changing environments, treatments, genetic manipulations. We further explore potential application incorporating personalized medicine, discovery, systems biology.

Язык: Английский

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