The poly-omics of ageing through individual-based metabolic modelling

Area of Science:

• Molecular Biology
• Systems Biology
• Computational Biology

Background:

• Ageing is classified as chronological (time since birth) or biological (environmental impact on gene expression).
• Transcriptomic ageing, a measure of biological ageing, is linked to specific genes and their expression levels.
• Current transcriptomic age predictors lack insight into metabolic and cellular phenotype changes.

Purpose of the Study:

• To develop a novel method for understanding transcriptomic ageing.
• To integrate gene expression data with metabolic models for a comprehensive analysis.
• To create a new metabolic age predictor.

Main Methods:

• Utilized poly-omic constraint-based models and machine learning.
• Created individual metabolic models from normalized CD4 T-cell gene expression data (n=499).
• Combined metabolic models with transcriptomic and chronological age predictors.

Main Results:

• Developed a novel metabolic age predictor.
• Provided new insights into the differences between transcriptomic and chronological ageing.
• Demonstrated the utility of poly-omic predictors in analyzing ageing.

Conclusions:

• Poly-omic predictors offer a more detailed analysis of transcriptomic ageing than gene-based methods.
• The developed approach provides a foundation for advancing the study of human cellular ageing mechanisms.