Fig. 1

Poly-omic ageing pipeline. We start with the transcriptomic data and chronological ages from the CD4 T-cells of 499 individuals. We use the chronological data and corresponding age-associated transcriptomic predictors to obtain the effect of both chronological and transcriptomic ageing on the transcriptomic layer. We then combine with the functional biological network data determined by the metabolism and poly-omic model to obtain individual-based metabolic models and their fluxomic profiles. Finally, we adapt machine learning techniques to show that fluxomic data clusters better with chronological age than transcriptomic data, and to identify metabolic predictors of ageing (the poly-omic ageing map). Definition of key terms. Transcriptomic – gene expression data represented by a measurement of the mRNA transcripts within a cell. Fluxomic – fluxomic data refers to reaction flux rates, namely the value for the rate of metabolite conversion, measured in millimoles per hour per grams of dry weight, for each reaction or collection of related reactions (subsystems) within a cell. Poly-omic – the integration of more than one type of ’omic’ data e.g. transcriptomic and fluxomic data