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The poly-omics of ageing through individual-based metabolic modelling
- Source :
- BMC Bioinformatics, BMC Bioinformatics, Vol 19, Iss S14, Pp 83-96 (2018)
- Publication Year :
- 2018
- Publisher :
- Springer Science and Business Media LLC, 2018.
-
Abstract
- Background Ageing can be classified in two different ways, chronological ageing and biological ageing. While chronological age is a measure of the time that has passed since birth, biological (also known as transcriptomic) ageing is defined by how time and the environment affect an individual in comparison to other individuals of the same chronological age. Recent research studies have shown that transcriptomic age is associated with certain genes, and that each of those genes has an effect size. Using these effect sizes we can calculate the transcriptomic age of an individual from their age-associated gene expression levels. The limitation of this approach is that it does not consider how these changes in gene expression affect the metabolism of individuals and hence their observable cellular phenotype. Results We propose a method based on poly-omic constraint-based models and machine learning in order to further the understanding of transcriptomic ageing. We use normalised CD4 T-cell gene expression data from peripheral blood mononuclear cells in 499 healthy individuals to create individual metabolic models. These models are then combined with a transcriptomic age predictor and chronological age to provide new insights into the differences between transcriptomic and chronological ageing. As a result, we propose a novel metabolic age predictor. Conclusions We show that our poly-omic predictors provide a more detailed analysis of transcriptomic ageing compared to gene-based approaches, and represent a basis for furthering our knowledge of the ageing mechanisms in human cells. Electronic supplementary material The online version of this article (10.1186/s12859-018-2383-z) contains supplementary material, which is available to authorized users.
- Subjects :
- Adult
Male
0301 basic medicine
Aging
Flux balance analysis
Biological age
T-Lymphocytes
Computational biology
Biology
lcsh:Computer applications to medicine. Medical informatics
Models, Biological
Biochemistry
Transcriptome
Young Adult
03 medical and health sciences
Individual based
Metabolic age
Structural Biology
CD4 T-cells
Machine learning
Cluster Analysis
Humans
lcsh:QH301-705.5
Molecular Biology
Gene
Metabolic modelling
Principal Component Analysis
Research
Applied Mathematics
Poly-omics
Genomics
Middle Aged
Omics
Computer Science Applications
Ageing
030104 developmental biology
lcsh:Biology (General)
lcsh:R858-859.7
Regression Analysis
Female
DNA microarray
Subjects
Details
- ISSN :
- 14712105
- Volume :
- 19
- Database :
- OpenAIRE
- Journal :
- BMC Bioinformatics
- Accession number :
- edsair.doi.dedup.....a5456f3040294e4e8d680ebc823410ae