The poly-omics of ageing through individual-based metabolic modelling
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 individu...
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| Format: | Article |
| Language: | English |
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BMC
2018-11-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-018-2383-z |
| _version_ | 1818837001687793664 |
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| author | Elisabeth Yaneske Claudio Angione |
| author_facet | Elisabeth Yaneske Claudio Angione |
| author_sort | Elisabeth Yaneske |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T03:15:34Z |
| format | Article |
| id | doaj.art-3ee2d674c9d345dea464466cd0b6ad2a |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-19T03:15:34Z |
| publishDate | 2018-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-3ee2d674c9d345dea464466cd0b6ad2a2022-12-21T20:37:54ZengBMCBMC Bioinformatics1471-21052018-11-0119S14839610.1186/s12859-018-2383-zThe poly-omics of ageing through individual-based metabolic modellingElisabeth Yaneske0Claudio Angione1Department of Computer Science and Information Systems, Teesside UniversityDepartment of Computer Science and Information Systems, Teesside UniversityAbstract 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.http://link.springer.com/article/10.1186/s12859-018-2383-zAgeingBiological ageMetabolic ageMetabolic modellingFlux balance analysisPoly-omics |
| spellingShingle | Elisabeth Yaneske Claudio Angione The poly-omics of ageing through individual-based metabolic modelling BMC Bioinformatics Ageing Biological age Metabolic age Metabolic modelling Flux balance analysis Poly-omics |
| title | The poly-omics of ageing through individual-based metabolic modelling |
| title_full | The poly-omics of ageing through individual-based metabolic modelling |
| title_fullStr | The poly-omics of ageing through individual-based metabolic modelling |
| title_full_unstemmed | The poly-omics of ageing through individual-based metabolic modelling |
| title_short | The poly-omics of ageing through individual-based metabolic modelling |
| title_sort | poly omics of ageing through individual based metabolic modelling |
| topic | Ageing Biological age Metabolic age Metabolic modelling Flux balance analysis Poly-omics |
| url | http://link.springer.com/article/10.1186/s12859-018-2383-z |
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