A data- and model-driven analysis reveals the multi-omic landscape of ageing

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Abstract

Altered expression of a number of genes has been correlated to biological ageing in humans. The biological age predicted from gene expression levels is known as transcriptomic age. This di ers from chronological age which is measured as the time that an individual has lived since their date of birth. Transcrip- tomic age can be older or younger than an individual's chronological age. At present, studies have focused on using transcriptomic data to predict transcrip- tomic age. However, this approach largely does not consider the e ect that genes have on the metabolic network and therefore on the observable cellular pheno- type. This research takes the current understanding of transcriptomic ageing a step further by generating and investigating genome-scale metabolic models of ageing, using machine learning methods and a multi-omic approach based on constraint-based modelling. We combine these models with a transcriptomic age predictor and gene expression data from CD4 T-Cells from human peripheral blood mononuclear cells in healthy individuals. We show that metabolic models augmented with transcriptomics data of ageing can generate greater metabolic insights into the di erences between chronological and transcriptomic age. Com- pared to standard transcriptomic-only approaches, our method provides a more comprehensive analysis of transcriptomic ageing and paves the way for a multi- omic understanding of ageing mechanisms in human cells.
Original languageEnglish
DOIs
Publication statusPublished - Apr 2017
Event5th International Work-Conference on Bioinformatics and Biomedical Engineering - Granada, Spain
Duration: 26 Apr 201728 Apr 2017

Conference

Conference5th International Work-Conference on Bioinformatics and Biomedical Engineering
Abbreviated titleIWBBIO 2017
CountrySpain
CityGranada
Period26/04/1728/04/17

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Gene Expression
Metabolic Networks and Pathways
Parturition
Genome
T-Lymphocytes
Research
Genes
Machine Learning

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Yaneske, E., & Angione, C. (2017). A data- and model-driven analysis reveals the multi-omic landscape of ageing. Paper presented at 5th International Work-Conference on Bioinformatics and Biomedical Engineering, Granada, Spain. https://doi.org/10.1007/978-3-319-56148-6_12
Yaneske, Elisabeth ; Angione, Claudio. / A data- and model-driven analysis reveals the multi-omic landscape of ageing. Paper presented at 5th International Work-Conference on Bioinformatics and Biomedical Engineering, Granada, Spain.
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abstract = "Altered expression of a number of genes has been correlated to biological ageing in humans. The biological age predicted from gene expression levels is known as transcriptomic age. This di ers from chronological age which is measured as the time that an individual has lived since their date of birth. Transcrip- tomic age can be older or younger than an individual's chronological age. At present, studies have focused on using transcriptomic data to predict transcrip- tomic age. However, this approach largely does not consider the e ect that genes have on the metabolic network and therefore on the observable cellular pheno- type. This research takes the current understanding of transcriptomic ageing a step further by generating and investigating genome-scale metabolic models of ageing, using machine learning methods and a multi-omic approach based on constraint-based modelling. We combine these models with a transcriptomic age predictor and gene expression data from CD4 T-Cells from human peripheral blood mononuclear cells in healthy individuals. We show that metabolic models augmented with transcriptomics data of ageing can generate greater metabolic insights into the di erences between chronological and transcriptomic age. Com- pared to standard transcriptomic-only approaches, our method provides a more comprehensive analysis of transcriptomic ageing and paves the way for a multi- omic understanding of ageing mechanisms in human cells.",
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Yaneske, E & Angione, C 2017, 'A data- and model-driven analysis reveals the multi-omic landscape of ageing' Paper presented at 5th International Work-Conference on Bioinformatics and Biomedical Engineering, Granada, Spain, 26/04/17 - 28/04/17, . https://doi.org/10.1007/978-3-319-56148-6_12

A data- and model-driven analysis reveals the multi-omic landscape of ageing. / Yaneske, Elisabeth; Angione, Claudio.

2017. Paper presented at 5th International Work-Conference on Bioinformatics and Biomedical Engineering, Granada, Spain.

Research output: Contribution to conferencePaperResearchpeer-review

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Yaneske E, Angione C. A data- and model-driven analysis reveals the multi-omic landscape of ageing. 2017. Paper presented at 5th International Work-Conference on Bioinformatics and Biomedical Engineering, Granada, Spain. https://doi.org/10.1007/978-3-319-56148-6_12