ArticleBMC bioinformatics2018
The poly-omics of ageing through individual-based metabolic modelling.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Skeletal Age Evaluation with BAUSportJournal of functional morphology and kinesiology · 2026Article
- Tobacco, biological aging and epigenetic resilience: the Andorran paradox.Frontiers in aging · 2026Article
- Aging and Pathological Conditions Similarity Revealed by Meta-Analysis of Metabolomics Studies Suggests the Existence of the Health and Age-Related Metapathway.Metabolites · 2024Article
- Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design.Synthetic and systems biotechnology · 2024Article
- New insights into methods to measure biological age: a literature review.Frontiers in aging · 2024Review
- From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry.FEMS microbiology reviews · 2023Review
- Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems.Computational and structural biotechnology journal · 2023Review
- Methods for Stratification and Validation Cohorts: A Scoping Review.Journal of personalized medicine · 2022Article
- Integrated Multi-Omics for Novel Aging Biomarkers and Antiaging Targets.Biomolecules · 2021Review
- Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacteriumSTAR protocols · 2021Article
- Advances in flux balance analysis by integrating machine learning and mechanism-based models.Computational and structural biotechnology journal · 2021Review
- A Distance-Based Framework for the Characterization of Metabolic Heterogeneity in Large Sets of Genome-Scale Metabolic Models.Patterns (New York, N.Y.) · 2020Article
- A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- The era of big data: Genome-scale modelling meets machine learning.Computational and structural biotechnology journal · 2020Review
- Machine and deep learning meet genome-scale metabolic modeling.PLoS computational biology · 2019Review
- Block Forests: random forests for blocks of clinical and omics covariate data.BMC bioinformatics · 2019Article
- Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine.BioMed research international · 2019Review
Corrections and comments
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundAgeing 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.
resultsWe 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.
conclusionsWe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.