Biology subjects

Efimov, E.

Publications and source records attributed to Efimov, E..

3 recordsLinked to original sources

bioRxiv2025

Somatic mutations impose an entropic upper bound on human lifespan

Somatic mutations accumulate with age and can cause cell death, but their quantitative contribution to limiting human lifespan remains unclear. We developed an incremental modeling framework that progressively incorporates factors contributing to aging into a model of population survival dynamics, which we used to estimate lifespan limits if all aging hallmarks were eliminated except somatic mutations. Our analysis reveals fundamental asymmetry across organs: post-mitotic cells such as neurons and cardiomyocytes act as critical longevity bottlenecks, with somatic mutations reducing median lifespan from a theoretical non-aging baseline of 430 years to 169 years. In contrast, proliferating tissues like liver maintain functionality for thousands of years through cellular replacement, effectively neutralizing mutation-driven decline. Multi-organ integration predicts median lifespans of 134-170 years --approximately twice current human longevity. This substantial yet incomplete reduction indicates that somatic mutations significantly drive aging but cannot alone account for observed mortality, implying comparable contributions from other hallmarks.

systems biology↗

bioRxiv2024

ComputAgeBench: Epigenetic Aging Clocks Benchmark

The success of clinical trials of longevity drugs relies heavily on identifying integrative health and aging biomarkers, such as biological age. Epigenetic aging clocks predict the biological age of an individual using their DNA methylation profiles, commonly retrieved from blood samples. However, there is no standardized methodology to validate and compare epigenetic clock models as yet. We propose ComputAgeBench, a unifying framework that comprises such a methodology and a dataset for comprehensive benchmarking of different clinically relevant aging clocks. Our methodology exploits the core idea that reliable aging clocks must be able to distinguish between healthy individuals and those with aging-accelerating conditions. Specifically, we collected and harmonized 66 public datasets of blood DNA methylation, covering 19 such conditions across different ages, and tested 13 published clock models. Additionally, we compiled 46 separate datasets to facilitate the training of new aging clocks. We believe our work will bring the fields of aging biology and machine learning closer together for the research on reliable biomarkers of health and aging. Codehttps://github.com/ComputationalAgingLab/ComputAge Datasethttps://huggingface.co/datasets/computage/computage_bench

bioinformatics↗

bioRxiv2023

Epistemic uncertainty challenges aging clock reliability in predicting rejuvenation effects

Epigenetic aging clocks have been widely used to validate rejuvenation effects during cellular reprogramming. However, these predictions are unverifiable because the true biological age of reprogrammed cells remains unknown. We present an analytical framework to consider rejuvenation predictions from the uncertainty perspective. Our analysis reveals that the DNA methylation profiles across reprogramming are poorly represented in the aging data used to train clock models, thus introducing high epistemic uncertainty in age estimations. Moreover, predictions of different published clocks are inconsistent, with some even suggesting zero or negative rejuvenation. While not questioning the possibility of age reversal, we show that the high clock uncertainty challenges the reliability of rejuvenation effects observed during in vitro reprogramming before pluripotency and throughout embryogenesis. Conversely, our method reveals a significant age increase after in vivo reprogramming. We recommend including uncertainty estimation in future aging clock models to avoid the risk of misinterpreting the results of biological age prediction.

systems biology↗