Advances in quantitative biology methods for studying replicative aging in Saccharomyces cerevisiae
| Autor: | Meng Jin, Lorraine Pillus, Richard O’Laughlin, Jeff Hasty, Lev S. Tsimring, Yang Li, Nan Hao |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Aging biology Physiology ved/biology Saccharomyces cerevisiae ved/biology.organism_classification_rank.species lcsh:R lcsh:Medicine Cell Biology Computational biology biology.organism_classification Budding yeast Quantitative biology 03 medical and health sciences Human health Molecular network 030104 developmental biology 0302 clinical medicine Neurology (clinical) Geriatrics and Gerontology Model organism Cell aging 030217 neurology & neurosurgery |
| Zdroj: | Translational Medicine of Aging, Vol 4, Iss, Pp 151-160 (2020) |
| ISSN: | 2468-5011 |
| Popis: | Aging is a complex, yet pervasive phenomenon in biology. As human cells steadily succumb to the deteriorating effects of aging, so too comes a host of age-related ailments such as neurodegenerative disorders, cardiovascular disease and cancer. Therefore, elucidation of the molecular networks that drive aging is of paramount importance to human health. Progress toward this goal has been aided by studies from simple model organisms such as Saccharomyces cerevisiae. While work in budding yeast has already revealed much about the basic biology of aging as well as a number of evolutionarily conserved pathways involved in this process, recent technological advances are poised to greatly expand our knowledge of aging in this simple eukaryote. Here, we review the latest developments in microfluidics, single-cell analysis and high-throughput technologies for studying single-cell replicative aging in S. cerevisiae. We detail the challenges each of these methods addresses as well as the unique insights into aging that each has provided. We conclude with a discussion of potential future applications of these techniques as well as the importance of single-cell dynamics and quantitative biology approaches for understanding cell aging. |
| Databáze: | OpenAIRE |
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