Aging and DNA repair in the Scheibye-Knudsen Group
What causes aging? And can we discover interventions leading to healthy aging? To answer these questions, the Scheibye-Knudsen Group uses protein biochemistry, cell biological methodologies, mouse and fly models and big-data machine.
The group utilizes state-of-the-art in silico, in vitro and in vivo analyses to (i) develop models of aging, (ii) investigate mechanisms of aging and (iii) design and test interventions leading to healthy longevity.
The application of this three-tier approach together with collaborations with multiple academic and industrial partners all over the globe gives the group a unique research platform to study the aging process.
“Our ultimate goal is to find new aging interventions enabling people to live longer and more productive lives” says Associate professor and Group Leader Morten Scheibye-Knudsen.
The group is newly started but has already co-authored a few papers on machine learning in aging research, and the characterization of premature aging disorders. More to come!
Investigating monogenic premature aging diseases
Single gene mutations can lead to diseases characterized by premature or accelerated aging. These often devastating diseases represent unique insights into normal aging. The purpose of these studies is to understand premature aging diseases in general, to discover new premature aging diseases and to develop treatments for these diseases.
Metabolic interventions in aging
Altered metabolism is a hallmark of aging. This project investigates if interventions targeting age-associated metabolic changes can alter the pace of aging in normal and premature aging diseases. To address this, we utilize next generation artificial intelligence analyses of cells, fruit flies and mice to assess the effect of interventions. In sum, this project may reveal not only a new treatment for premature aging diseases but also interventions for normal aging.
Targeting DNA repair in aging
An accumulation of irreparable DNA lesions is a central finding in aging cells and DNA repair activities appear to decrease with age. These observations allow us to target aging through two novel approaches: by re-engineering enzymes able to resolve the DNA damage and by utilizing next generation artificial intelligence algorithms to discover compounds that can stimulate DNA repair.
Using AI to characterize human aging
Big-data is emerging as a powerful tool to understand complex phenotypes. Herein, we examine very large datasets describing pathologies that occur with human aging. The goal: to discover new biomarkers and mechanisms of aging.