ADD THESE DATES TO YOUR E-DIARY OR GOOGLE CALENDAR
This site is part of the Siconnects Division of Sciinov Group
This site is operated by a business or businesses owned by Sciinov Group and all copyright resides with them.
ADD THESE DATES TO YOUR E-DIARY OR GOOGLE CALENDAR
.jpeg.jpeg)
The Harker School, USA
Title:Characterizing Longevity Therapies Impacts on Aging-Associated Decay of Transcriptional Order
As an increasing percentage of the global population is older than 65, understanding aging’s effects is
becoming increasingly relevant. It’s understood that aging changes the transcriptome, but which changes
are maladaptive remain unknown. Through evaluation of cell type balance, differential expression,
transcriptional noise, and drift variance, I assessed and compared which effects were reversed with two
methods of lifespan extension: exercise and parabiosis. I identified two cell types, microglia and
oligodendro cells, and four gene sets, central nervous system, signal release from synapse,
neurotransmitter secretion, and potassium channels, that may serve as indicators of older tissues through
cell type balance and transcriptional drift variance analysis. I also determined transcriptional noise to be
less effective for identifying transcriptomic age in comparison to other metrics I examined. Overall, my
findings demonstrate the potential relationship between transcriptional drift and cell type balance and age
and indicate areas of further investigation for future research.
Melody Yin is a senior at The Harker School in San Jose. She first discovered her interest in computer science in 6th grade, when she taught herself Python and Java. Her research focuses on bioinformatics, particularly the area of transcription initiation regulation and genetics. However, her research interests span other areas as well, such as concussion diagnosis systems; her first research project was influenced by her experiences as a student-athlete, developing a hybrid machine-learning model to predict recovery time from sports-related concussions. Since then, she has focused on developing statistical and machine learning-based methods to interpret genetic variation through the lens of gene regulation and transcription initiation through the lens of aging. She is extremely passionate about applying computer science and machine learning in research in a way that effectively enhances the scientific community's understanding of transcription initiation regulation and the genome.