Department
Biological Sciences
Major
Biological Sciences
Research Advisor
Scharf, Andrea
Advisor's Department
Biological Sciences
Funding Source
Missouri University of Science and Technology
Abstract
Population dynamics follows the fluctuations in birth and death rates. The effects of varying reproductive schedules and aging trajectories play important roles in population survival. Previous projects have aimed to draw connections between these traits through the analysis of data collected from laboratory ecosystems and a computational simulation (wormPOP1) comparing wild type and mutant populations of Caenorhabditis elegans. However, these data sets are extensive, and the analysis has proven to be challenging and time consuming. The goal of my project was to optimize the data analysis process. Using data that had been previously collected in these experiments, I worked to produce a data analysis pipeline using the programming language R. Additional simulations were also used to provide supplemental data for analysis. Finally, this data analysis pipeline was used to gain a deeper understanding of how the life history traits of reproduction and lifespan are connected to support population survival.
Biography
Clare Koerkenmeier is an undergraduate student at Missouri University of Science and Technology majoring in Biological Sciences. There, she is active in the SCRUBS prehealth organization. She is also a part of the Kummer Vanguard Scholars program and Honors Academy. In the future, Clare intends to attend medical school.
Research Category
Sciences
Presentation Type
Oral Presentation
Document Type
Presentation
Location
Havener Center - Carver Room
Presentation Date
10 April 2024, 1:00 pm - 4:00 pm
Data analysis pipeline of C. elegans population dynamics
Havener Center - Carver Room
Population dynamics follows the fluctuations in birth and death rates. The effects of varying reproductive schedules and aging trajectories play important roles in population survival. Previous projects have aimed to draw connections between these traits through the analysis of data collected from laboratory ecosystems and a computational simulation (wormPOP1) comparing wild type and mutant populations of Caenorhabditis elegans. However, these data sets are extensive, and the analysis has proven to be challenging and time consuming. The goal of my project was to optimize the data analysis process. Using data that had been previously collected in these experiments, I worked to produce a data analysis pipeline using the programming language R. Additional simulations were also used to provide supplemental data for analysis. Finally, this data analysis pipeline was used to gain a deeper understanding of how the life history traits of reproduction and lifespan are connected to support population survival.
