Scholars' Mine - Undergraduate Research Conference at Missouri S&T: Data analysis pipeline of C. elegans population dynamics
 

Data analysis pipeline of C. elegans population dynamics

Clare Koerkenmeier, 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.

 
Apr 10th, 1:00 PM Apr 10th, 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.