Math-Biology REU

2019 projects

Honey bee health – Analyzing virus transmission and social immunity in complex societies
Lead mentor: Dr. Olav Rueppell
Honey bees (Apis mellifera L) are of significant ecological and economic importance and present excellent experimental study systems. Usually, one reproductive queen lives with thousands of female workers in a cohesive colony, coordinated by a complex communication and division of labor system. The bee colony represents in many regards a functional unit that can be compared to a superorganism. Thus, the colony is a dense, integrated network of individuals, which makes it susceptible to diseases. Recently, honey bee health has declining dramatically, threatening the pollination services that the apicultural industry provides. Social network structure is known to be affected by disease but many critical parameters have not been empirically and theoretically evaluated. Building on previous work, we will investigate IAPV transmission dynamics and pathogen evolution in small groups of honey bees to better understand the dynamic of a IAPV outbreaks and individual infection risk. This work will contribute to understanding of host – virus interactions in social groups and to improving honey bee health.

Estimation of recombination rates in bacteria
Lead mentor: Dr. Louis-Marie Bobay
Bacteria are non-sexual organisms that reproduce by clonal division. However, these organisms have the ability to exchange small fragments of DNA through recombination, and this process frequently confers microbes the ability to adapt rapidly to changing environments and medical treatments. Recombination is largely responsible for the spread of antibiotic resistance genes and pathogenicity factors across bacteria, but despite its central role in bacterial adaptation, the recombination process remains poorly understood. The frequency and patterns of recombination vary drastically across species and methods to accurately quantify the rates of DNA transfers are needed.

This project aims to estimate recombination rate by using empirical estimates of Linkage Disequilibrium (LD) across a set of 153 species of bacteria. LD measures the association between alleles along genomes, and this approach is frequently used to estimate recombination rates in sexual organisms, but little work has been done to apply this approach to bacteria. Building on previous works from the lab and other research groups, the students working on this project will develop a mathematical model to infer recombination rates from LD data in bacteria. These results will help understand how recombination rates vary across bacterial species and along their genomes.