Math-Biology REU

2018 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. Multiple disease agents have been identified and we will study an important virus, Israeli Acute Paralysis Virus from a practical and theoretical perspective. We will investigate IAPV transmission in small experimental groups of honey bees with varying transmission routes to understand the dynamic of a IAPV outbreak and individual infection risk. This work should contribute to understanding of honey bee – virus interactions and help improving honey bee health.

Vaccination Game Theory
Lead mentor: Dr. Igor Erovenko
As witnessed by the recent outbreak of measles, there is a gap between interest of the individuals and the interest of the population as a whole. From the individuals’ perspective, the benefits of vaccination (i.e. not getting the disease) may not be high enough to outweigh the cost of the vaccination (i.e. potential vaccine side effects) especially when majority of the population is vaccinated that the disease outbreak seems highly unlikely. Such scenarios are successfully modeled by game theory. During the Summer 2018, our team will work on developing game theoretical models for diseases of students’ interest as well as try to extend the results for the spatially structured populations.

Evolution of life history traits
Lead mentor: Dr. David Remington
We will study the genetic basis for adaptive evolution of life history traits in plants, which has important implications for how plants will respond to climate change. We use the perennial rock cress (Arabidopis lyrata) as a model system to study the evolution of perenniality in response to variation in climate. Our empirical research has found that trade-offs in reproduction vs. vegetative growth in populations from the warm vs. cool extremes of the A. lyrata range (North Carolina vs. Norway) result from quantitative trait loci (QTLs) that affect aspects of perenniality. North Carolina alleles lead to greater reproductive output at the expense of vegetative growth, but reduce survival without increasing reproduction under Norway conditions. Previous research by UNCG undergraduates found that genetic differences between the extreme populations affect the allocation of time to vegetative vs. reproductive growth on individual shoots. We proposed a conceptual model to explain reproductive output and survival differences between populations in different environments, and thus a potential mechanism for local adaptation. We will explore the behavior of a deterministic model incorporating relationships between trait values, fitness components, and environmental variables using differential equations. Students will develop mathematical representations of the model, find parameter values that optimize fitness under different climate conditions, and predict the trajectory of natural selection under changing climates.

Detection of DNA transfers in bacteria
Lead mentor: Dr. Louis-Marie Bobay
Bacteria reproduce clonally by transmitting near-exact copies of their genome from one generation to the next. Mutations are the primary source of genomic alterations but secondary modifications arise by the exchange of DNA across bacterial cells through a mechanism named homologous recombination (i.e. gene flow). Transfers of DNA are frequently involved in the transmission of adaptive traits with important societal implications, such as antibiotic resistance and pathogenicity. Bacteria are known to recombine at diverse rates, but methods estimating the rates and patterns of gene flow frequently give inconsistent estimates. One key challenge is the development of algorithms able to distinguish true recombination events from multiple independent mutation events that mimic recombination (i.e. false positives). In this project, we will investigate how different parameters such as mutation rate, mutation spectrum and nucleotide composition, affect the rate of false positive detection. By simulating bacterial evolution in silico and by analyzing large genomic datasets, our team will aim at predicting the expected number of false positives for given sets of parameters. This work will contribute to improve current methods aiming at detecting and quantifying gene flow and its impact on bacterial evolution.