Hettie Chapman
Email: hester.chapman@hhu.de
Affiliation: Quantitative and Theoretical Biology
Heinrich-Heine-University Düsseldorf
www.qtb.hhu.de/
Education and experience:
In July 2021 I completed my Master of Mathematics at the University of Bath, United Kingdom. This was an integrated masters programme with a year in industry, meaning that I was given a strong grounding in both pure and applied mathematics before completing my masters thesis. During my third year at Bath, I studied two modules in mathematical biology, as well as numerous modules based on dynamical systems and mathematical modelling. This piqued my interest in biology, and as such my thesis was on the evolution of sexually transmitted infections to manipulate host sperm allocation.
My final year project was based on the assumption that when the females of a population mate with multiple males, sperm compete to fertilise the same egg, with females remating until fertilised. Male reproductive success can therefore be assumed to depend on sperm-allocation strategies. If a male is infected with an STI, greater ejaculate volumes also increase the likelihood of transmitting this STI. As a result, we expect STI's to evolve to interfere with male sperm allocation strategies by manipulating ejaculate volume. During the project I developed deterministic models for a host-STI system which I used to investigate the population dynamics, followed by the adaptive dynamics to look at the evolution when a rare mutant STI is introduced.
Project description:
ESR5: Modeling the metabolism of S. aureus in conjunction with the host cell
Staphylococcus aureus is a human commensal bacteria, living in up to 30% of the population harm- free. However, in a number of cases, this can lead to opportunistic pathogenic infection that causes serious harm. Among the diseases caused by S. aureus are pneumonia, bacteremia, toxic shock syndrome and meningitis. S. aureus has several antibiotic resistant strains, including methicillin resistant Staphylococcus aureus (MRSA), which causes around 10 000 deaths per year in the USA alone.
We will use genome-scale metabolic models (GEMs) to better understand the mechanisms behind S. aureus virulence, and predict novel drug targets in the bacteria. To do this, we will build GEMs for a range of S. aureus strains which have readily available fully sequenced genomes. These networks will be used to determine gene and reaction essentiality, as well as to calculate metabolic scopes. Modelling of S. aureus in conjunction with the host macrophage will be used to identify which genes and reactions are good drug target candidates.