Primary supervisor

Enes Makalic

Co-supervisors

  • Lisa Martin
  • Jane Fenelon

Background and motivation
An understanding of reproductive biology is essential to any Zoo captive breeding program. A significant part of this is understanding what steroid hormones (e.g. testosterone or oestrogen) are present at any one time so that they can be used as monitoring tools. For example, to determine when a female is receptive to mating as some females will aggressively attack and injure a male if not ready to mate. Commercial kits are available to identify individual steroid hormones via blood samples. However, there are over 50 known steroid hormones and steroid hormone usage differs between species and even within a species across their lifespans and genders. This is a particular problem for Australian native wildlife since many of the kits do not work well for marsupials likely because they are using different steroid versions. However, identifying these has been challenging to date. There are now technically improved methods available like LC-MS/MS (Liquid Chromatography - tandem Mass Spectrometry) that can accurately identify and quantify all of the steroid hormones (known and novel) present in any particular blood sample.

Aim/outline

Using the well-studied marsupial, the tammar wallaby, we have a large database of blood samples taken from throughout their reproductive cycle. We aim to run these samples on LC-MS/MS to systematically identify and quantify their steroid hormone profiles. This project will create a database of all of these samples linked to each of their steroid hormone profiles. Together with a reproductive biologist this will then be mapped back to what is known about their reproductive cycles. We also have access to historical data looking at a limited number of steroid hormones in the wallaby. Key research questions will be:

  • To what extent is this historical data reflective of the full reproductive steroid profile?
  • Are there other more relevant hormone profiles that could be used as predictive tools to identify particular reproductive stages?

 

Required knowledge

Data Science, Machine Learning, Statistics, Database Design, Software Engineering.