Project description
Organismal development is an incredibly dynamic and sensitive process and for many species it is occurring in an increasingly volatile environment. Yet within a species, there exists significant inter-individual variability in the development of both an embryo’s form and function and this can have significant implications for both individual organisms and evolutionary processes. This PhD studentship will use high-throughput bioimaging and deep learning to investigate the extent to which biological variation in unstable environments can be used to predict later biological processes, sensitivities and outcomes. The overarching goal is to test the predictive capacity of variation in developing organisms. Phenomics, is the acquisition of high-dimensional data on an organism wide scale and will be an approach central to this research. Using the techniques established in the EmbryoPhenomics research group you will acquire high temporal, spatial and functional resolution data describing changes in the form and function of developing embryos. Deep learning will then be applied to testing the capacity of these data to predict subsequent biological responses of individuals, ranging in timescale from hours to days, and even months.
You will be working in the dynamic Ecophysiology and Development Research Group performing both high-throughput phenotyping experiments and using these to inform and test models trained using deep learning. The study system for this research will be the great pond snail Lymnaea stagnalis, and you will be capitalising on instrumentation and computer vision pipelines already created by the team. Skills developed during this studentship will include embryology, computational biology, deep learning and high-throughput phenotyping.