The evolution of genetic networks during rapid adaptation

Genetic networks consist of interactions between genes and their products. Such interactions modulate molecular phenotypes, like cascading signals that create chemical gradients, and the levels of hormones that affect traits throughout development and physiological responses. The amount of genetic variation that sits on these nodes and edges remains largely unknown, and therefore theories on the genetics of adaptation still rely on the simplest genotype-to-phenotype map, or G to P map. In this project, you will explore how different genetic network structures evolve during adaptation. Your work will set a novel foundation for our understanding of the G to P map and its influence on the response to selection. As we meet the challenges of climate change, your work will help us understand the meaning of population resilience in the Anthropocene and will provide insights into thinking models to predict trajectories of adaptation in the next hundred years of population evolution

You'll join the School of The Environment as a PhD student in the ARC Centre of Excellence for Plant Success in Nature and Agriculture. This offers access to an outstanding intellectual environment where transdisciplinary research thrives. Here, mathematicians and biologists collaborate to tackle fundamental biological problems, exploring how adaptation governs biodiversity patterns and informs decisions on novel food sources and phenotype extinction. In our lab, we prioritize well-being, respect for ideas and personal boundaries, and your growth as a researcher. We uphold the highest standards of scientific integrity and rigor while fostering mutual respect for personal identities. Our commitment is to create an inclusive, supportive research culture that values integrity, inclusivity, and international excellence.  Visit www.ortizbarrientoslab.org to learn more about our research culture.  

A working knowledge of evolution would be of benefit to someone working on this project.

A desire to learn mathematical biology or computational biology would be a plus to the application.

We are committed to building a diverse and inclusive community and strongly encourage candidates from underrepresented groups to apply.