Gene Regulatory Network Evolution
Using a combination of molecular genetic manipulations/analyses and experimental evolution, within the context of GRNs, I explore two central questions:
- How does novelty arise in evolution?
- Does environmental change drive genome complexity, and if so how?
Under specific ecological selection we can observe rapid and repeatable evolutionary re-wiring between different genetic networks. This confers robustness to networks and presents the opportunity for genetic innovation to evolve. Changeable environments have been identified as a likely driver to facilitate the evolution and expansion of GRNs. My research identifies genetic and environmental drivers of novel gene regulator recruitment within networks, and aims to understand whether more complex GRNs promote survival and create opportunities for innovation in changeable environments.
What makes evolution sometimes, but not often, repeatable? When determining causes of bias, we would optimally utilise a model system that evolves in an extremely parallel manner i.e. repeatedly via the same nucleotide substitution across independent lines.
We are establishing a model system to begin to understand the components that impact the predictability of evolution by comparing two strains of bacteria of the same species (Pseudomonas fluorescens) that show two very different types of adaptive routes to the same selective challenge – one repeatable the other variable. Both bacteria evolve the same phenotype (motility) via mutations in the same gene networks. However, one strain shows high repeatability, such that the same point mutation in the same gene is consistently realised across independent populations; the other strain shows greater variability with different mutations realised across independent populations. Why do we see repeatable evolution in one genetic background and not the other?
Experimental Evolution of Cancers
Evolutionary processes play a central role in the development, progression and response to treatment of cancers. The current challenge facing researchers is to harness evolutionary theory to further our understanding of the clinical progression of cancers. Central to this endeavour will be the development of experimental systems and approaches by which theories of cancer evolution can be effectively tested. My research aims to adapt experimental evolutionary techniques to address fundamental evolutionary processes in cancers and provide quantitative data that will enable the evolutionary dynamics of cancers to be reliably estimated.