Engineering Robustness: Using Synthetic Biology to Unravel and Enhance Evolutionary Dynamics in Gene Networks

The difference between whether populations survive or go extinct during environmental shifts depends on whether the rate of adaptation can keep up with the rate of change. All living organisms rely on huge numbers of genes working in concert to support a variety of functions. Many of these genes are organised into networks, which can be controlled collectively. These networks have evolved a staggering complexity and can be made up of numerous interacting genes, molecular products, and regulatory elements, that together enable an organism to exquisitely respond to changes in their environment. Changes to these networks facilitate rapid adaptation because single mutations can impact many genes simultaneously. However, we don't understand how network structures themselves evolve, and the implications that these have on an organism's 'evolvability' and ability to adapt. The ability for evolution to rescue cell motility has been observed where the correct regulation of the flagella motor has been forcefully broken, allowing potentially to establish rules for how some regulatory networks are easier to rewire and evolve than others. Using synthetic biology to create our own regulatory networks from scratch, we can for the first time explicitly test these rules in new ways. Specifically, this project aims to generate numerous synthetic gene regulatory networks that under selection have the potential to rescue a desired phenotype. Using our 'rulebook', we have a priori predictions as to which network structures are best suited to this task and through experimental evolution, we can assess whether the rules hold more generally, providing deeper insight into how evolvable specific organisms and phenotypes are, the role of such rewiring to generate evolutionary innovations when new challenges are faced, and potentially harnessed to create synthetic living systems that can evolve in predictable ways.