We will focus on two types of traits:  

• traits for which well-known genetic and regulatory networks exist 
• traits for which knowledge of network structure remains nascent, yet their role in adaptation is well known.  

The proposed traits include flowering time, branching, water-potential dynamics, water-sink strength, and gravitropism. 

The Senecio system includes multiple ecotypes with contrasting performance in flowering time, branching patterns, exposure to contrasting water regimes, growth habits, and where interactions with stressors such as salinity and drought exist. This system provides a powerful framework to understand how plant strategies have evolved over hundreds of thousands of generations, and how the genetic information they carry is functionally redundant across replicates of the evolutionary process. 

To make sense of the role of networks (known and discovered), we will use powerful computational and mathematical approaches to investigate the robustness of biological systems as they have evolved independently from one another. This will reveal the potential constraints and flexibility of complex traits as they evolve, and will inform rules of functional assembly for future perturbation experiments. 

Experiments in the laboratory, glasshouse, controlled-environment rooms, and in the field using natural variation will help us to reverse-engineer the genotype-to-phenotype map responsible for the response to selection.  

We will expand our tests to Arabidopsis and pea, using gene-editing approaches to help us test predictions about how genes control traits and fitness variation.  

Over time, we will explore transgenic approaches in Senecio, as well as the possibility of transferring functional information to crop model systems.