Lead Chief Investigator: Daniel Ortiz-Barrientos, The University of Queensland

Collaborating Chief Investigators: Mark Cooper, Steven Smith, Tim Brodribb, Kevin Burrage, Diane Donovan, Christine Beveridge, Barbara Holland, Ian Wright


How adaptations originate and are maintained remains a fundamental question in biology.  

Using a combination of genetic mapping, transcriptomics, physiology, and mathematical and computational experiments, we aim to learn how biological systems evolve and remain successful over time. 

We will discover functional connections between hormonal pathways known to participate in different aspects of plant growth, development and reproduction. 

Our team’s expertise will allow us to efficiently transfer this knowledge to model systems such as Arabidopsis and pea, and ultimately to agricultural crops such as sorghum, sunflowers and maize. 

This research will help mathematicians create a powerful language (and tools) to bridge plant success in nature and in artificial systems. This language will reveal insights into how evolutionary quantitative genetics illuminates both the origin and maintenance of adaptations, but also how it converses with the role of quantitative genetics in breeding practices. 

It will help experimentalists to learn about the genetics of well-known traits across a variety of physiological, morphological and reproductive strategies, while providing new knowledge on how plant performance arises through the integration of these strategies. 

It will bring together experimentalists and theoreticians across multiple hierarchies of organisation, thus building an integrative approach to the discovery of plant success. 

And by working in Senecio, an Australian native daisy, we will produce new world-class genomic and genetic resources for future work on plant physiology, development and evolution. 

Our approach

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.