Lead Chief Investigator: Steven Smith, The University of Tasmania

Collaborating Chief Investigators: Tim Brodribb, Christine Beveridge, Daniel Ortiz-Barrientos, Diane Donovan, John Bowman, James Weller, Graeme Hammer, Peter Waterhouse, Ian Wright, Mark Cooper, David Jordan


In plants, the effective allocation of resources—especially carbon, nitrogen and water—underpins yield potential in crops and plant resilience and reproduction in natural environments.  

Control of these resources is mediated by resource signalling (for example, sugar signalling) and hormones, acting through transcription factors to influence development. Sugars provide signals, energy and osmolytes for growth, while hormones integrate endogenous and environmental signals to control growth.  

Sugar transporters are of central importance, while other control factors include FT and miRNAs. 

Our aim is to identify the molecular networks and drivers that control shoot growth under variable conditions, by analysing transcriptomes together with measurements of key resources and physiological factors, such as sugars, water and hormones. 

Using this information, we will create models that predict shoot growth based on easily measured parameters. One goal is to link leaf physiology to lateral bud outgrowth, as the control of tillering in cereals and branching in dicots are vital characters. 

Our approach

We will investigate the networks that control resource acquisition, allocation and utilisation during the plant’s growth and development. Our focus is on transitions, including the outgrowth of lateral buds and the switch from sink to source in developing leaves. These processes play major roles in determining shoot architecture, productivity and plant success. 

In our experiments, we will use pea as a model system, since axillary buds and leaves follow a well-defined developmental program and pea tissues are readily accessible for gene expression and biochemical analyses.  

We will extend our work to cereals to provide a comparative approach and application to major crops.  

Our research will closely integrate with other projects linking physiological studies with genetic and mathematical approaches to derive predictive models of growth