Within the Centre for Plant Success, the theme "Facilitating discoveries through novel design principles, mathematics, and technologies" develops mathematical and computational approaches that advance our understanding of plant success. This theme serves as a bridge between themes focused on understanding underlying gene regulatory networks and those focused on practical agricultural applications.
We seek to learn from evolutionary patterns of adaptation by understanding how plants have independently evolved similar “solutions” to similar environments. Application of multi-response phylogenetic mixed models provides a sophisticated statistical framework to analyse trait evolution and correlation patterns. For example, using eucalypts as a model system, we have uncovered a coordinated shift in functional traits associated with replicated evolution into arid environments. The group is developing phylogenetic G2P methods based on hidden Markov models to identify genomic regions associated with adaptive traits, alongside methods for analysing presence-absence patterns in sequence data for evidence of convergent evolution. We are also conducting a large-scale study of the comparative transcriptomics of adaptation to aridity. We have been using PlantSage, our rapid genome, epigenome, genetic trait discovery, validation, and editing system (based on the Australian native, Nicotiana benthamiana), to understand and engineer differing drought resilience strategies.
We seek to enhance the Agricultural Production Systems Simulator (APSIM) platform in relation to modelling and prediction of the physiology and genetics of key adaptive traits (branching/tillering; phenology; high temperature and water limitation responses) in field crops. The ongoing development of APSIM affords a predictive link between the functional physiology underpinning key adaptive traits, their genetic variability, and consequences on plant/crop scale phenotypic outcomes in target production environments. These enhancements are linked closely with G-P modelling and prediction activities in Theme 5.
The group also develops sophisticated approaches for working with mechanistic and crop growth models, including techniques in model calibration with uncertainty, emulation, and optimal control for maximising desired model outputs. This research extends to establishing the predictivity of the parameter-free models in use within the Centre, analysis of genetic networks using hypergraphs, and improved identification of evolutionary pressures in the context of genetic drift.
Technological innovations advanced within the theme include development of novel gene editing technologies, and the use of miniaturised cameras to do fine scale monitoring of plants water-use in the field.
