We will develop new techniques for handling the diversity and complexity of genetic networks, using rigorous mathematical and computational approaches to identify key features within the data and the interactions between these features. 

These techniques will deliver stability of network analysis under varying evolutionary and environmental pressures. 

The development of these tools requires outstanding knowledge in numerical analysis and associated computational algorithms. 

Using network theory to reduce underlying complexity, we will produce models capable of connecting plant mechanisms and genomes for prediction, especially across distinct plant groups and across different environments. 

Our work will provide a quantitative computational bridge across models developed to study diverse native plants and crops in varying environments, and could lead to the development of new frameworks for applications in agriculture and in the conservation and management of natural systems.