Christine Anne Beveridge

This study identifies the miR319-targeted TCP module as a positive regulator of shoot branching in Arabidopsis, linking plant architecture to carbon and nitrogen availability.

The paper develops a parameter-free model of shoot branching using a causal network derived from literature, achieving 86% accuracy in predicting branching direction from 78 perturbations.

FLASH-P constructs causal regulatory networks from literature, predicting perturbation outcomes with 90% accuracy across seven species.

PSoup is an R package that converts knowledge graphs into qualitative, parameter-free models to predict biological network behavior under perturbations.

The paper develops a gene-to-phenotype network for shoot branching in plants to explore how genetic canalization affects selection responses and trait prediction across different contexts.

The paper explores using crop models as gene-to-phenotype link functions to optimize selection in plant breeding programs by integrating quantitative genetic theory and trait modeling.

The paper develops genetically encoded fluorescent biosensors for detecting strigolactones using plant receptors, showing specific fluorescence changes in response to strigolactones and their analogs.