Deep Learning for Crop Design (MORRIS_J19DTP1)
- Research Area Frontier Bioscience
- Partner The John Innes Centre (JIC)
Prof Richard Morris -
- Application Deadline 26/11/2018
To meet future food demands we need to be able to design our crops to produce more outputs for less inputs and to adapt to future climates and growing conditions. Targeted genome editing provides the technology to change any gene we want but we need to know which genes to engineer and why. To provide a rational framework for crop design we need to be able to precompute how changes in genes will affect the overall growth and development of a plant for given environmental conditions. The equation Genotype x Environment = Phenotype hides one of the most complex grand challenges in biology beneath its apparent simplicity. This project will exploit recent advances in AI and machine learning to address this challenge on the example of stem elongation in wheat. You will join a fun and productive interdisciplinary team of leading researchers to infer gene regulatory networks, use the latest AI approaches for image processing of growing plants, and deep learning to interrogate large datasets to find patterns between gene expression, plant development and environmental conditions. Training in wheat genetics, molecular biology, high-level programming, machine learning, bioinformatics and key transferable skills are offered as part of this exciting project as well as the opportunity to engage directly with relevant industries.