PlantCODE forges critical links between two emerging fields, Artificial Intelligence (AI) and Synthetic Biology. It is an initiative that brings together a small team of biologists, engineers, physicists, mathematicians and computer scientists to tackle the fundamental challenge of engineering of growing plant systems - where advanced biology and artificial intelligence techniques allow the creation of predictive multi-scale models for living systems, and DNA code that can be used to guide genome engineering and redesign of plants (https://www.plantcode.org).
PlantCODE brings together a closely integrated group of laboratories led by Haseloff (Plant Sciences: Marchantia, Synthetic Biology and OpenPlant), Richard Smith (JIC: plant microscopy, image processing and visualisation), Yadav (Plant Sciences: neural network analysis of plant biology), Hibberd (Plant Sciences: functional genomics and physiological engineering in plants), Jönsson (SLCU: computer modelling of plant development), Shönlieb (DAMTP: machine learning and image analysis), O’Leary (Engineering: neural networks and machine learning), Bakshi (Nanoscience: high throughput microfluidics and machine learning), Lio’ (Computer Sciences & Technology, AI Group: deep learning for biological systems) and Ajioka (Pathology: synthetic DNA circuits and open curriculum development. These laboratories share a sharp focus on (i) the use of a new, extraordinarily facile and simple model plant system for high throughput, multi-scale analysis of plant growth, (ii) use of deep learning algorithms to create dynamic executable models for growth, (iii) design and testing of gene editing approaches to rewire regulatory networks, and (iv) provision of open tools and materials for reprogramming biology. This work is spread across traditional disciplines of biology, engineering, physics, computer science and mathematics, but is forerunner of a new integrated approach to understanding and engineering organismal growth. The work is currently funded by research grants, collaborative research projects, shared research students and funds for interdisciplinary coordination.
More details and links to laboratory websites at: https://www.plantcode.org/people