Project Reference: TRG27-SLB-HJ1

Project title: Using AI techniques to decipher new gene regulatory networks in the Shoot Apical Meristem

Project outline

The shoot apical meristem (SAM) is a critical tissue from which all above-ground plant organs develop, including leaves, stems, branches, and flowers. It is located at the shoot tip and contains a pool of undifferentiated stem cells that continuously divide, providing new cells for organ formation and plant growth. 

The SAM has a complex gene regulatory network (GRN) that coordinates cell division and differentiation to maintain its size. Two important components are CLAVATA3 (CLV3) and WUSCHEL (WUS), forming a negative feedback loop and suggested to be at the core of SAM maintenance. Additional identified SAM regulators include hormones (e.g. cytokinins, auxin, ABA), transcription factors such as KNOX proteins (e.g. STM), and other peptides. However, an integrated understanding of this essential organ's regulatory network is missing.

The aim of this PhD project is to identify novel regulatory connections in the SAM GRN using graph-based neural network approaches from single-cell and spatial transcriptomics data, to ultimately predict their expression dynamics within differential equation models. This work will be an invaluable contribution to the broader effort to construct a comprehensive framework of the SAM's regulatory mechanisms, ultimately enabling a fully integrated computational model of the SAM with predictive power to guide engineering of plant traits. 

There are three main objectives. Firstly, to identify new elements in the SAM GRN, we will develop and adapt artificial intelligence pipelines based on graph neural networks to predict regulatory interactions from SAM single-cell and spatial RNA sequencing data. The second goal is to predict the expression dynamics of these new elements of the regulatory network using mechanistic modelling and utilising template information from a Cell Atlas of the SAM. Third, the model will suggest perturbations that lead to specific phenotypes, to guide an informed path to engineering plant traits.

Principal Supervisor

Professor Henrik Jönsson

Email address: henrik.jonsson@slcu.cam.ac.uk

Department / Partner Institute: Sainsbury Laboratory

Website:  https://www.slcu.cam.ac.uk/research/research-group/jonsson-group

BBSRC strategic theme

Understanding the rules of life