Decoding Dynamic Plant Meristems

About the project

About the Project All aerial parts of a plant originate from a small population of pluripotent cells within the shoot apical meristem (SAM). Far from being a static structure, the SAM is continuously changing in size, shape and cellular organisation as the plant develops. One of its most apparent transitions occurs during the reproductive phase change, when the SAM switches from producing leaves to producing flowers. This transition requires substantial energy and coincides with increasing levels of carbon. However, how carbon availability is connected to the cellular reorganisation of the SAM remains poorly understood . This project aims to understand how the SAM dynamically responds to changing carbon availability during the floral transition. Rather than studying individual genes or pathways in isolation, the project will integrate carbon signalling with established networks controlling flowering time, meristem maintenance and hormone signalling. This will provide a systems-level view of how changes in carbon availability are translated into coordinated changes in gene expression and SAM architecture. The ultimate goal is to develop an interactive, cell-resolution model of SAM dynamics that can be used to generate and test new hypotheses about developmental transitions. The project will use the model plant Arabidopsis as a powerful experimental system, while developing knowledge and approaches that can ultimately be transferred to crop species, where developmental transitions of meristematic tissues are directly linked to yield performance. The project will address three key questions: How do carbon levels, SAM morphology and gene expression change throughout the floral transition? Which cells and regulatory factors coordinate the communication between different SAM domains as the transition occurs? Can quantitative experimental data be integrated into a dynamic model that predicts how carbon, hormones and gene regulatory networks interact to control SAM development? The project combines advanced microscopy, molecular biology, metabolite analysis, transcriptomics and computational modelling . The student will use three-dimensional high-resolution live microscopy to quantify changes in SAM architecture and combine this with fluorescent reporters and FRET-based biosensors with high spatial and temporal resolution. Transcriptome sequencing approaches will be used to determine how gene expression changes during the floral transition. These datasets will be integrated with information on metabolites and hormones to identify relationships between carbon status, regulatory networks and changes in SAM organisation. A particularly innovative aspect of the project will be the integration of these experimental datasets into a spatial computational model . AI-assisted hypothesis generation will be used to identify potential regulatory relationships, which can then be experimentally tested. The resulting data will iteratively refine the model, creating a dynamic framework in which experimental observations and computational predictions inform one another. The student will therefore work across biological scales — from individual cells and molecules through to the architecture of the whole SAM — and gain experience of how modern quantitative and computational approaches can be combined to answer fundamental biological questions. The project will be iterative by design: experimental observations will inform the model, model predictions will generate new hypotheses, and new experiments will test those predictions . This project is ambitious, and will leverage from shared experimental and theoretical method development already ongoing in the host groups. The project offers an unusually strong opportunity for the student to develop as an independent scientist bringing together several disciplines and addresses a biological system whose dynamics cannot be fully understood from a single experimental perspective. The student will not simply generate datasets for a predefined model. They will be encouraged to identify patterns in their own data, formulate hypotheses about how individual cells and SAM domains communicate, and work with the computational team at SLCU to translate these ideas into testable predictions. AI-assisted hypothesis generation will provide an additional route for identifying unexpected relationships that can be experimentally investigated. As the project progresses, the student will therefore have increasing influence over which regulatory mechanisms are experimentally prioritised and how the model develops. They will learn to move between quantitative observation, hypothesis generation, computational prediction and experimental validation — an increasingly important approach across modern biological research. The supervisory team brings together complementary expertise in plant developmental biology, nutrient signalling and quantitative computational modelling . The primary supervisor, Dr Vanessa Wahl (JHI) , is an internationally recognised plant developmental biologist with expertise in nutrient-dependent regulation of SAM development and growth, including breakthrough work on the molecular control of SAM activity and flowering. Her fundamental research is increasingly being translated to potato. Prof Henrik Jönsson (SLCU) is a leading expert in computational morphodynamics , with extensive experience integrating genetic, hormonal and mechanical regulation into quantitative, cell-resolved models of the Arabidopsis SAM. Interested students are encouraged to get in touch with the supervisory team before the application deadline , either through the FindAPhD platform, directly by email ( vanessa.wahl@hutton.ac.uk ), or via LinkedIn , to discuss the project and their suitability. Applications should include a CV and a short motivation letter explaining the applicant’s interest in the project, how their background and interests align with the research, and why they consider themselves well suited to undertake the PhD. We particularly welcome applications from students who are curious, motivated to develop their own scientific ideas, and excited by the opportunity to work at the interface of plant developmental biology and computational morphodynamics.

Research areas