Artificial Intelligence

Engineering plant cells circuitries for multiparametric high-throughput platforms for weed control (EngCiWeCo)

University College London

Not stated

Location
London, United Kingdom
Funding
Competition Funded PhD Project (Students Worldwide)
Application deadline
1 December 2026

About the project

About the Project Rationale and Importance. Developing physiologically relevant screening platforms for weed-control compounds in adult plants is slow, low-throughput, and requires large chemical inputs. This multidisciplinary ICASE project addresses this challenge by engineering plant cells systems as programmable platforms for high-throughput, mechanism led-discovery of sustainable weed-control chemistries. It will develop lead-generation tools to test how cellular responses to herbicides translate to whole plants. The approach will identify target-active hit molecules, building lead series, and shift Mode-of-Action (MoA) inference from correlation to mechanistic validation. Fit to BBSRC remit. The project will develop an engineering biology toolkit for translation and application, using precision genome engineering to create functionally modified cells and enabling technologies. It aligns strongly with transformative technologies, sustainable agriculture and food and and clean growth via reduced chemical inputs. Partnering with SYNGENTA, the work supports environmentally sustainable compound development with clear commercial pathways and market impact. Background. Cultured plant cell suspensions are model systems for engineering biology, enabling studies of metabolism, high-value compound production, and cell-cycle in uniform populations. Their homogeneity reduces tissue-specific variability, allowing direct sampling of the culture medium, which mimics the apoplast, for analysis of the secretome. This enables profiling of cell-wall remodelling, stress responses, and intercellular signalling. Protoplasts from these cultures are valuable because they can be transformed, regenerated, and imaged in high-throughput screening. Exploiting these advantages requires specialised expertise to engineer cultures/ protoplast systems and overcome barriers to transformation in diverse crops/ weeds. This 4-year project leverages the synergic expertise of the Devoto and Mott labs at RHUL and UCL, together with SYNGENTA supervisors Drs Linney and Johnston. The academic teams bring strong capabilities in plant cell culture and transformation, protoplast generation, CRISPR-Cas9 genome editing, sustainable extraction of bioactives and bioinformatics (including transcriptomics/RNA-seq) for quantitative analysis of phenotypic profiles, offering the PhD student immediate access to established know-how. SYNGENTA’s strengths in cross-functional cell-based phenotyping and fluorescence microscopy will broaden the candidate’s training. Main Aims. The project aims to identify and separate compounds by Mode-of-Action (MoA) within a single, unbiased experiment. Cell cultures and protoplasts are essential for rapid introduction of fluorescent tags, enabling single-cell resolution for high content phenotyping, such as Cell Painting Assay (CPA), and to study enhanced compound uptake following cell-wall removal. Objectives. A. Develop a rapid, automated platform for cell transformation and protoplast generation to enable early-stage, high-throughput herbicide MoA identification. B. Use molecular genetics and high-throughput imaging for mechanistic insight, producing protoplasts/plant cell lines for CPA with multi-organelle fluorescent tagging. The resulting phenotypic profiles, and changes in response to compounds, will identify gene circuitry and biosynthetic pathway effects. The platform will support future discovery of novel compounds within the SYNGENTA portfolio. Training. This 4-year project leverages the synergic expertise of academia, and industry. The academic teams bring strong capabilities in plant cell culture and transformation, protoplast generation, CRISPR-Cas9 genome editing, bioinformatics (including transcriptomics/RNA-seq), and sustainable extraction and testing of bioactives, offering the PhD student immediate access to established know-how. SYNGENTA’s strengths in cross-functional cell-based phenotyping and fluorescence microscopy will broaden the candidate’s Engineering Biology training. The student will work with scientists across disciplines such as Discovery Biologists, Weed Control Scientists, Biochemists, Molecular Biologists as well as Computational, Synthetic and Analytical Chemists. Environment. The project will take place mainly within the active Devoto laboratory research team, Department of Biological Sciences, at Royal Holloway, University of London (Egham campus), fully equipped with state of the art facilities and know-how for the project, and includes collaboration, with co-supervision by Dr Emma Linney and Dr Mark Johnston, with SYNGENTA Jealott’s hill ( https://www.syngenta.com/ ), and will incorporate a minimum 3-months industry placement (costs fully covered), where the student will gain expertise in automated liquid handling robotics, high-throughput and high-resolution microscopy techniques, cell-based phenotyping, and image/data analysis techniques including machine learning approaches. The project will benefit from further collaboration with Prof Richard Mott, at UCL, providing further supervisory support ( https://profiles.ucl.ac.uk/53101-richard-mott ). For further information on the project and about the research carried out by Prof Devoto, please visit the research pages www.Devotolab.org , https://pure.royalholloway.ac.uk/en/persons/alessandra-devoto/ ; www.linkedin.com/in/alessandra-devoto-790481310; phone +441784443184, X and Bluesky @Devotolab.

Research areas

Artificial IntelligenceGenetic EngineeringMolecular BiologyMachine LearningBioinformaticsBiotechnologyCell BiologyData Science