Dr Argyris Zardilis Research Portfolio

Research

I am a computational biologist interested in the scientific and methodological challenges of understanding multi-scale processes in development. My model system is early Arabidopsis thaliana flower development, which combines biological complexity with experimental tractability.

My work spans the molecular and mechanical signals that regulate growth and shape. On the molecular side, I led the development of a spatiotemporal atlas of early flower development integrating live imaging, cell tracking, and the expression of key developmental genes. On the mechanics side, I have also developed finite element mechanical models to show that mechanical stress patterns aspects of growth in early flowers, acting both through microtubule networks and by influencing developmental gene expression.

A growing thread in my work is the development of methods for inverse problems in morphogenesis: automatically extracting mechanistic biophysical models from large-scale multi-modal datasets combining 4D imaging and sequencing data. This includes learning unified representations across data modalities and differentiable physics-based inference to discover the mechano-chemical rules governing complex developmental processes. An example of this work was work presented at ICLR 2025.

During my PhD at the University of Edinburgh, I developed Chromar, a formal language for modelling multi-scale biological systems, and used it to build a multi-scale whole-plant lifecycle model of Arabidopsis extended to the population level.

Brief Bio

I’m originally from Cyprus and I did my undergraduate in Computer Science at the University of Southampton, followed by an MPhil in Computational Biology at the University of Cambridge and a PhD with Prof Andrew Millar (Biology) and Prof Gordon Plotkin (Computer Science) at the University of Edinburgh entitled ‘Modelling language for Biology with applications’.

Science communications and outreach

I love talking to people about science and regularly volunteer for public outreach events at the Sainsbury Laboratory, including Cambridge Festival, Festival of Plants, Big Biology Day and Open Cambridge.

I participated in the Gatsby Plant Science Summer School in 2022, which introduces first-year university students to the varied opportunities of plant science careers, through talks from scientists, career sessions, workshops and discussions. As part of the programme, I shared what I do at the Sainsbury Laboratory, where I am developing new model frameworks to better understand plant morphogenesis.

In the above video, produced for the Gatsby Plant Science Education Programme, I talk about how mathematical frameworks can be used to study a particular system or process, such as plant morphogenesis - the science of how cells grow together to form complex shapes and structures.

Publications

Zardilis, A., Budnikova, A., Jönsson, H. (2025). Learning a mechanical growth model of flower morphogenesis, preprint. https://doi.org/10.1101/2025.02.27.640522

Riglet, L., Zardilis, A., Fairnie, A. L. M., Yeo, M. T., Jönsson, H. and Moyroud, E. (2024). Hibiscus bullseyes reveal mechanisms controlling petal pattern proportions that influence plant-pollinator interactions. Science Advances 10(37). https://doi.org/10.1126/sciadv.adp5574

Refahi, Y., Zardilis, A., Michelin, G., Wightman, R., Leggio, B., Legrand, J., ... & Traas, J. (2021). A multiscale analysis of early flower development in Arabidopsis provides an integrated view of molecular regulation and growth control. Developmental Cell, 56(4), 540-556. https://doi.org/10.1016/j.devcel.2021.01.019

Nelson, S., & Zardilis, A. (2021). Power and Responsibility: The Role of the Sciences in Reducing Social Inequality.  https://doi.org/10.17863/CAM.66531

Zardilis, A., Hume, A., & Millar, A. J. (2019). A multi-model framework for the Arabidopsis life cycle. Journal of experimental botany, 70(9), 2463-2477. https://doi.org/10.1093/jxb/ery394

Honorato-Zimmer, R., Millar, A. J., Plotkin, G. D., & Zardilis, A. (2019). Chromar, a language of parameterised agents. Theoretical Computer Science, 765, 97-119. https://doi.org/10.1016/j.tcs.2017.07.034