Hadjivasiliou Lab | Patterning in dynamic environments

THE FRANCIS CRICK INSTITUTE LIMITED; Greater London; On-site; GBP 18,000 - 24,000; Full time

Job summary

The Francis Crick Institute in London hosts a Crick PhD project led by Zena Hadjivasiliou, focusing on patterning in dynamic environments. The project aims to build theoretical models that couple tissue dynamics with morphogen signalling, guided by experimental observations and collaborations.

The student will develop techniques in HPC, numerical methods for ODEs/PDEs, and machine learning to interpret data and predict tissue-level emergent properties such as robustness and scaling.

Qualifications

• Background in mathematics, physics, computer science, engineering or related discipline.
• Strong interest in bridging physics, computational and mathematical biology, developmental and evolutionary biology.
• Some experience in computational coding is strongly encouraged.

Responsibilities

• Collaborate with experimental colleagues and work directly with experimental data.
• Develop theoretical frameworks to predict tissue growth, cell movement and morphogen interpretation.

Skills

Mathematics

Physics

Computer science

Engineering

Biology interest

Computational coding

Tools

High Performance Computing

Machine Learning

ODE/PDE solvers

Job description

Hadjivasiliou Lab | Patterning in dynamic environments

Research topics

A 2027 Crick PhD project with Zena Hadjivasiliou.

Project background and description

A hallmark of embryo development is the generation of spatial patterns guided by morphogen gradients and gene regulatory networks (GRNs). This has led to an extensive body of work that explores the interaction between long-range signalling molecules and cellular response. However, we understand little about the physical mechanisms via which cellular and tissue dynamics contribute to spatial patterning, and emergent properties due to feedback between cell behaviour, such as proliferation and movement, and cellular signalling. The focus of this project is to develop theoretical frameworks, grounded in and coupled to experimental observations, to advance our understanding of how tissue organization and cell dynamics interact with biochemical patterning.
The student will build on theoretical tools developed in the Hadjivasiliou lab to make predictions about how tissue growth, cell movement and proliferation impact the interpretation of extracellular signals, and how feedback between intracellular signalling and cell dynamics drives emergent properties at the tissue-level such as robustness and scaling. The project will involve collaborations with experimental colleagues, offering opportunities to work directly with experimental data.
The host lab will provide training in techniques necessary for the project such as best coding practices, High Performance Computing, analytical and numerical techniques to solve ODEs/PDEs and investigate dynamical systems, and Machine Learning for data analysis. More broadly, the student will develop the skills to independently apply physics and mathematics to biological questions and work collaborative at the interface of these disciplines.

Candidate background

This project will suit candidates with a background in mathematics, physics, computer science, engineering or related discipline and an interest in biology. The ideal candidate will have a strong interest in bridging the fields of physics, computational and mathematical biology, developmental and evolutionary biology, and a willingness to work with experimental data and interact with biologists. Some experience in computational coding is strongly encouraged.

References

1. Autorino, C., Khoromskaia, D., Harari, L., Floris, E., Booth, H., Pallares-Cartes, C., . . . Petridou, N. (2025)

Preprint: A closed feedback between tissue phase transitions and morphogen gradients drives patterning dynamics.

Available at: bioRxiv

https://www.biorxiv.org/content/10.1101/2025.06.06.658228v1.full.pdf

2. Stapornwongkul, K.S. and Vincent, J.P. (2021)

Generation of extracellular morphogen gradients: the case for diffusion.

Nature Reviews Genetics 22: 393–411.PubMed abstract

3. Serrano Nájera, G., Plum, A.M., Steventon, B., Weijer, C.J. and Serra, M. (2025)

Control of tissue flows and embryo geometry in avian gastrulation.

Nature Communications 16: 5174.PubMed abstract

4. Spiess, K., Taylor, S.E., Fulton, T., Toh, K., Saunders, D., Hwang, S., . . . Verd, B. (2024)

Approximated gene expression trajectories for gene regulatory network inference on cell tracks.

iScience 27: 110840.PubMed abstract

5. Romanova-Michaelides, M., Hadjivasiliou, Z., Aguilar-Hidalgo, D., Basagiannis, D., Seum, C., Dubois, M., . . . Gonzalez-Gaitan, M. (2022)

Morphogen gradient scaling by recycling of intracellular Dpp.

Nature 602: 287–293.PubMed abstract