Hadjivasiliou Lab | Patterning in dynamic environments

Crick; Greater London; Hybrid; GBP 23,000 - 27,000; Full time

Job summary

The Crick Institute invites applications for a 2027 Crick PhD project led by Zena Hadjivasiliou. The project aims to build theoretical frameworks that couple tissue growth, cell movement and morphogen signaling to predict emergent tissue-level properties, using HPC and data-driven analysis.

Collaborations with experimental colleagues will provide direct access to datasets, enabling validation of models and refinement of predictions in an interdisciplinary setting.

Qualifications

• Strong background in mathematics, physics, computer science, or engineering.
• Interest in bridging physics, computational biology and experimental data.

Responsibilities

• Develop theoretical frameworks coupling tissue growth, morphogen signaling and GRNs.
• Make predictions on how tissue dynamics affect interpretation of extracellular signals.
• Collaborate with experimental colleagues and analyze data from the Hadjivasiliou lab.

Skills

Mathematics

Physics

Computer Science

Engineering

Computational Coding

Education

BSc in Mathematics/Physics/Engineering or related

Tools

High Performance Computing

Machine Learning

Job description

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.

Looking at the research undertaken in our lab, what aspect would you be most interested in exploring further? Drawing on your own research experience, what perspective, skill or approach would you bring to investigating it?

References

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

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

Generation of extracellular morphogen gradients: the case for diffusion.

Control of tissue flows and embryo geometry in avian gastrulation.

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

Morphogen gradient scaling by recycling of intracellular Dpp.