A 2025 Crick PhD project with Fabian Fröhlich (Crick) & Chris Tape (UCL).

Signalling enables cells to respond to external cues. Signalling is inherently heterogenous, which enables multicellular organisation of cells in tissues but also complicates disease treatment. This heterogeneity arises from drivers at system and molecular scales, intertwined through feedback loops, making quantitative understanding and predictions challenging. Recent studies suggest that resistance to targeted anti-cancer drugs emerges through drug-tolerant persister subpopulations. These persister cells exploit the feedback between cellular signalling and cell states to escape treatment by altering their cell state in response to signalling-targeted therapies, thereby rewiring signalling to evade treatment. The underlying cell-state transitions are transient, reversible, as well as dynamic, and are often facilitated by the co-option of developmental transcriptional programs.

The overall goal of this project is to develop combinations of targeted anti-cancer drugs that overcome drug resistance by reprogramming cellular states. We aim to achieve this by quantifying the dynamic signalling and cell state response of a panel of patient-derived colorectal cancer organoids to monotherapies using mass cytometry (CyTOF). We will then use the corresponding data and insights to develop a computational model that couples pathway models with data-driven models of cell state transitions. While the focus will be on the mitogen activated protein kinase (MAPK) signalling pathway, we will also explore crosstalk with other signalling pathways.

This project is part of an ERC-funded research program and is a collaborative effort between the Tape lab (UCL) and the Fröhlich lab (Crick). It will be planned and executed in close cooperation with cell culture, high-throughput screening, and bioprinting core facilities at the Crick to ensure scalability and reproducibility through automation. The project will include both wet-lab and dry-lab experiments, with a stronger emphasis on wet-lab work.

Candidate background

The ideal candidate should possess a quantitative mindset, an engineering view on biology and an interest in scale-up of experiments. Essential qualifications include prior wet lab experience. Additionally, experience with organoid systems, coding, dynamical systems, and/or single cell analysis is desirable.