Fröhlich Lab | Deep Mechanistic Models for the Prediction of Dynamic Cell Fates

A 2024 Crick PhD project with Fabian Fröhlich. This application is open until 12:00 noon on 09 November 2023.
Deadline for applications has passed.

Key information

Applications closed
09 November 2023, 12:00 GMT
Posted 14 September 2023

Research topics

Computational & Systems Biology Signalling & Oncogenes Imaging Biochemistry & Proteomics
Background texture taken from the lab imagery.

A 2024 Crick PhD project with Fabian Fröhlich. 

Project background and description

Molecular processes allow cells to respond to extracellular cues. Which and how strongly distinct processes are activated in individual cells varies from cell to cell and depends on cell identity [1]. Cell identity can be identified from various experimental modalities using machine learning approaches. In contrast, the dynamic response of molecular processes to extracellular is usually described using mathematical models [2, 3]. The connection between molecularly or morphologically defined cell identities and their dynamic response to extracellular cues remains poorly understood but is central to human health and fundamental biology.

In the Fröhlich lab, we are developing Deep Mechanistic Models (DMMs) a hybrid approach that combines machine learning with mathematical models. These models utilize machine learning techniques to deduce the identity of a cell, which is then employed to provide context for a mathematical model that captures the dynamics of intracellular, molecular processes. We are currently applying these methods to predict dynamic signalling response of cell lines to growth factor stimulation and drug inhibition based on baseline transcriptomic, proteomic and phosphoproteomic data (manuscript in preparation, data see [4]).

Moving forward, we want to extend the application of DMMs to prediction of other molecular processes and phenotypes, such as cellular differentiation. Moreover, we aim to incorporate additional types of input data, such as (multiplexed immune-fluorescence) images or single-cell RNA-seq/proteomics data and unravel the connection between phenotypically, molecularly and morphologically defined cell identities.  The specific extensions and applications will be tailored to the expertise and preferences of the individual candidate, ensuring a meaningful alignment with their background and interests.

Candidate background

Candidates from diverse backgrounds, including but not limited to Systems Biology, Bioinformatics, Computer Science, Data Science, Mathematics and Physics, are encouraged to apply. A quantitative mindset, coding experience (Python/Julia/C or similar language) and an interest in solving biological problems is expected.


1.         Morris, S.A. (2019)

            The evolving concept of cell identity in the single cell era.

            Development 146: dev169748. PubMed abstract

2.         Fröhlich, F., Gerosa, L., Muhlich, J. and Sorger, P.K. (2023)

            Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance.

            Molecular Systems Biology 19: e10988. PubMed abstract

3.         Fröhlich, F., Kessler, T., Weindl, D., Shadrin, A., Schmiester, L., Hache, H., . . . Hasenauer, J. (2018)

            Efficient parameter estimation enables the prediction of drug response using a mechanistic pan-cancer pathway model.

            Cell Systems 7: 567-579.e566. PubMed abstract

4.         Tognetti, M., Gabor, A., Yang, M., Cappelletti, V., Windhager, J., Rueda, O.M., . . . Bodenmiller, B. (2021)

            Deciphering the signaling network of breast cancer improves drug sensitivity prediction.

            Cell Systems 12: 401-418 e412. PubMed abstract