A 2023 Crick PhD project with Fabian Fröhlich
Project background and description
Molecular processes allow cells to respond to extracellular cues. Which and how strongly different processes are activated in individual cells varies from cell to cell and depends on protein expression levels. This dependency is dictated at the structural level, which defines if and how proteins interact to define molecular processes. To this end, allosteric interactions give rise to long range dependencies between interactions, enabling cells to respond to extracellular cues in a complex and context-dependent manner. As molecular processes are central to both tissue development and homeostasis, and are dysregulated in various diseases, better understanding is relevant for both, fundamental biology and human health.
In the Fröhlich lab, we are interested in building structure-based, dynamic models of molecular processes. Structure-based models are formulated based on thermodynamic principles and incorporate information about the energetic landscape of protein conformations and interactions in model equations (typically ordinary differential equations). This enables the principled description of phenomena such as biomolecular condensation or allosteric interactions. In the past, we have used such models to describe rewiring and drug resistance in cancer cells [1, 2] and developed computational tools to deploy such models at scale [3-5]. In the future, we want to apply machine learning to structure-based models to learn how coarse-grained, context-dependent regulatory rules emerge from fine-grained interactions. Moreover, we will apply these models to design synthetic molecular circuits and predict synergistic interactions between drugs
Several possible projects are available within this framework, including (1) neural-network-based coarse-graining of structure-based models and (2) model-guided design of multi-component allosteric switches. The exact project will be designed based on the applicants’ interests and background.
Candidate background
Candidates from diverse backgrounds, including but not limited to Structural Biology, Biochemistry, 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.
References
1. Fröhlich, F., Gerosa, L., Muhlich, J. and Sorger, P.K. (2022)
Preprint: Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance.
Available at: bioRxiv. https://doi.org/10.1101/2022.02.17.480899
2. Gerosa, L., Chidley, C., Frohlich, F., Sanchez, G., Lim, S.K., Muhlich, J., . . . Sorger, P.K. (2020)
Receptor-driven ERK pulses reconfigure MAPK signaling and enable persistence of drug-adapted BRAF-mutant melanoma cells.
Cell Systems 11: 478-494.e479. PubMed abstract
3. Fröhlich, F. and Sorger, P.K. (2022)
Fides: Reliable trust-region optimization for parameter estimation of ordinary differential equation models.
PLOS Computational Biology 18: e1010322. PubMed abstract
4. Frohlich, F., Weindl, D., Schalte, Y., Pathirana, D., Paszkowski, L., Lines, G.T., . . . Hasenauer, J. (2021)
AMICI: high-performance sensitivity analysis for large ordinary differential equation models.
Bioinformatics 37: 3676-3677. PubMed abstract
5. Schmiester, L., Schälte, Y., Bergmann, F.T., Camba, T., Dudkin, E., Egert, J., . . . Weindl, D. (2021)
PEtab-Interoperable specification of parameter estimation problems in systems biology.
PLOS Computational Biology 17: e1008646. PubMed abstract
