We are convinced that perturbing cells and quantifying the resulting dynamic response is essential to derive causal explanations. We are particularly interested in perturbations with temporal precision and high specificity such as growth factors or small molecules, but also consider genetic alterations. Most projects in the lab use ordinary differential equations to describe the dynamic response to such perturbations, but we also leverage the dynamic nature of biological system to generate novel biological insights from static data.
Analysis of large and complex datasets requires scalable and reproducible computational methods. We have a track record of developing computational tools, including the simulation engine
AMICI [Fröhlich et. al 2021] and the calibration toolbox pyPESTO [Schälte et. al 2023], and strive to continue to push the envelope through methodological innovation.
Mathematical models are powerful means to encode prior knowledge. While valuable in data-sparse settings, this limits unbiased discovery. We apply data-driven methods to enable the unbiased inference of explainable dynamic models from large perturbational screens.
Signalling exhibits considerable cell-to-cell variability resulting in heterogeneous cellular decision-making outcomes. We build models of signal transduction that predict these outcomes based on quantifications of the molecular make-up of cells [Fröhlich et. al 2018].
Signalling is intricately regulated by numerous molecular mechanisms involving protein-protein interactions and post-translational modifications. We build detailed mechanistic models that account for protein structure [Gerosa et. al 2020, Fröhlich et. al 2023] to learn the emergent regulatory principles for signalling.
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