B02 – Inferring the dynamics underlying protrusion-driven cell motility

Motility of adherent eukaryotic cells is at the heart of many essential biological processes, such as morphogenesis, functions of the immune system, or the spread of cancer cells. It is based on deformations of the cell contour that are driven by coordinated actions of the actin cytoskeleton and controlled by an intracellular signaling network. Different theoretical models have been proposed to describe and analyze the locomotion of adherent cells. Among them, one of the most widely used classes is based on a dynamic phase field that is driven by an intracellular reaction-diffusion system. It is the long-term goal of our project to establish a systematic, data-based framework for the comparison and improvement of this class of motility models.

Based on a previously developed cell contour analysis algorithm and a well-established reaction-diffusion-based motility model, we first focus on the systematic comparison of this type of motility model to experimental data, along with establishing the associated data assimilation techniques. We will provide cost functionals for a comparison between data and modeling results. Furthermore, the dynamics of the intracellular components that drive the cell shape dynamics shall be estimated from experimentally measured contours. The resulting predictions will then be compared to fluorescence microscopy recordings of different signaling and cytoskeletal markers to evaluate the quality of the model predictions. Finally, this approach will be used to compare and improve existing motility models and to address specific biological questions that are related to switches between different motility phenotypes.

  • Schindler, D., Moldenhawer, T., Stange, M., Beta, C., Holschneider, M. and Huisinga, W. (2020). Analysis of protrusion dynamics in amoeboid cell motility by means of regularized contour flows.arXiv: 2005.12678

  • Moreno, E., Flemming, S., Font, F., Holschneider, M., Beta, C., and Alonso, S. (2020). Modeling cell crawlingstrategies with a bistable model: From amoeboid to fan-shaped cell motion. Physica D, 412:132591, doi:10.1016/j.physd.2020.132591

  • M. Stange, T. Moldenhawer, and C. Beta (2020). Fluorescent (C)LSM image sequences of dictyostelium discoideum (Ax2-LifeAct mRFP) for cell track and cell contour analysis. doi:10.5061/dryad. b5mkkwhbd.

  • D. Schindler, T. Moldenhawer, L. Lindenmeier, and M. Holschneider. Amoepy (version 1.0), 2020. doi:10.5281/zenodo.3982372

  • Flemming, S., Font, F., Alonso, S., and Beta, C. (2020). How cortical waves drive fission of motile cells. PNAS, 117(12):6330–6338, doi:10.1073/pnas.1912428117

  • Alonso, S., Stange, M. and Beta, C. (2018). Modeling random crawling, membrane deformation and intracellular polarity of motile amoeboid cells. PLoS ONE, 13(8): e0201977. doi: 10.1371/journal.pone.0201977

  • Cherstvy, A. G., Nagel, O., Beta, C. and Metzler, R. (2018). Non-Gaussianity, population heterogeneity, and transient superdiffusion in the spreading dynamics of amoeboid cells. doi: 10.1039/c8cp04254c