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.
Kemeth, F., Alonso, S., Echebarria, B., Moldenhawer, T., Beta, C. and Kevrekidis I. (2022). Black and Gray Box Learning of Amplitude Equations: Application to Phase Field Systems. arXiv: 2207.03954
Schindler, D., Moldenhawer, T., Beta, C., Huisinga, W. and Holschneider, M. (2022). Three-component contour dynamics model to simulate and analyze amoeboid cell motility. arXiv:2210.12978
Moldenhawer, T., Moreno, E., Schindler, D., Flemming, S., Holschneider, M., Huisinga, W., Alonso, S. and Beta, C. (2022). Spontaneous transitions between amoeboid and keratocyte-like modes of migration. Front. Cell Dev. Biol., 10:898351, doi:10.3389/fcell.2022.898351
Yochelis, A., Flemming, S. and Beta, C. (2022) Versatile Patterns in the Actin Cortex of Motile Cells: Self-Organized Pulses Can Coexist with Macropinocytic Ring-Shaped Waves. Phys. Rev. Lett., 129:088101, doi:10.1103/PhysRevLett.129.088101
Vilk, O., Aghion, E., Avgar, T., Beta, C., Nagel, O., Sabri, A., Sarfati, R., Schwartz, D., Weiss, M., Krapf, D., Nathan, R., Metzler, R. and Assaf, M. (2022) Unravelling the origins of anomalous diffusion: From molecules to migrating storks. Phys. Rev. Research, 4:033055, doi:10.1103/PhysRevResearch.4.033055
Moreno, E., Grossmann, R., Beta, C. and Alonso S. (2022) From Single to Collective Motion of Social Amoebae: A Computational Study of Interacting Cells. Front. Phys., 9:750187, doi:10.3389/fphy.2021.750187
Schindler, D., Moldenhawer, T., Stange, M., Lepro, V., Beta, C., Holschneider, M., and Huisinga, W. (2021). Analysis of protrusion dynamics in amoeboid cell motility by means of regularized contour flows. PLoS Comput Biol 17(8): e1009268. doi:journal.pcbi.1009268
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