1. Data assimilation - the collaborative research centre SFB1294
  2. Research
  3. Research Area B
  4. B07

B07 - Inferring the collective dynamics of active particles by data assimilation

This project started with the second funding period in July 2021.

This project brings investigates how active particles, exemplified by living cells, move, interact, and organize into collective patterns. We combine experiments with advanced data analysis and modelling to develop reliable models of cell motility and pattern formation from experimental observations. 

In the second funding period, we established methods to infer parameter distributions for heterogeneous ensembles of motile particles described by stochastic differential equations, and applied model selection techniques to identify the formulation that best explains data. 

The current project focuses on collective behaviour at high densities, where interactions between particles lead to the emergence of complex patterns. By combining single-cell tracking with measurements of large-scale motion, we aim to connect processes across different spatial and temporal scales. Experimental observations will be integrated with stochastic and continuum models to infer the mechanisms underlying collective dynamics. 

Ultimately, the project seeks to bridge the gap between individual-cell behaviour and large-scale pattern formation through a data-driven combination of experiments, statistical inference, and physical modelling. In doing so, it will provide new insights into collective motion and advance the theoretical understanding of active matter.

No available.

  • Albrecht, J., Dautzenberg, L.S., Opper, M., Beta, C., Großmann, R. (2026): Likelihood-based heterogeneity inference reveals nonstationary effects in biohybrid cell-cargo transport. Physical Review Research, 8, 013106.

  • Fast, V., Datta, A., Park, J.,  Großmann, R., Pfeifer, V.,  Kim, Y., Lee, W., Lim, S., Beta, C. (2026): Swimming patterns of a multi-mode bacterial swimmer in fluid shear flow. Biophysical Journal, 125.

  • Albrecht, J., Opper, M., Großmann, R. (2026): A Likelihood Approach for Inference of Population Heterogeneity in Particle Ensembles with Second-Order Langevin Dynamics. Communications Physics, 9, 165.

  • Beier, S., Pfeifer, V., Datta, A., Großmann, R., Beta, C. (2026): Dual Chemotaxis Strategy of Bacteria in Porous Media: Run Time and Turn Angle Bias. Biophysical Journal 125.

  • Beier, S., Datta, A., Pfeifer, V., Großmann, R., Beta, C. (2025): Trajectory data for bacterial motility in semi-solid agar.https://zenodo.org/records/17592316.

  • Dautzenberg, L.S., Albrecht, J., Großmann, R., Beta, C. (2025): Trajectories of polystyrene beads driven by a carpet of ameboid cells.https://zenodo.org/records/17804335

  • Datta, A., Beier, S.,  Pfeifer, V.,  Großmann, R., Beta, C. (2025): Bacterial swimming in porous gels exhibits intermittent run motility with active turns and mechanical trapping. Scientific Reports, 15.

  • V. Pfeifer, V. Muraveva, and Beta, C. (2024): Flagella and Cell Body Staining of Bacteria with Fluorescent Dyes. In: Cell Motility and Chemotaxis: Methods and Protocols, edited by Carsten Beta and Cristina Martinez-Torres (Springer, 2024), p.79-85.

  • R. Großmann et al. (2024): Non-Gaussian Displacements in Active Transport on a Carpet of Motile Cells. Phys. Rev. Lett. 132(8) 088301. doi: 10.1103/PhysRevLett.132.088301

  • A. Datta, C. Beta and R. Großmann (2024) Random walks of intermittently self-propelled particles Phys. Rev. Res. 6 043281. doi: https://doi.org/10.1103/PhysRevResearch.6.043281

  • Albrecht, J., Reich, S. (2024): Parameter estimation for partially observed second-order diffusion processes. Stochastic Transport in Upper Ocean Dynamics Annual Workshop.

  • Pfeifer, V., Beier, S., Alirezaeizanjani, Z., and Beta, C. (2022): Role of the two flagellar stators in swimming motility of Pseudomonas putida. Mbio 13(6) e02182-22, doi: 10.1128/mbio.02182-22.