## Research

The central theme of our research is to understand how stochastic interactions of cells and molecules control the functional behaviour of multicellular systems. We specifically focus on how cells interact to shape and pattern tissues during embryonic development. To address this question, we use approaches from statistical mechanics and soft matter physics. Our research combines three complementary theoretical approaches: biophysical models, information theory, and stochastic inference.

### Cell fate decisions and tissue patterning in development

Embryonic development relies on the emergence of different types of cells in a tissue. In many systems, patterns these cell fates are established in a self-organized manner. We investigate the basic principles of how cell-cell communication controls self-organization through mechanical interactions and chemical signaling. We seek to identify principles that allow multicellular systems to self-organize reproducibly despite the inevitable presence of stochastic fluctuations. We approach this central question using a combination of theoretical approaches: we use reaction-diffusion and dynamical systems models to build minimal models of signal generation and interpretation. We analyse these models using information theory to understand their universal principles. Finally, we use stochastic inference and machine learning to perform model inference directly from experimental data sets of organoid systems.

D. B. Brückner and G. Tkačik: PNAS 121 (23) e2322326121

S. Lehr*, D. B. Brückner*, M. Greunz-Schindler, T. Minchington, J. Merrin, E. Hannezo, A. Kicheva: bioRxiv 2023.11.15.567070

### Active mechanics of cells and tissues

Collectively migrating cells are a prime example of active matter. A central biophysical question is how the collective behaviour of such interacting cells responds to external constraints including curvature, topology and defined geometric boundaries. We develop minimal active matter models of interacting active particles to understand how cell interactions interplay with their environment. This will help understand how tissues generate collective flows in wound healing, cancer metastasis and embryogenesis.

E. Vercruysse*, D. B. Brückner*, M. Gómez-González, M. Luciano, Y. Kalukula, L. Rossetti, X. Trepat, E. Hannezo, S. Gabriele: Nature Physics (2024)

S. Tavano, D. B. Brückner, S. Tasciyan, R. Hauschild, X. Tong, R. Kardos, A. Schauer and C. P. Heisenberg: bioRxiv 2023.07.21.550024

T. Brandstätter*, D. B. Brückner*, Y. L. Han, R. Alert, M. Guo, C. P. Broedersz: Nature Communications 14, 1643 (2023)

T. Zisis*, D. B. Brückner*, T. Brandstätter, W. X. Siow, J. d'Alessandro, A. Vollmar, C. P. Broedersz, S. Zahler: Biophys. J. 121:44, 2022

D. B. Brückner, M. Schmitt, A. Fink, G. Ladurner, J. Flommersfeld, N. Arlt, E. Hannezo, J. O. Rädler, C. P. Broedersz: Phys. Rev. X 12:031041, 2022

Review: C. Schwayer and D. B. Brückner: Journal of Cell Science 136, jcs261515 (2023)

Review: D. B. Brückner and E. Hannezo: Cold Spring Harbour Perspectives in Biology (2024)

### Stochastic dynamics of enhancer-promoter search processes

Chromosomes are highly organized to fit into the eukaryotic nucleus. For many functional processes, pair-wise interactions of distal chromosomal elements, such as enhancers and promoters, are essential. However, how the stochastic real-time dynamics of DNA loci determines search processes, such as mean first passage times of specific regulatory regions, remains unclear. We develop a combination of inference approaches, polymer simulations and scaling analyses to learn the physics of chromosome dynamics from experimental trajectories of pairs of DNA loci.

D. B. Brückner*, H. Chen*, L. Barinov, B. Zoller, T. Gregor: Science 380, 1357-1362 (2023)

### Inference from stochastic trajectories

Can we learn the physics of a system just by looking at it? This fundamental problem of inference from data is particularly difficult to solve in systems that are stochastic, meaning that the dynamics exhibit fluctuations that shape the trajectories of the system. We develop methods to infer the underlying dynamics of stochastic systems directly from experimental trajectories. The key challenges that we are addressing are to achieve reliable inference in the face of finite data, discrete observations, measurement errors and high signal dimensionality. These tools have applications in a broad range of stochastic systems, from biomolecules, migrating cells and animal swarms to financial markets.

D. B. Brückner*, P. Ronceray*, C. P. Broedersz: Phys. Rev. Lett. 125:058103, 2020

D. B. Brückner*, A. Fink*, C. Schreiber, P. J. F. Röttgermann, J. O. Rädler, C. P. Broedersz: Nature Physics 15:595, 2019

D. B. Brückner, N. Arlt, A. Fink, P. Ronceray, J. O. Rädler, C. P. Broedersz: PNAS 118:e2016602118, 2021

D. B. Brückner, A. Fink, J. O. Rädler, C. P. Broedersz: J. R. Soc. Interface 17:20190689, 2020

Review: D. B. Brückner and C. P. Broedersz: Reports on Progress in Physics 87, 056601 (2024)