The group of Vasily Zaburdaev in the Department of Biology at FAU and at the Max Planck Zentrum für Physik und Medizin develops theoretical models which help to understand complex biological phenomena and their implications in disease. The group brings expertise in theoretical biophysics, statistical physics and numerical methods and works in close collaboration with experimental groups.
Exploring Brain Mechanics (EBM): Understanding, engineering and exploiting mechanical properties and signals in central nervous system development, physiology and pathology
(Third Party Funds Group – Overall project)Term: 1. January 2023 - 31. December 2026
Funding source: DFG / Sonderforschungsbereich / Transregio (SFB / TRR)
Thecentral nervous system (CNS) is our most complex organ system. Despite tremendousprogress in our understanding of the biochemical, electrical, and geneticregulation of CNS functioning and malfunctioning, many fundamental processesand diseases are still not fully understood. For example, axon growth patterns inthe developing brain can currently not be well-predicted based solely on thechemical landscape that neurons encounter, several CNS-related diseases cannotbe precisely diagnosed in living patients, and neuronal regeneration can stillnot be promoted after spinal cord injuries.
Duringmany developmental and pathological processes, neurons and glial cells aremotile. Fundamentally, motion is drivenby forces. Hence, CNS cells mechanicallyinteract with their surrounding tissue. They adhere to neighbouring cells and extracellular matrix using celladhesion molecules, which provide friction, and generate forces usingcytoskeletal proteins. These forces aretransmitted to the outside world not only to locomote but also to probe themechanical properties of the environment, which has a long overseen huge impacton cell function.
Onlyrecently, groups of several project leaders in this consortium, and a few other groupsworldwide, have discovered an important contribution of mechanical signalsto regulating CNS cell function. For example, they showed that brain tissuemechanics instructs axon growth and pathfinding in vivo, that mechanicalforces play an important role for cortical folding in the developing humanbrain, that the lack of remyelination in the aged brain is due to an increasein brain stiffness in vivo, and that many neurodegenerative diseases areaccompanied by changes in brain and spinal cord mechanics. These first insights strongly suggest thatmechanics contributes to many other aspects of CNS functioning, and it islikely that chemical and mechanical signals intensely interact at the cellularand tissue levels to regulate many diverse cellular processes.
The CRC 1540 EBM synergises the expertise of engineers, physicists,biologists, medical researchers, and clinicians in Erlangen to explore mechanicsas an important yet missing puzzle stone in our understanding of CNSdevelopment, homeostasis, and pathology. Our strongly multidisciplinary teamwith unique expertise in CNS mechanics integrates advanced invivo, in vitro, and in silico techniques across time(development, ageing, injury/disease) and length (cell, tissue, organ) scalesto uncover how mechanical forces and mechanical cell and tissue properties,such as stiffness and viscosity, affect CNS function. We especially focus on(A) cerebral, (B) spinal, and (C) cellular mechanics. Invivo and in vitro studies provide a basic understanding ofmechanics-regulated biological and biomedical processes in different regions ofthe CNS. In addition, they help identify key mechano-chemical factors forinclusion in in silico models and provide data for model calibration andvalidation. In silico models, in turn, allow us to test hypotheses without the need of excessive or even inaccessibleexperiments. In addition, they enable the transfer and comparison of mechanics data and findingsacross species and scales. They also empower us to optimise processparameters for the development of in vitro brain tissue-like matricesand in vivo manipulation of mechanical signals, and, eventually, pavethe way for personalised clinical predictions.
Insummary, we exploit mechanics-based approaches to advance ourunderstanding of CNS function and to provide the foundation for futureimprovement of diagnosis and treatment of neurological disorders.
- Abuhattum, S., Kuan, H.S., Müller, P., Guck, J., & Zaburdaev, V. (2022). Unbiased retrieval of frequency-dependent mechanical properties from noisy time-dependent signals. Biophysical Reports, 2(3). https://dx.doi.org/10.1016/j.bpr.2022.100054
- Firooz, S., Kaessmair, S., Zaburdaev, V., Javili, A., & Steinmann, P. (2022). On continuum modeling of cell aggregation phenomena. Journal of the Mechanics and Physics of Solids, 167. https://dx.doi.org/10.1016/j.jmps.2022.105004
- Poenisch, W., & Zaburdaev, V. (2022). A Pili-Driven Bacterial Turbine. Frontiers in Physics, 10. https://dx.doi.org/10.3389/fphy.2022.875687
- Tran, M.P., Chatterjee, R., Dreher, Y., Fichtler, J., Jahnke, K., Hilbert, L.,... Göpfrich, K. (2022). A DNA Segregation Module for Synthetic Cells. Small. https://dx.doi.org/10.1002/smll.202202711
- Vurnek, D., Amon, L., Buttazzo, L., Lehmann, C., Zaburdaev, V., & Dudziak, D. (2022). Spatial, structural and density determinants of DC:T cell interaction under immunostimulatory conditions. In EUROPEAN JOURNAL OF IMMUNOLOGY (pp. 60-61). HOBOKEN: WILEY.
- Anchang, C.G., Xu, C., Raimondo, M.G., Atreya, R., Maier, A., Schett, G.,... Ramming, A. (2021). The potential of omics technologies for the treatment of immune‐mediated inflammatory diseases. International Journal of Molecular Sciences, 22(14). https://dx.doi.org/10.3390/ijms22147506
- Clopes, J., Shin, J., Jahnel, M., Grill, S.W., & Zaburdaev, V. (2021). Thermal fluctuations assist mechanical signal propagation in coiled-coil proteins. Physical Review E, 104(5). https://dx.doi.org/10.1103/PhysRevE.104.054403
- Hilbert, L., Sato, Y., Kuznetsova, K., Bianucci, T., Kimura, H., Jülicher, F.,... Vastenhouw, N.L. (2021). Transcription organizes euchromatin via microphase separation. Nature Communications, 12(1). https://dx.doi.org/10.1038/s41467-021-21589-3
- Kuan, H.-S., Poenisch, W., Juelicher, F., & Zaburdaev, V. (2021). Continuum Theory of Active Phase Separation in Cellular Aggregates. Physical Review Letters, 126(1). https://dx.doi.org/10.1103/PhysRevLett.126.018102
- Noa, A., Kuan, H.-S., Aschmann, V., Zaburdaev, V., & Hilbert, L. (2021). The hierarchical packing of euchromatin domains can be described as multiplicative cascades. PLoS Computational Biology, 17(5). https://dx.doi.org/10.1371/journal.pcbi.1008974
- Pancholi, A., Klingberg, T., Zhang, W., Prizak, R., Mamontova, I., Noa, A.,... Hilbert, L. (2021). RNA polymerase II clusters form in line with surface condensation on regulatory chromatin. Molecular Systems Biology, 17(9). https://dx.doi.org/10.15252/msb.202110272
- Adame-Arana, O., Weber, C.A., Zaburdaev, V., Prost, J., & Jülicher, F. (2020). Liquid Phase Separation Controlled by pH. Biophysical Journal. https://dx.doi.org/10.1016/j.bpj.2020.07.044
- Kaptan, D., Penkov, S., Zhang, X., Gade, V.R., Raghuraman, B.K., Galli, R.,... Kurzchalia, T. (2020). Exogenous ethanol induces a metabolic switch that prolongs the survival of Caenorhabditis elegans dauer larva and enhances its resistance to desiccation. Aging Cell. https://dx.doi.org/10.1111/acel.13214
- Luhr, J.J., Alex, N., Amon, L., Krater, M., Kubankova, M., Sezgin, E.,... Guck, J. (2020). Maturation of Monocyte-Derived DCs Leads to Increased Cellular Stiffness, Higher Membrane Fluidity, and Changed Lipid Composition. Frontiers in Immunology, 11. https://dx.doi.org/10.3389/fimmu.2020.590121
- Mazaheri, M., Ehrig, J., Shkarin, A., Zaburdaev, V., & Sandoghdar, V. (2020). Ultrahigh-Speed Imaging of Rotational Diffusion on a Lipid Bilayer. Nano Letters, 20(10), 7213-7219. https://dx.doi.org/10.1021/acs.nanolett.0c02516
- Taylor, R.W., Holler, C., Mahmoodabadi, R.G., Küppers, M., Mirzaalian Dastjerdi, H., Zaburdaev, V.,... Sandoghdar, V. (2020). High-Precision Protein-Tracking With Interferometric Scattering Microscopy. Frontiers in Cell and Developmental Biology, 8. https://dx.doi.org/10.3389/fcell.2020.590158