Prof. Dr. Vasily Zaburdaev
Chair of Mathematics in Life Sciences

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.
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IB-DFG_SPP_VO 944/14-1: Biophysikalische Mechanismen der Gewebeinvasion und Festsetzung von Parasiten in einem Mausmodell für Hakenwurminfektionen
(Third Party Funds Group – Sub project)
Overall project: SPP2332 Physics of parasitism
Project leader: ,
Term: 1. March 2025 - 29. February 2028
Acronym: IB-DFG_SPP_VO 944/14-1
Funding source: DFG / Schwerpunktprogramm (SPP) -
SFB 1540 - EBM: 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)
Project leader:
Term: 1. January 2023 - 31. December 2026
Acronym: SFB 1540 - EBM
Funding source: DFG / Sonderforschungsbereich / Transregio (SFB / TRR)
URL: https://www.crc1540-ebm.research.fau.eu/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.
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SFB 1540 C01: Modellierung und Simulation der Mechanik von Zell-Matrix Interaktionen (C01)
(Third Party Funds Group – Sub project)
Overall project: SFB 1540: Erforschung der Mechanik des Gehirns (EBM): Verständnis, Engineering und Nutzung mechanischer Eigenschaften und Signale in der Entwicklung, Physiologie und Pathologie des zentralen Nervensystems
Project leader: ,
Term: 1. January 2023 - 31. December 2026
Acronym: SFB 1540 C01
Funding source: DFG / Sonderforschungsbereich (SFB)C01 verbindet Modellierung und Simulation zur Aufdeckung der Rolle mechanischer Zell-Matrix-Wechselwirkungen im Gehirngewebe. Dabei berücksichtigen agentenbasierte und phänomenologische Kontinuumsmodelle die Zell-Matrix-Wechselwirkungen, die Zellmigration sowie die aktive Krafterzeugung. Mittels in silico Implementierungen werden wir analysieren, wie Zell-Zell- und Zell-Matrix-Wechselwirkungen die mechanischen Eigenschaften des Systems auf Kontinuumsebene bestimmen. Weiterhin werden wir die Dynamik der Bildung neuronaler Organoide in künstlichen Matrizen mittels nichtlinearer Kontinuumsmodellierung und -simulation untersuchen.
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IMPRS-PM: International Max-Planck Research School
(Third Party Funds Single)
Project leader:
Term: 1. January 2022 - 31. December 2027
Acronym: IMPRS-PM
Funding source: Max-Planck-Gesellschaft -
GRK 2740 ImmunoMicroTope Project A7: A7: Characterization and mathematical modeling of the STAT6-regulated micromilieu in response to Nippostrongylus brasiliensis infections
(Third Party Funds Group – Sub project)
Overall project: GRK 2740: Immunmikrotop: Mikroumgebungsbedingte, metabolische und mikrobielle Signale zur Regulation der Immunzell-Pathogen-Interaktion
Project leader: ,
Term: 1. January 2022 - 31. December 2030
Acronym: GRK 2740 ImmunoMicroTope Project A7
Funding source: DFG / Graduiertenkolleg (GRK)
URL: https://www.immunomicrotope.de/beispiel-seite/project-areas/project-area-a-micro-milieu/a7-characterization-and-mathematical-modHelminth infections affect about 1/3 of the world population and to date there are no helminth-specific vaccines available. IL-4/IL-13-induced activation of the transcription factor STAT6 in non-hematopoetic cells plays a critical role for worm expulsion during infection with gastrointestinal helminths. We will investigate how expression of STAT6-regulated genes in different cell types cause changes of the immunomicrotope (mucus production, pH, tissue tonicity, short chained fatty acids) in skin, lung and intestine. We will further determine how the worm transcriptome changes during colonization of the small intestine in wild-type versus STAT6-deficient mice. In close collaboration with Vasily Zaburdaev (part B of this project) we will use mathematical modeling to address changes of individual parameters on tissue migration and expulsion of N. brasiliensis.
2026
- Firooz, S., Reddy, B.D., Zaburdaev, V., & Steinmann, P. (2026). Cellular aggregate formation: Continuum modelling and computational aspects. Computer Methods in Applied Mechanics and Engineering, 451. https://doi.org/10.1016/j.cma.2025.118687
- Klingberg, T.G., Wachter, I., Pancholi, A., Akyel, M., Gohar, Y., Kumar, P.,... Hilbert, L. (2026). Stem Cell Differentiation Disperses Transcriptional Clusters via a Conserved Surface-Condensate Trajectory. Advanced Science. https://doi.org/10.1002/advs.75924
- Kravikass, M., Bischof, L., Karandasheva, K., Furlanetto, F., Dolai, P., Falk, S.,... Zaburdaev, V. (2026). In silico neuritogenesis model underpins mechanical interactions with extracellular matrix as determinants of persistent axonal growth in stiffer microenvironments.
- Neumann, O., Kravikass, M., John, N., Gopalan Ramachandran, R., Steinmann, P., Zaburdaev, V.,... Budday, S. (2026). In silico model of axonal pathfinding during spinal cord regeneration in zebrafish larvae. (Unpublished, Submitted).
2025
- Biswas, A., Muñoz, O., Kim, K., Hoege, C., Lorton, B.M., Nikolay, R.,... Reber, S. (2025). Conserved nucleocytoplasmic density homeostasis drives cellular organization across eukaryotes. Nature Communications, 16(1). https://doi.org/10.1038/s41467-025-62605-0
- Chakraborti, S., & Zaburdaev, V. (2025). Density-dependent transport coefficients in two-dimensional cellular aggregates. Physical Review E, 112. https://doi.org/10.1103/x9qs-28vj
- Chatterjee, R., Kuan, H.-S., Jülicher, F., & Zaburdaev, V. (2025). Nonequilibrium Structure and Relaxation in Active Microemulsions. Physical Review Letters, 135(3), 038401-. https://doi.org/10.1103/jfm6-8h9s
- Kletter, T., Muñoz, O., Reusch, S., Biswas, A., Halavatyi, A., Neumann, B.,... Reber, S. (2025). Cell state-specific cytoplasmic density controls spindle architecture and scaling. Nature Cell Biology. https://doi.org/10.1038/s41556-025-01678-x
- Miranda, S.S.E., Abbaszade, G., Hess, W.R., Drescher, K., Saliba, A.E., Zaburdaev, V.,... Mascher, T. (2025). Resolving spatiotemporal dynamics in bacterial multicellular populations: approaches and challenges. Microbiology and Molecular Biology Reviews, 89(1). https://doi.org/10.1128/mmbr.00138-24
- Schnitzerlein, M., Greto, E., Wegner, A., Möller, A., Aust, O., Ben Brahim, O.,... Uderhardt, S. (2025). Cellular morphodynamics as quantifiers for functional states of resident tissue macrophages in vivo. PLoS Computational Biology, 21(5). https://doi.org/10.1371/journal.pcbi.1011859
2024
- Angeloni, M., van Doeveren, T., Lindner, S., Volland, P., Schmelmer, J., Foersch, S.,... Bahlinger, V. (2024). A deep-learning workflow to predict upper tract urothelial carcinoma protein-based subtypes from H&E slides supporting the prioritization of patients for molecular testing. Journal of Pathology: Clinical Research, 10(2). https://doi.org/10.1002/2056-4538.12369
- Chai, L., Shank, E.A., & Zaburdaev, V. (2024). Where bacteria and eukaryotes meet. Journal of Bacteriology, 206(2). https://doi.org/10.1128/jb.00049-23
- Chai, L., Zaburdaev, V., & Kolter, R. (2024). How bacteria actively use passive physics to make biofilms. Proceedings of the National Academy of Sciences of the United States of America, 121(40). https://doi.org/10.1073/pnas.2403842121
- Chakraborti, S., & Zaburdaev, V. (2024). Transport in cellular aggregates described by fluctuating hydrodynamics. Physical Review Research, 6(4). https://doi.org/10.1103/PhysRevResearch.6.043064
- Firooz, S., Reddy, B.D., Zaburdaev, V., & Steinmann, P. (2024). Mean zero artificial diffusion for stable finite element approximation of convection in cellular aggregate formation. Computer Methods in Applied Mechanics and Engineering, 419, 116649. https://doi.org/10.1016/j.cma.2023.116649
- Jordan, J., Jaitner, N., Meyer, T., Brame, L., Ghrayeb, M., Köppke, J.,... Sack, I. (2024). Rapid Stiffness Mapping in Soft Biologic Tissues With Micrometer Resolution Using Optical Multifrequency Time‐Harmonic Elastography. Advanced Science. https://doi.org/10.1002/advs.202410473
- Möckel, C., Beck, T., Kaliman, S., Abuhattum, S., Kim, K., Kolb, J.,... Guck, J. (2024). Estimation of the mass density of biological matter from refractive index measurements. Biophysical Reports, 100156. https://doi.org/10.1016/j.bpr.2024.100156
2023
- Tschurikow, X., Gadzekpo, A., Tran, M.P., Chatterjee, R., Sobucki, M., Zaburdaev, V.,... Hilbert, L. (2023). Amphiphiles Formed from Synthetic DNA-Nanomotifs Mimic the Stepwise Dispersal of Transcriptional Clusters in the Cell Nucleus. Nano Letters. https://doi.org/10.1021/acs.nanolett.3c01301
- Ye, Y., Ghrayeb, M., Miercke, S., Arif, S., Müller, S., Mascher, T.,... Zaburdaev, V. (2023). Residual cells and nutrient availability guide wound healing in bacterial biofilms. Soft Matter, 20(5), 1047-1060. https://doi.org/10.1039/d3sm01032e
- Zhang, X., Penkov, S., Kurzchalia, T.V., & Zaburdaev, V. (2023). Periodic ethanol supply as a path toward unlimited lifespan of Caenorhabditis elegans dauer larvae. Frontiers in Aging, 4. https://doi.org/10.3389/fragi.2023.1031161
2022
- 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), 100054. https://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://doi.org/10.1016/j.jmps.2022.105004
- Poenisch, W., & Zaburdaev, V. (2022). A Pili-Driven Bacterial Turbine. Frontiers in Physics, 10. https://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://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.
2021
- 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://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://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://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://doi.org/10.1103/PhysRevLett.126.018102
- Lühr, J., Alex, N., Amon, L., Kraeter, M., Kubankova, M., Sezgin, E.,... Dudziak, D. (2021). Maturation of monocyte-derived DCs leads to increased cellular stiffness, higher membrane fluidity, and changed lipid composition. In EUROPEAN JOURNAL OF IMMUNOLOGY (pp. 235-235). HOBOKEN: WILEY.
- 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), e1008974. https://doi.org/10.1371/journal.pcbi.1008974
- Pancholi, A., Klingberg, T.G., 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://doi.org/10.15252/msb.202110272
2020
- Adame-Arana, O., Weber, C.A., Zaburdaev, V., Prost, J., & Jülicher, F. (2020). Liquid Phase Separation Controlled by pH. Biophysical Journal. https://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://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://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://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://doi.org/10.3389/fcell.2020.590158