001     1043301
005     20250724210254.0
037 _ _ |a FZJ-2025-02822
041 _ _ |a English
100 1 _ |a Behle, Eric
|0 P:(DE-Juel1)188557
|b 0
|e Corresponding author
|u fzj
111 2 _ |a DPG Spring Meeting 2025
|g SKM25
|c Regensburg
|d 2025-03-16 - 2025-03-21
|w Germany
245 _ _ |a Leveraging Experimental Vasculature Data for High Resolution Brain Tumor Simulations
260 _ _ |c 2025
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a CONFERENCE_POSTER
|2 ORCID
336 7 _ |a Output Types/Conference Poster
|2 DataCite
336 7 _ |a Poster
|b poster
|m poster
|0 PUB:(DE-HGF)24
|s 1753341138_4439
|2 PUB:(DE-HGF)
|x After Call
520 _ _ |a Cancer remains a leading cause of mortality. Multidisciplinary studies probe its pathology to increase treatment options. Computational modeling of tumors on HPC resources offers insight into its progress and an avenue for advancing our understanding. However, initialization and parameterization of the underlying models require high-resolution data from real tissue structures. Here, we leveraged HPC resources and a massive dataset of a mouse brain's entire vascular network. We processed these image stacks into detailed 3D representations, identified brain regions of interest, and conducted a series of large-scale simulations to investigate how tumor growth is influenced by local vascular network characteristics. By simulating tumor growth with sub-cellular resolution, we can probe to which extent vessel density and network length influence growth. We determined that vessel density is the primary determinant of growth rate. Finally, our results allowed us to extrapolate tumor cell growth predictions for the entire mouse brain, highlighting the critical role of vascular topology in tumor progression. Such increasingly realistic simulations of cancer cells may enable researchers to bridge the gap between basic biology and clinical practice, supporting development of cancer therapies.
536 _ _ |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)
|0 G:(DE-HGF)POF4-5111
|c POF4-511
|f POF IV
|x 0
536 _ _ |a PhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)
|0 G:(DE-Juel1)PHD-NO-GRANT-20170405
|c PHD-NO-GRANT-20170405
|x 1
700 1 _ |a Herold, J. M.
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Schug, Alexander
|0 P:(DE-Juel1)173652
|b 2
|u fzj
856 4 _ |u https://www.dpg-verhandlungen.de/year/2025/conference/regensburg/part/bp/session/14/contribution/9
909 C O |o oai:juser.fz-juelich.de:1043301
|p VDB
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)188557
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)173652
913 1 _ |a DE-HGF
|b Key Technologies
|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
|1 G:(DE-HGF)POF4-510
|0 G:(DE-HGF)POF4-511
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Enabling Computational- & Data-Intensive Science and Engineering
|9 G:(DE-HGF)POF4-5111
|x 0
914 1 _ |y 2025
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 0
980 _ _ |a poster
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 _ _ |a UNRESTRICTED


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