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000874346 005__ 20231023123818.0
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000874346 020__ $$a978-3-95806-443-0
000874346 037__ $$aFZJ-2020-01379
000874346 041__ $$aEnglish
000874346 1001_ $$0P:(DE-HGF)0$$aBerghoff, Marco$$b0
000874346 1112_ $$aNIC Symposium 2020$$cJülich$$d2020-02-27 - 2020-02-28$$wGermany
000874346 245__ $$aMassively Parallel Large-Scale Multi-Model Simulation of Tumour Development Including Treatments
000874346 260__ $$aJülich$$bForschungszentrum Jülich GmbH Zentralbibliothek, Verlag$$c2020
000874346 29510 $$aNIC Symposium 2020
000874346 300__ $$a63 - 71
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000874346 3367_ $$0PUB:(DE-HGF)7$$2PUB:(DE-HGF)$$aContribution to a book$$mcontb
000874346 4900_ $$aPublication Series of the John von Neumann Institute for Computing (NIC) NIC Series$$v50
000874346 520__ $$aThe  temporal  and  spatial  resolution  in  the  microscopy  of  tissues  has  increased  significantly within the last years, yielding new insights into the dynamics of tissue development and the role of single cells within it.  Still, the theoretical description of the connection of single cell processes to macroscopic tissue reorganisations is lacking. Especially in tumour development, single cells play a crucial role in the advance of tumour properties.   We developed a simulation framework that can model tissue development up to the centimetre scale with micrometre resolution of single cells.  Through parallelisation, it enables the efficient use of high-performance computing systems, therefore enabling detailed simulations on 10.000s of cores. Our generalised tumour model respects adhesion driven cell migration, cell-to-cell signalling, and mutation-driven tumour heterogeneity.  We scan the response of the tumour development depending on division inhibiting substances such as cytostatic agents.   Furthermore,  we are investigating the interaction with radiotherapy to find a suitable therapy plan.  Currently, the emergence of ever-more-powerful experimental techniques such as light sheet microscopy already offers unprecedented subcellular insight into tissue dynamics.  Combined with powerful machine learning techniques, such large data sets (TB’s +) can be effectively evaluated promising realistic parameters for our simulations for topics ranging from cancer development to embryogenesis  or  morphogenesis  with  considerable  impact  both  for  basic  science  and  applied biomedical fields.
000874346 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0
000874346 536__ $$0G:(DE-Juel1)PHD-NO-GRANT-20170405$$aPhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)$$cPHD-NO-GRANT-20170405$$x1
000874346 7001_ $$0P:(DE-Juel1)174346$$aRosenbauer, Jakob$$b1
000874346 7001_ $$0P:(DE-Juel1)173652$$aSchug, Alexander$$b2$$eCorresponding author
000874346 7870_ $$0FZJ-2020-01353
000874346 8564_ $$uhttps://juser.fz-juelich.de/record/874346/files/NIC_2020_Schug.pdf$$yOpenAccess
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000874346 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000874346 9141_ $$y2020
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000874346 9201_ $$0I:(DE-Juel1)NIC-20090406$$kNIC$$lJohn von Neumann - Institut für Computing$$x0
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