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@PHDTHESIS{Behle:1043294,
      author       = {Behle, Eric},
      title        = {{O}n the road to digital twins of tumors},
      school       = {Heinrich-Heine-University Düsseldorf},
      type         = {Dissertation},
      address      = {Düsseldorf},
      publisher    = {Universitäts-und Landesblibliothek Düsseldorf},
      reportid     = {FZJ-2025-02820},
      pages        = {120 p.},
      year         = {2025},
      note         = {Dissertation, Heinrich-Heine-University Düsseldorf, 2025},
      abstract     = {Many multicellular organisms are composed of tissues.
                      Tissues are groups of similar cells with one or more
                      specific tasks, and it is the combination of multiple
                      tissues that leads to the formation of organs and larger
                      structures within the body. As the complexity of an organism
                      increases, so does the number of different tissues. Tissue
                      generation and differentiation thus plays crucial roles
                      covering the range from life processes from its early stages
                      during embryogenesis to necessary maintenance such as wound
                      healing. Unfortunately, the proliferation machinery of some
                      cells can become defective due to multiple causes. Through
                      this, the tissue growth process may become flawed and
                      uncontrollable. This can lead to the multi-faceted
                      phenomenon known as cancer. Cancer is a disease that can
                      occur in the entire body, and its heterogeneity makes a
                      generally applicable treatment difficult. While
                      understanding the detailed origins of the cell proliferation
                      defects on a genetic and epigenetic level is crucial to
                      stopping the disease before it starts, doctors and patients
                      are often faced with the fact that it has already progressed
                      to a macroscopic stage ((O(mm)) at the time of diagnosis. In
                      this situation, it is necessary to understand the laws
                      governing the growth of tumors, in order to develop
                      effective treatment strategies. Computational models are a
                      useful tool for this for two main reasons. Firstly, because
                      a mechanistic model capable of reproducing experimentally
                      observed behavior underlines our understanding of the
                      underlying mechanisms. Secondly, because a working model
                      would vastly benefit treatment via quick assessments of
                      options without any side effects or harm to the patient.
                      Hence, work is ongoing to develop so-called digital twins of
                      growing tumors. A challenge in this endeavour is the
                      scale-spanning nature of cancer. This means that any model
                      simulating macroscopic tissue and tumor growth must be
                      capable of doing so at single-cell resolution. Such models
                      are computationally demanding, and often require
                      supercomputing infrastructures to employ. Furthermore, care
                      must be taken to parameterize them correctly, and large
                      amounts of experimental data are required for this. During
                      this doctoral project, I have worked with and extended Cells
                      in Silico (CiS), a highly scalable tissue simulation
                      framework previously developed by my group. CiS is capable
                      of simulating biological tissues composed of tens of
                      millions of cells at subcellular resolution, and is
                      therefore a promising candidate for simulating a digital
                      twin. However, before doing so, it must be extended further,
                      and data for its parameterization must be found. To combat
                      the problem of in vivo data scarcity, I have employed a
                      "divide and conquer" approach, in which I aimed to partially
                      parameterize CiS by focusing on smaller in vitro sub
                      systems, for which data exist. During my studies, I first
                      focused on an investigation of the structural environment of
                      tumors, by working with tumor spheroids grown in collagen
                      matrices of varying density. For this, I performed a large
                      number of spheroid growth simulations, in order to reproduce
                      the behavior of the in vitro spheroids. To analyze the
                      agreement between in vitro and in silico spheroids, we
                      developed the overall deviation score (ODS). The ODS, which
                      is a metric for comparing the structure of two spheroids
                      regardless of their origin, provided an objective function
                      for the parameterization of CiS. During this project, we
                      discovered that CiS needs a more realistic description for
                      the extracellular matrix in order to accurately reproduce
                      spheroid behavior. A project to include such a description
                      is ongoing within my group. In the second part of my
                      project, I focused on the nutrient environment of tumors.
                      Here, I incorporated a set of detailed mouse brain
                      vasculature data into CiS, in order to build a more
                      realistic nutrient environment. I then studied the growth
                      behavior of tumors placed in vascular environments of
                      different density and thickness. Within my simulations, I
                      found that vessel density is the main contributor to final
                      tumor volume. Finally, I focused on the advancement of
                      supercomputing infrastructure by participating in the
                      development of a benchmarking pipeline for the JUPITER
                      supercomputer. Overall, my work has improved CiS, and paved
                      the way for bringing it closer to simulating digital twins
                      of tumors.},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)11},
      urn          = {https://nbn-resolving.org/urn:nbn:de:hbz:061-20250610-092027-1},
      doi          = {10.34734/FZJ-2025-02820},
      url          = {https://juser.fz-juelich.de/record/1043294},
}