@article{KaltdorfBreitenbachKarletal.2023,
  author    = {Kaltdorf, Martin and Breitenbach, Tim and Karl, Stefan and Fuchs, Maximilian and Kessie, David Komla and Psota, Eric and Prelog, Martina and Sarukhanyan, Edita and Ebert, Regina and Jakob, Franz and Dandekar, Gudrun and Naseem, Muhammad and Liang, Chunguang and Dandekar, Thomas},
  title     = {Software JimenaE allows efficient dynamic simulations of Boolean networks, centrality and system state analysis},
  series = {Scientific Reports},
  volume    = {13},
  journal   = {Scientific Reports},
  doi       = {10.1038/s41598-022-27098-7},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-313303},
  year      = {2023},
  abstract  = {The signal modelling framework JimenaE simulates dynamically Boolean networks. In contrast to SQUAD, there is systematic and not just heuristic calculation of all system states. These specific features are not present in CellNetAnalyzer and BoolNet. JimenaE is an expert extension of Jimena, with new optimized code, network conversion into different formats, rapid convergence both for system state calculation as well as for all three network centralities. It allows higher accuracy in determining network states and allows to dissect networks and identification of network control type and amount for each protein with high accuracy. Biological examples demonstrate this: (i) High plasticity of mesenchymal stromal cells for differentiation into chondrocytes, osteoblasts and adipocytes and differentiation-specific network control focusses on wnt-, TGF-beta and PPAR-gamma signaling. JimenaE allows to study individual proteins, removal or adding interactions (or autocrine loops) and accurately quantifies effects as well as number of system states. (ii) Dynamical modelling of cell-cell interactions of plant Arapidopsis thaliana against Pseudomonas syringae DC3000: We analyze for the first time the pathogen perspective and its interaction with the host. We next provide a detailed analysis on how plant hormonal regulation stimulates specific proteins and who and which protein has which type and amount of network control including a detailed heatmap of the A.thaliana response distinguishing between two states of the immune response. (iii) In an immune response network of dendritic cells confronted with Aspergillus fumigatus, JimenaE calculates now accurately the specific values for centralities and protein-specific network control including chemokine and pattern recognition receptors.},
  language  = {en}
}
@article{BaurNietzerKunzetal.2020,
  author    = {Baur, Florentin and Nietzer, Sarah L. and Kunz, Meik and Saal, Fabian and Jeromin, Julian and Matschos, Stephanie and Linnebacher, Michael and Walles, Heike and Dandekar, Thomas and Dandekar, Gudrun},
  title     = {Connecting cancer pathways to tumor engines: a stratification tool for colorectal cancer combining human in vitro tissue models with boolean in silico models},
  series = {Cancers},
  volume    = {12},
  journal   = {Cancers},
  number    = {1},
  issn      = {2072-6694},
  doi       = {10.3390/cancers12010028},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193798},
  pages     = {28},
  year      = {2020},
  abstract  = {To improve and focus preclinical testing, we combine tumor models based on a decellularized tissue matrix with bioinformatics to stratify tumors according to stage-specific mutations that are linked to central cancer pathways. We generated tissue models with BRAF-mutant colorectal cancer (CRC) cells (HROC24 and HROC87) and compared treatment responses to two-dimensional (2D) cultures and xenografts. As the BRAF inhibitor vemurafenib is—in contrast to melanoma—not effective in CRC, we combined it with the EGFR inhibitor gefitinib. In general, our 3D models showed higher chemoresistance and in contrast to 2D a more active HGFR after gefitinib and combination-therapy. In xenograft models murine HGF could not activate the human HGFR, stressing the importance of the human microenvironment. In order to stratify patient groups for targeted treatment options in CRC, an in silico topology with different stages including mutations and changes in common signaling pathways was developed. We applied the established topology for in silico simulations to predict new therapeutic options for BRAF-mutated CRC patients in advanced stages. Our in silico tool connects genome information with a deeper understanding of tumor engines in clinically relevant signaling networks which goes beyond the consideration of single drivers to improve CRC patient stratification.},
  language  = {en}
}
@article{KuehnemundtLeifeldSchergetal.2021,
  author    = {K{\"u}hnemundt, Johanna and Leifeld, Heidi and Scherg, Florian and Schmitt, Matthias and Nelke, Lena C. and Schmitt, Tina and Bauer, Florentin and G{\"o}ttlich, Claudia and Fuchs, Maximilian and Kunz, Meik and Peindl, Matthias and Br{\"a}hler, Caroline and Kronenthaler, Corinna and Wischhusen, J{\"o}rg and Prelog, Martina and Walles, Heike and Dandekar, Thomas and Dandekar, Gudrun and Nietzer, Sarah L.},
  title     = {Modular micro-physiological human tumor/tissue models based on decellularized tissue for improved preclinical testing},
  series = {ALTEX},
  volume    = {38},
  journal   = {ALTEX},
  doi       = {10.14573/altex.2008141},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-231465},
  pages     = {289-306},
  year      = {2021},
  abstract  = {High attrition-rates entailed by drug testing in 2D cell culture and animal models stress the need for improved modeling of human tumor tissues. In previous studies our 3D models on a decellularized tissue matrix have shown better predictivity and higher chemoresistance. A single porcine intestine yields material for 150 3D models of breast, lung, colorectal cancer (CRC) or leukemia. The uniquely preserved structure of the basement membrane enables physiological anchorage of endothelial cells and epithelial-derived carcinoma cells. The matrix provides different niches for cell growth: on top as monolayer, in crypts as aggregates and within deeper layers. Dynamic culture in bioreactors enhances cell growth. Comparing gene expression between 2D and 3D cultures, we observed changes related to proliferation, apoptosis and stemness. For drug target predictions, we utilize tumor-specific sequencing data in our in silico model finding an additive effect of metformin and gefitinib treatment for lung cancer in silico, validated in vitro. To analyze mode-of-action, immune therapies such as trispecific T-cell engagers in leukemia, as well as toxicity on non-cancer cells, the model can be modularly enriched with human endothelial cells (hECs), immune cells and fibroblasts. Upon addition of hECs, transmigration of immune cells through the endothelial barrier can be investigated. In an allogenic CRC model we observe a lower basic apoptosis rate after applying PBMCs in 3D compared to 2D, which offers new options to mirror antigen-specific immunotherapies in vitro. In conclusion, we present modular human 3D tumor models with tissue-like features for preclinical testing to reduce animal experiments.},
  language  = {en}
}
@article{KunzGoettlichWallesetal.2017,
  author    = {Kunz, Meik and G{\"o}ttlich, Claudia and Walles, Thorsten and Nietzer, Sarah and Dandekar, Gudrun and Dandekar, Thomas},
  title     = {MicroRNA-21 versus microRNA-34: Lung cancer promoting and inhibitory microRNAs analysed in silico and in vitro and their clinical impact},
  series = {Tumor Biology},
  volume    = {39},
  journal   = {Tumor Biology},
  number    = {7},
  doi       = {10.1177/1010428317706430},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-158399},
  year      = {2017},
  abstract  = {MicroRNAs are well-known strong RNA regulators modulating whole functional units in complex signaling networks. Regarding clinical application, they have potential as biomarkers for prognosis, diagnosis, and therapy. In this review, we focus on two microRNAs centrally involved in lung cancer progression. MicroRNA-21 promotes and microRNA-34 inhibits cancer progression. We elucidate here involved pathways and imbed these antagonistic microRNAs in a network of interactions, stressing their cancer microRNA biology, followed by experimental and bioinformatics analysis of such microRNAs and their targets. This background is then illuminated from a clinical perspective on microRNA-21 and microRNA-34 as general examples for the complex microRNA biology in lung cancer and its diagnostic value. Moreover, we discuss the immense potential that microRNAs such as microRNA-21 and microRNA-34 imply by their broad regulatory effects. These should be explored for novel therapeutic strategies in the clinic.},
  language  = {en}
}
@article{PeindlGoettlichCrouchetal.2022,
  author    = {Peindl, Matthias and G{\"o}ttlich, Claudia and Crouch, Samantha and Hoff, Niklas and L{\"u}ttgens, Tamara and Schmitt, Franziska and Pereira, Jes{\´u}s Guillermo Nieves and May, Celina and Schliermann, Anna and Kronenthaler, Corinna and Cheufou, Danjouma and Reu-Hofer, Simone and Rosenwald, Andreas and Weigl, Elena and Walles, Thorsten and Sch{\"u}ler, Julia and Dandekar, Thomas and Nietzer, Sarah and Dandekar, Gudrun},
  title     = {EMT, stemness, and drug resistance in biological context: a 3D tumor tissue/in silico platform for analysis of combinatorial treatment in NSCLC with aggressive KRAS-biomarker signatures},
  series = {Cancers},
  volume    = {14},
  journal   = {Cancers},
  number    = {9},
  issn      = {2072-6694},
  doi       = {10.3390/cancers14092176},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-270744},
  year      = {2022},
  abstract  = {Epithelial-to-mesenchymal transition (EMT) is discussed to be centrally involved in invasion, stemness, and drug resistance. Experimental models to evaluate this process in its biological complexity are limited. To shed light on EMT impact and test drug response more reliably, we use a lung tumor test system based on a decellularized intestinal matrix showing more in vivo-like proliferation levels and enhanced expression of clinical markers and carcinogenesis-related genes. In our models, we found evidence for a correlation of EMT with drug resistance in primary and secondary resistant cells harboring KRAS\(^{G12C}\) or EGFR mutations, which was simulated in silico based on an optimized signaling network topology. Notably, drug resistance did not correlate with EMT status in KRAS-mutated patient-derived xenograft (PDX) cell lines, and drug efficacy was not affected by EMT induction via TGF-β. To investigate further determinants of drug response, we tested several drugs in combination with a KRAS\(^{G12C}\) inhibitor in KRAS\(^{G12C}\) mutant HCC44 models, which, besides EMT, display mutations in P53, LKB1, KEAP1, and high c-MYC expression. We identified an aurora-kinase A (AURKA) inhibitor as the most promising candidate. In our network, AURKA is a centrally linked hub to EMT, proliferation, apoptosis, LKB1, and c-MYC. This exemplifies our systemic analysis approach for clinical translation of biomarker signatures.},
  language  = {en}
}