@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{MehmoodAlsalehWantetal.2023,
  author    = {Mehmood, Rashid and Alsaleh, Alanoud and Want, Muzamil Y. and Ahmad, Ijaz and Siraj, Sami and Ishtiaq, Muhammad and Alshehri, Faizah A. and Naseem, Muhammad and Yasuhara, Noriko},
  title     = {Integrative molecular analysis of DNA methylation dynamics unveils molecules with prognostic potential in breast cancer},
  series = {BioMedInformatics},
  volume    = {3},
  journal   = {BioMedInformatics},
  number    = {2},
  issn      = {2673-7426},
  doi       = {10.3390/biomedinformatics3020029},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-321171},
  pages     = {434 -- 445},
  year      = {2023},
  abstract  = {DNA methylation acts as a major epigenetic modification in mammals, characterized by the transfer of a methyl group to a cytosine. DNA methylation plays a pivotal role in regulating normal development, and misregulation in cells leads to an abnormal phenotype as is seen in several cancers. Any mutations or expression anomalies of genes encoding regulators of DNA methylation may lead to abnormal expression of critical molecules. A comprehensive genomic study encompassing all the genes related to DNA methylation regulation in relation to breast cancer is lacking. We used genomic and transcriptomic datasets from the Cancer Genome Atlas (TGCA) Pan-Cancer Atlas, Genotype-Tissue Expression (GTEx) and microarray platforms and conducted in silico analysis of all the genes related to DNA methylation with respect to writing, reading and erasing this epigenetic mark. Analysis of mutations was conducted using cBioportal, while Xena and KMPlot were utilized for expression changes and patient survival, respectively. Our study identified multiple mutations in the genes encoding regulators of DNA methylation. The expression profiling of these showed significant differences between normal and disease tissues. Moreover, deregulated expression of some of the genes, namely DNMT3B, MBD1, MBD6, BAZ2B, ZBTB38, KLF4, TET2 and TDG, was correlated with patient prognosis. The current study, to our best knowledge, is the first to provide a comprehensive molecular and genetic profile of DNA methylation machinery genes in breast cancer and identifies DNA methylation machinery as an important determinant of the disease progression. The findings of this study will advance our understanding of the etiology of the disease and may serve to identify alternative targets for novel therapeutic strategies in cancer.},
  language  = {en}
}