Functional Exploration of Antisense Long Non-Coding Rnas Containing Transposable Elements: a Bioinformatics Approach
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Supplemental Information to Mammadova-Bach Et Al., “Laminin Α1 Orchestrates VEGFA Functions in the Ecosystem of Colorectal Carcinogenesis”
Supplemental information to Mammadova-Bach et al., “Laminin α1 orchestrates VEGFA functions in the ecosystem of colorectal carcinogenesis” Supplemental material and methods Cloning of the villin-LMα1 vector The plasmid pBS-villin-promoter containing the 3.5 Kb of the murine villin promoter, the first non coding exon, 5.5 kb of the first intron and 15 nucleotides of the second villin exon, was generated by S. Robine (Institut Curie, Paris, France). The EcoRI site in the multi cloning site was destroyed by fill in ligation with T4 polymerase according to the manufacturer`s instructions (New England Biolabs, Ozyme, Saint Quentin en Yvelines, France). Site directed mutagenesis (GeneEditor in vitro Site-Directed Mutagenesis system, Promega, Charbonnières-les-Bains, France) was then used to introduce a BsiWI site before the start codon of the villin coding sequence using the 5’ phosphorylated primer: 5’CCTTCTCCTCTAGGCTCGCGTACGATGACGTCGGACTTGCGG3’. A double strand annealed oligonucleotide, 5’GGCCGGACGCGTGAATTCGTCGACGC3’ and 5’GGCCGCGTCGACGAATTCACGC GTCC3’ containing restriction site for MluI, EcoRI and SalI were inserted in the NotI site (present in the multi cloning site), generating the plasmid pBS-villin-promoter-MES. The SV40 polyA region of the pEGFP plasmid (Clontech, Ozyme, Saint Quentin Yvelines, France) was amplified by PCR using primers 5’GGCGCCTCTAGATCATAATCAGCCATA3’ and 5’GGCGCCCTTAAGATACATTGATGAGTT3’ before subcloning into the pGEMTeasy vector (Promega, Charbonnières-les-Bains, France). After EcoRI digestion, the SV40 polyA fragment was purified with the NucleoSpin Extract II kit (Machery-Nagel, Hoerdt, France) and then subcloned into the EcoRI site of the plasmid pBS-villin-promoter-MES. Site directed mutagenesis was used to introduce a BsiWI site (5’ phosphorylated AGCGCAGGGAGCGGCGGCCGTACGATGCGCGGCAGCGGCACG3’) before the initiation codon and a MluI site (5’ phosphorylated 1 CCCGGGCCTGAGCCCTAAACGCGTGCCAGCCTCTGCCCTTGG3’) after the stop codon in the full length cDNA coding for the mouse LMα1 in the pCIS vector (kindly provided by P. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Cellular and Molecular Signatures in the Disease Tissue of Early
Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Mitoxplorer, a Visual Data Mining Platform To
mitoXplorer, a visual data mining platform to systematically analyze and visualize mitochondrial expression dynamics and mutations Annie Yim, Prasanna Koti, Adrien Bonnard, Fabio Marchiano, Milena Dürrbaum, Cecilia Garcia-Perez, José Villaveces, Salma Gamal, Giovanni Cardone, Fabiana Perocchi, et al. To cite this version: Annie Yim, Prasanna Koti, Adrien Bonnard, Fabio Marchiano, Milena Dürrbaum, et al.. mitoXplorer, a visual data mining platform to systematically analyze and visualize mitochondrial expression dy- namics and mutations. Nucleic Acids Research, Oxford University Press, 2020, 10.1093/nar/gkz1128. hal-02394433 HAL Id: hal-02394433 https://hal-amu.archives-ouvertes.fr/hal-02394433 Submitted on 4 Dec 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License Nucleic Acids Research, 2019 1 doi: 10.1093/nar/gkz1128 Downloaded from https://academic.oup.com/nar/advance-article-abstract/doi/10.1093/nar/gkz1128/5651332 by Bibliothèque de l'université la Méditerranée user on 04 December 2019 mitoXplorer, a visual data mining platform to systematically analyze and visualize mitochondrial expression dynamics and mutations Annie Yim1,†, Prasanna Koti1,†, Adrien Bonnard2, Fabio Marchiano3, Milena Durrbaum¨ 1, Cecilia Garcia-Perez4, Jose Villaveces1, Salma Gamal1, Giovanni Cardone1, Fabiana Perocchi4, Zuzana Storchova1,5 and Bianca H. -
Whole Exome Sequencing in Families at High Risk for Hodgkin Lymphoma: Identification of a Predisposing Mutation in the KDR Gene
Hodgkin Lymphoma SUPPLEMENTARY APPENDIX Whole exome sequencing in families at high risk for Hodgkin lymphoma: identification of a predisposing mutation in the KDR gene Melissa Rotunno, 1 Mary L. McMaster, 1 Joseph Boland, 2 Sara Bass, 2 Xijun Zhang, 2 Laurie Burdett, 2 Belynda Hicks, 2 Sarangan Ravichandran, 3 Brian T. Luke, 3 Meredith Yeager, 2 Laura Fontaine, 4 Paula L. Hyland, 1 Alisa M. Goldstein, 1 NCI DCEG Cancer Sequencing Working Group, NCI DCEG Cancer Genomics Research Laboratory, Stephen J. Chanock, 5 Neil E. Caporaso, 1 Margaret A. Tucker, 6 and Lynn R. Goldin 1 1Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 2Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 3Ad - vanced Biomedical Computing Center, Leidos Biomedical Research Inc.; Frederick National Laboratory for Cancer Research, Frederick, MD; 4Westat, Inc., Rockville MD; 5Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; and 6Human Genetics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA ©2016 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol.2015.135475 Received: August 19, 2015. Accepted: January 7, 2016. Pre-published: June 13, 2016. Correspondence: [email protected] Supplemental Author Information: NCI DCEG Cancer Sequencing Working Group: Mark H. Greene, Allan Hildesheim, Nan Hu, Maria Theresa Landi, Jennifer Loud, Phuong Mai, Lisa Mirabello, Lindsay Morton, Dilys Parry, Anand Pathak, Douglas R. Stewart, Philip R. Taylor, Geoffrey S. Tobias, Xiaohong R. Yang, Guoqin Yu NCI DCEG Cancer Genomics Research Laboratory: Salma Chowdhury, Michael Cullen, Casey Dagnall, Herbert Higson, Amy A. -
A Chromosome Level Genome of Astyanax Mexicanus Surface Fish for Comparing Population
bioRxiv preprint doi: https://doi.org/10.1101/2020.07.06.189654; this version posted July 6, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Title 2 A chromosome level genome of Astyanax mexicanus surface fish for comparing population- 3 specific genetic differences contributing to trait evolution. 4 5 Authors 6 Wesley C. Warren1, Tyler E. Boggs2, Richard Borowsky3, Brian M. Carlson4, Estephany 7 Ferrufino5, Joshua B. Gross2, LaDeana Hillier6, Zhilian Hu7, Alex C. Keene8, Alexander Kenzior9, 8 Johanna E. Kowalko5, Chad Tomlinson10, Milinn Kremitzki10, Madeleine E. Lemieux11, Tina 9 Graves-Lindsay10, Suzanne E. McGaugh12, Jeff T. Miller12, Mathilda Mommersteeg7, Rachel L. 10 Moran12, Robert Peuß9, Edward Rice1, Misty R. Riddle13, Itzel Sifuentes-Romero5, Bethany A. 11 Stanhope5,8, Clifford J. Tabin13, Sunishka Thakur5, Yamamoto Yoshiyuki14, Nicolas Rohner9,15 12 13 Authors for correspondence: Wesley C. Warren ([email protected]), Nicolas Rohner 14 ([email protected]) 15 16 Affiliation 17 1Department of Animal Sciences, Department of Surgery, Institute for Data Science and 18 Informatics, University of Missouri, Bond Life Sciences Center, Columbia, MO 19 2 Department of Biological Sciences, University of Cincinnati, Cincinnati, OH 20 3 Department of Biology, New York University, New York, NY 21 4 Department of Biology, The College of Wooster, Wooster, OH 22 5 Harriet L. Wilkes Honors College, Florida Atlantic University, Jupiter FL 23 6 Department of Genome Sciences, University of Washington, Seattle, WA 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.07.06.189654; this version posted July 6, 2020. -
The Landscape of Human Mutually Exclusive Splicing
bioRxiv preprint doi: https://doi.org/10.1101/133215; this version posted May 2, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. The landscape of human mutually exclusive splicing Klas Hatje1,2,#,*, Ramon O. Vidal2,*, Raza-Ur Rahman2, Dominic Simm1,3, Björn Hammesfahr1,$, Orr Shomroni2, Stefan Bonn2§ & Martin Kollmar1§ 1 Group of Systems Biology of Motor Proteins, Department of NMR-based Structural Biology, Max-Planck-Institute for Biophysical Chemistry, Göttingen, Germany 2 Group of Computational Systems Biology, German Center for Neurodegenerative Diseases, Göttingen, Germany 3 Theoretical Computer Science and Algorithmic Methods, Institute of Computer Science, Georg-August-University Göttingen, Germany § Corresponding authors # Current address: Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland $ Current address: Research and Development - Data Management (RD-DM), KWS SAAT SE, Einbeck, Germany * These authors contributed equally E-mail addresses: KH: [email protected], RV: [email protected], RR: [email protected], DS: [email protected], BH: [email protected], OS: [email protected], SB: [email protected], MK: [email protected] - 1 - bioRxiv preprint doi: https://doi.org/10.1101/133215; this version posted May 2, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. -
Open Data for Differential Network Analysis in Glioma
International Journal of Molecular Sciences Article Open Data for Differential Network Analysis in Glioma , Claire Jean-Quartier * y , Fleur Jeanquartier y and Andreas Holzinger Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria; [email protected] (F.J.); [email protected] (A.H.) * Correspondence: [email protected] These authors contributed equally to this work. y Received: 27 October 2019; Accepted: 3 January 2020; Published: 15 January 2020 Abstract: The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes. -
WO 2014/210448 Al 31 December 2014 (31.12.2014) P O P C T
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (10) International Publication Number (43) International Publication Date WO 2014/210448 Al 31 December 2014 (31.12.2014) P O P C T (51) International Patent Classification: BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, A61K 45/00 (2006.01) C12N 15/09 (2006.01) DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, A61K 48/00 (2006.01) HN, HR, HU, ID, IL, IN, IR, IS, JP, KE, KG, KN, KP, KR, KZ, LA, LC, LK, LR, LS, LT, LU, LY, MA, MD, ME, (21) International Application Number: MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, PCT/US2014/044554 OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, (22) International Filing Date: SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, 27 June 2014 (27.06.2014) TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (25) Filing Language: English (84) Designated States (unless otherwise indicated, for every (26) Publication Language: English kind of regional protection available): ARIPO (BW, GH, (30) Priority Data: GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, SZ, TZ, 61/840,21 1 27 June 2013 (27.06.2013) US UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, (71) Applicant: THE BOARD OF REGENTS OF THE UNI¬ EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, VERSITY OF TEXAS SYSTEM [US/US]; 201 W. -
The Kinesin Spindle Protein Inhibitor Filanesib Enhances the Activity of Pomalidomide and Dexamethasone in Multiple Myeloma
Plasma Cell Disorders SUPPLEMENTARY APPENDIX The kinesin spindle protein inhibitor filanesib enhances the activity of pomalidomide and dexamethasone in multiple myeloma Susana Hernández-García, 1 Laura San-Segundo, 1 Lorena González-Méndez, 1 Luis A. Corchete, 1 Irena Misiewicz- Krzeminska, 1,2 Montserrat Martín-Sánchez, 1 Ana-Alicia López-Iglesias, 1 Esperanza Macarena Algarín, 1 Pedro Mogollón, 1 Andrea Díaz-Tejedor, 1 Teresa Paíno, 1 Brian Tunquist, 3 María-Victoria Mateos, 1 Norma C Gutiérrez, 1 Elena Díaz- Rodriguez, 1 Mercedes Garayoa 1* and Enrique M Ocio 1* 1Centro Investigación del Cáncer-IBMCC (CSIC-USAL) and Hospital Universitario-IBSAL, Salamanca, Spain; 2National Medicines Insti - tute, Warsaw, Poland and 3Array BioPharma, Boulder, Colorado, USA *MG and EMO contributed equally to this work ©2017 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol. 2017.168666 Received: March 13, 2017. Accepted: August 29, 2017. Pre-published: August 31, 2017. Correspondence: [email protected] MATERIAL AND METHODS Reagents and drugs. Filanesib (F) was provided by Array BioPharma Inc. (Boulder, CO, USA). Thalidomide (T), lenalidomide (L) and pomalidomide (P) were purchased from Selleckchem (Houston, TX, USA), dexamethasone (D) from Sigma-Aldrich (St Louis, MO, USA) and bortezomib from LC Laboratories (Woburn, MA, USA). Generic chemicals were acquired from Sigma Chemical Co., Roche Biochemicals (Mannheim, Germany), Merck & Co., Inc. (Darmstadt, Germany). MM cell lines, patient samples and cultures. Origin, authentication and in vitro growth conditions of human MM cell lines have already been characterized (17, 18). The study of drug activity in the presence of IL-6, IGF-1 or in co-culture with primary bone marrow mesenchymal stromal cells (BMSCs) or the human mesenchymal stromal cell line (hMSC–TERT) was performed as described previously (19, 20). -
Development of Novel Analysis and Data Integration Systems to Understand Human Gene Regulation
Development of novel analysis and data integration systems to understand human gene regulation Dissertation zur Erlangung des Doktorgrades Dr. rer. nat. der Fakult¨atf¨urMathematik und Informatik der Georg-August-Universit¨atG¨ottingen im PhD Programme in Computer Science (PCS) der Georg-August University School of Science (GAUSS) vorgelegt von Raza-Ur Rahman aus Pakistan G¨ottingen,April 2018 Prof. Dr. Stefan Bonn, Zentrum f¨urMolekulare Neurobiologie (ZMNH), Betreuungsausschuss: Institut f¨urMedizinische Systembiologie, Hamburg Prof. Dr. Tim Beißbarth, Institut f¨urMedizinische Statistik, Universit¨atsmedizin, Georg-August Universit¨at,G¨ottingen Prof. Dr. Burkhard Morgenstern, Institut f¨urMikrobiologie und Genetik Abtl. Bioinformatik, Georg-August Universit¨at,G¨ottingen Pr¨ufungskommission: Prof. Dr. Stefan Bonn, Zentrum f¨urMolekulare Neurobiologie (ZMNH), Referent: Institut f¨urMedizinische Systembiologie, Hamburg Prof. Dr. Tim Beißbarth, Institut f¨urMedizinische Statistik, Universit¨atsmedizin, Korreferent: Georg-August Universit¨at,G¨ottingen Prof. Dr. Burkhard Morgenstern, Weitere Mitglieder Institut f¨urMikrobiologie und Genetik Abtl. Bioinformatik, der Pr¨ufungskommission: Georg-August Universit¨at,G¨ottingen Prof. Dr. Carsten Damm, Institut f¨urInformatik, Georg-August Universit¨at,G¨ottingen Prof. Dr. Florentin W¨org¨otter, Physikalisches Institut Biophysik, Georg-August-Universit¨at,G¨ottingen Prof. Dr. Stephan Waack, Institut f¨urInformatik, Georg-August Universit¨at,G¨ottingen Tag der m¨undlichen Pr¨ufung: der 30. M¨arz2018