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  Understanding Regulatory Mechanisms Underlying Stem Cells Helps to Identify Cancer Biomarkers

Nazarieh, M. (2018). Understanding Regulatory Mechanisms Underlying Stem Cells Helps to Identify Cancer Biomarkers. PhD Thesis, Universität des Saarlandes, Saarbrücken. doi:10.22028/D291-27265.

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Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-0001-9D69-9 Versions-Permalink: https://hdl.handle.net/21.11116/0000-0001-9D87-6
Genre: Hochschulschrift

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externe Referenz:
https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/27104 (beliebiger Volltext)
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 Urheber:
Nazarieh, Maryam1, Autor           
Helms, Volker2, Ratgeber
Lengauer, Thomas1, Gutachter           
Affiliations:
1Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society, ou_40046              
2External Organizations, ou_persistent22              

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 Zusammenfassung: Detection of biomarker genes play a crucial role in disease detection and treatment. Bioinformatics offers a variety of approaches for identification of biomarker genes which play key roles in complex diseases. These computational approaches enhance the insight derived from experiments and reduce the efforts of biologists and experimentalists. This is essentially achieved through prioritizing a set of genes with certain attributes. In this thesis, we show that understanding the regulatory mechanisms underlying stem cells helps to identify cancer biomarkers. We got inspired by the regulatory mechanisms of the pluripotency network in mouse embryonic stem cells and formulated the problem where a set of master regulatory genes in regulatory networks is identified with two combinatorial optimization problems namely as minimum dominating set and minimum connected dominating set in weakly and strongly connected components. Then we applied the developed methods to regulatory cancer networks to identify disease-associated genes and anti-cancer drug targets in breast cancer and hepatocellular carcinoma. As not all the nodes in the solutions are critical, we developed a prioritization method to rank a set of candidate genes which are related to a certain disease based on systematic analysis of the genes that are differentially expressed in tumor and normal conditions. Moreover, we demonstrated that the topological features in regulatory networks surrounding differentially expressed genes are highly consistent in terms of using the output of several analysis tools. We compared two randomization strategies for TF-miRNA co-regulatory networks to infer significant network motifs underlying cellular identity. We showed that the edge-type conserving method surpasses the non-conserving method in terms of biological relevance and centrality overlap. We presented several web servers and software packages that are publicly available at no cost. The Cytoscape plugin of minimum connected dominating set identifies a set of key regulatory genes in a user provided regulatory network based on a heuristic approach. The ILP formulations of minimum dominating set and minimum connected dominating set return the optimal solutions for the aforementioned problems. Our source code is publicly available. The web servers TFmiR and TFmiR2 construct disease-, tissue-, process-specific networks for the sets of deregulated genes and miRNAs provided by a user. They highlight topological hotspots and offer detection of three- and four-node FFL motifs as a separate web service for both organisms mouse and human.

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Sprache(n): eng - English
 Datum: 2018-06-2820182018
 Publikationsstatus: Erschienen
 Seiten: 139 p.
 Ort, Verlag, Ausgabe: Saarbrücken : Universität des Saarlandes
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: BibTex Citekey: nazariehphd2017
DOI: 10.22028/D291-27265
 Art des Abschluß: Doktorarbeit

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