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Prioritization of candidate cancer genes on chromosome 17q through reverse engineered transcriptional regulatory networks in medulloblastoma groups 3 and 4
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University.
Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, Utbildningsvetenskap och Matematik. (Matematik/tillämpad Matematik)ORCID-id: 0000-0002-1624-5147
Visa övriga samt affilieringar
(Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
Nationell ämneskategori
Bioinformatik (beräkningsbiologi)
Identifikatorer
URN: urn:nbn:se:mdh:diva-42586OAI: oai:DiVA.org:mdh-42586DiVA, id: diva2:1286217
Tillgänglig från: 2019-02-06 Skapad: 2019-02-06 Senast uppdaterad: 2019-08-15Bibliografiskt granskad
Ingår i avhandling
1. Graph theory based approaches for gene prioritization in biological networks: Application to cancer gene detection in medulloblastoma
Öppna denna publikation i ny flik eller fönster >>Graph theory based approaches for gene prioritization in biological networks: Application to cancer gene detection in medulloblastoma
2019 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Networks provide an intuitive and highly adaptable means to model relationships between objects. When translated to mathematical graphs, they become further amenable to a plethora of mathematical operations that allow a detailed study of the underlying relational data. Thus, it is not surprising that networks have evolved to a predominant method for analyzing such data in a vast variety of research fields. However, with increasing complexity of the studied problems, application of network modeling also becomes more challenging. Specifically, given a process to be studied, (i) which interactions are important and how can they be modeled, (ii) how can relationships be inferred from complex and potentially noisy data, and (iii) which methods should be used to test hypotheses or answer the relevant questions? This thesis explores the concept and challenges of network analysis in the context of a well-defined application area, i.e. the prediction of cancer genes from biological networks, with an application to medulloblastoma research.

Medulloblastoma represents the most common malignant brain tumor in children. Currently about 70% of treated patients survive, but they often suffer from permanent cognitive sequelae. Medulloblastoma has previously been shown to harbor at least four distinct molecular subgroups. Related studies have also greatly advanced our understanding of the genetic aberrations associated with MB subgroups. However, to translate such findings to novel and improved therapy options, further insights are required into how the dysregulated genes interact with the rest of the cellular system, how such a cross-talk can drive tumor development, and how the arising tumorigenic processes can be targeted by drugs. Establishing such understanding requires investigations that can address biological processes at a more system-wide level, a task that can be approached through the study of cellular systems as mathematical networks of molecular interactions.

This thesis discusses the identification of cancer genes from a network perspective, where specific focus is placed on one particular type of network, i.e. so called gene regulatory networks that model relationships between genes at the expression level. The thesis outlines the bridge between biological and mathematical network concepts. Specifically, the computational challenge of inferring such networks from molecular data is presented. Mathematical approaches for analyzing these networks are outlined and it is explored how such methods might be affected by network inference. Further focus is placed on dealing with the challenges of establishing a suitable gene expression dataset for network inference in MB. Finally, the thesis is concluded with an application of various network approaches in a hypothesis-driven study in MB, in which various novel candidate genes were prioritized.  

Ort, förlag, år, upplaga, sidor
Västerås: Mälardalens högskola, 2019
Serie
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 286
Nationell ämneskategori
Matematik Bioinformatik (beräkningsbiologi)
Forskningsämne
matematik/tillämpad matematik
Identifikatorer
urn:nbn:se:mdh:diva-42590 (URN)978-91-7485-420-6 (ISBN)
Disputation
2019-03-08, Gamma, Mälardalens högskola, Västerås, 13:15 (Engelska)
Opponent
Handledare
Tillgänglig från: 2019-02-06 Skapad: 2019-02-06 Senast uppdaterad: 2019-02-14Bibliografiskt granskad

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Engström, Christopher
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Bioinformatik (beräkningsbiologi)

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