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Graph theory based approaches for gene prioritization in biological networks: Application to cancer gene detection in medulloblastoma
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics.
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
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.  

Place, publisher, year, edition, pages
Västerås: Mälardalens högskola , 2019.
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 286
National Category
Mathematics Bioinformatics (Computational Biology)
Research subject
Mathematics/Applied Mathematics
Identifiers
URN: urn:nbn:se:mdh:diva-42590ISBN: 978-91-7485-420-6 (print)OAI: oai:DiVA.org:mdh-42590DiVA, id: diva2:1286239
Public defence
2019-03-08, Gamma, Mälardalens högskola, Västerås, 13:15 (English)
Opponent
Supervisors
Available from: 2019-02-06 Created: 2019-02-06 Last updated: 2019-02-14Bibliographically approved
List of papers
1. Graph Centrality Based Prediction of Cancer Genes
Open this publication in new window or tab >>Graph Centrality Based Prediction of Cancer Genes
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2016 (English)In: Engineering Mathematics II: Algebraic, Stochastic and Analysis Structures for Networks, Data Classification and Optimization / [ed] Sergei Silvestrov; Milica Rancic, Springer, 2016, p. 275-311Chapter in book (Refereed)
Abstract [en]

Current cancer therapies including surgery, radiotherapy and chemotherapy are often plagued by high failure rates. Designing more targeted and personalized treatment strategies requires a detailed understanding of druggable tumor drivergenes. As a consequence, the detection of cancer driver genes has evolved to a critical scientific field integrating both high-through put experimental screens as well as computational and statistical strategies. Among such approaches, network based prediction tools have recently been accentuated and received major focus due to their potential to model various aspects of the role of cancer genes in a biological system. In this chapter, we focus on how graph centralities obtained from biological networks have been used to predict cancer genes. Specifically, we start by discussing the current problems in cancer therapy and the reasoning behind using network based cancer gene prediction, followed by an outline of biological networks, their generation and properties. Finally, we review major concepts, recent results as well as future challenges regarding the use of graph centralities in cancer gene prediction.

Place, publisher, year, edition, pages
Springer, 2016
Series
Springer Proceedings in Mathematics and Statistics, ISSN 2194-1009 ; 179
Keywords
graph, graph centrality, biological networks, cancer therapies, cancer driver genes, biological system
National Category
Computational Mathematics Bioinformatics (Computational Biology) Bioinformatics and Systems Biology Genetics Medical and Health Sciences
Research subject
Mathematics/Applied Mathematics
Identifiers
urn:nbn:se:mdh:diva-33381 (URN)2-s2.0-85012877104 (Scopus ID)978-3-319-42104-9 (ISBN)978-3-319-42105-6 (ISBN)
Available from: 2016-10-11 Created: 2016-10-11 Last updated: 2019-02-06Bibliographically approved
2. Loss of Conservation of Graph Centralities in Reverse-engineered Transcriptional Regulatory Networks
Open this publication in new window or tab >>Loss of Conservation of Graph Centralities in Reverse-engineered Transcriptional Regulatory Networks
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2017 (English)In: Methodology and Computing in Applied Probability, ISSN 1387-5841, E-ISSN 1573-7713, ISSN 1387-5841, Vol. 19, no 4, p. 1095-1105Article in journal (Refereed) Published
Abstract [en]

Graph centralities are commonly used to identify and prioritize disease genes in transcriptional regulatory networks. Studies on small networks of experimentally validated protein-protein interactions underpin the general validity of this approach and extensions of such findings have recently been proposed for networks inferred from gene expression data. However, it is largely unknown how well gene centralities are preserved between the underlying biological interactions and the networks inferred from gene expression data. Specifically, while previous studies have evaluated the performance of inference methods on synthetic gene expression, it has not been established how the choice of inference method affects individual centralities in the network. Here, we compare two gene centrality measures between reference networks and networks inferred from corresponding simulated gene expression data, using a number of commonly used network inference methods. The results indicate that the centrality of genes is only moderately conserved for all of the inference methods used. In conclusion, caution should be exercised when inspecting centralities in reverse-engineered networks and further work will be required to establish the use of such networks for prioritizing disease genes.

Place, publisher, year, edition, pages
Springer, 2017
Keywords
Transcriptional regulatory network inference,  Simulated gene expression,  Graph centrality
National Category
Probability Theory and Statistics Bioinformatics and Systems Biology Bioinformatics (Computational Biology)
Research subject
Mathematics/Applied Mathematics
Identifiers
urn:nbn:se:mdh:diva-36593 (URN)10.1007/s11009-017-9554-7 (DOI)000413792200006 ()2-s2.0-85016734266 (Scopus ID)
Conference
15th Applied Stochastic Models and Data Analysis International Conference (ASMDA), Univ Piraeus, Piraeus, GREECE, JUN 30-JUL 04, 2015
Funder
Swedish Childhood Cancer Foundation
Available from: 2017-09-30 Created: 2017-10-01 Last updated: 2019-02-06Bibliographically approved
3. Prediction of high centrality nodes from reverse-engineered transcriptional regulator networks
Open this publication in new window or tab >>Prediction of high centrality nodes from reverse-engineered transcriptional regulator networks
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2016 (English)In: Proocedings of the 4th Stochastic Modeling Techniques and Data Analysis International Conference with Demographics Workshop / [ed] Christos H Skiadas, 2016, p. 517-531Conference paper, Published paper (Refereed)
Abstract [en]

The prioritization of genes based on their centrality in biological networkshas emerged as a promising technique for the prediction of phenotype related genes.A number of methods have been developed to derive one such type of network, i.e.transcriptional regulatory networks, from expression data. In order to reliably prioritizegenes from such networks, it is crucial to investigate how well the inferencemethods reconstruct the centralities that exist in the true biological system. We haverecently reported that the correlation of centrality rankings between reference andinferred networks is only modest when using an unbiased inference approach. In thisstudy we extend on these results and demonstrate that the correlation remains modestalso when using a biased inference utilizing a priori information about transcriptionfactors. However, we show further that despite this lack of a strong correlation, theinferred networks still allow a signicant prediction of genes with high centralities inthe reference networks.

Keywords
Transcriptional network inference, network inference, graph centrality, degree, betweenness.
National Category
Bioinformatics (Computational Biology) Probability Theory and Statistics
Research subject
Mathematics/Applied Mathematics
Identifiers
urn:nbn:se:mdh:diva-36583 (URN)
Conference
4th Stochastic Modeling Techniques and Data Analysis International Conference with Demographics Workshop
Available from: 2017-09-30 Created: 2017-09-30 Last updated: 2019-02-06Bibliographically approved
4. Batch-normalization of cerebellar and medulloblastoma gene expression datasets utilizing empirically defined negative control genes
Open this publication in new window or tab >>Batch-normalization of cerebellar and medulloblastoma gene expression datasets utilizing empirically defined negative control genes
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2019 (English)In: Article in journal (Refereed) Epub ahead of print
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:mdh:diva-42585 (URN)
Available from: 2019-02-06 Created: 2019-02-06 Last updated: 2019-02-06
5. Prioritization of candidate cancer genes on chromosome 17q through reverse engineered transcriptional regulatory networks in medulloblastoma groups 3 and 4
Open this publication in new window or tab >>Prioritization of candidate cancer genes on chromosome 17q through reverse engineered transcriptional regulatory networks in medulloblastoma groups 3 and 4
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(English)Manuscript (preprint) (Other academic)
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:mdh:diva-42586 (URN)
Available from: 2019-02-06 Created: 2019-02-06 Last updated: 2019-02-06

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