Generalised networks for protein interaction analysis

Thesis

Protein interaction networks (PINs) are mathematical representations of interactions between proteins within organisms. Studying their properties can give insights into biological functions and the importance of proteins, and it can therefore aid in drug- discovery. Graphs are the most common mathematical object used to represent PINs. In this thesis, we investigate generalised mathematical representations of PINs. In particular, we examine multilayer networks (MLNs) and node-weighted networks. These mathematical objects allow the construction of temporal PINs and tissue-specific PINs by combining gene-expression data with PINs. We introduce promiscuity as an information-theoretical measure of a node’s distribution of neighbours across different layers in MLNs. We examine promiscuity in synthetic networks and tissue-specific PINs and find that the vast majority of proteins are not cell-type specific. Integrating temporal gene-expression data with PINs allows us to create temporal PINs in the form of MLNs. We investigate an eigenvector-based temporal centrality in a temporal PIN of yeast during the cell cycle. We thereby examine the change of proteins’ importance over time, which reflects their activity during the cell cycle. We then discuss the detection of community structure in node-weighted networks. For synthetic networks, we show that considering node weights can alter detected community structure. We combine a human PIN with gene-expression data to construct tissue-specific PINs and investigate their community structure. Comparing the detected communities with gene-ontology information, we find some tissue-specific functions of these PINs. Overall, the case studies in this thesis suggest that MLN and node-weighted networks are suitable for the integration of protein-interaction data with other biological data sets.

Authors: Klimm, F

• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: Systems Biology; Network Science; Protein-Protein Interaction; Machine Learning
• Subjects: Statistics; Biomedical Sciences; Bioinformatics; Mathematics