Thesis (MSc)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: This thesis involved the development and application of network approaches for the construction, analysis and visualization of phylogenomic and transcriptomic networks. A co-evolutionary network model of grapevine genes was constructed based on three mechanisms of evolution. The investigation of local neighbourhoods of this network revealed groups of functionally related genes, illustrating that the multi-mechanism evolutionary model was identifying groups of potentially co-evolving genes. An extended network definition, namely 3-way networks, was investigated, in which edges model relationships between triplets of objects. Strategies for weighting and pruning these 3-way networks were developed and applied to a phylogenomic dataset of 211 bacterial genomes. These 3-way bacterial networks were compared to standard 2-way network models constructed from the same dataset. The 3-way networks modelled more complex relationships and revealed relationships which were missed by the two-way network models. Network meta-modelling was explored in which global network and node-bynode network comparison techniques were applied in order to investigate the effect of the similarity metric chosen on the topology of multiple types of networks, including transcriptomic and phylogenomic networks. Two new network comparison techniques were developed, namely PCA of Topology Profiles and Cross-Network Topological Overlap. PCA of Topology Profiles compares networks based on a selection of network topology indices, whereas Cross- Network Topological Overlap compares two networks on a node-by-node level, identifying nodes in two networks with similar neighbourhood topology and thus highlighting areas of the networks with conflicting topologies. These network comparison methods clearly indicated how the similarity metric chosen to weight the edges of the network influences the resulting network topology, consequently influencing the biological interpretation of the networks. / AFRIKAANSE OPSOMMING: Hierdie tesis hou verband met die ontwikkeling en toepassing van netwerk benaderings vir die konstruksie, analise en visualisering van filogenomiese en transkriptomiese netwerke. 'n Mede-evolusionêre netwerk model van wingerdstok gene is gebou, gebaseerd op drie meganismes van evolusie. Die ondersoek van plaaslike omgewings van die netwerk het groepe funksioneel verwante gene aan die lig gebring, wat daarop dui dat die multi-meganisme evolusionêre model groepe van potensieele mede-evolusieerende gene identifiseer. 'n Uitgebreide netwerk definisie, naamliks 3-gang netwerke, is ondersoek, waarin lyne die verhoudings tussen drieling voorwerpe voorstel. Strategieë vir weeg en snoei van hierdie 3-gang netwerke was ontwikkel en op 'n filogenomiese datastel van 211 bakteriële genome toegepas. Hierdie 3-gang bakteriële netwerke is met die standaard 2-gang netwerk modelle wat saamgestel is uit dieselfde datastel vergelyk. Die 3-gang netwerke het meer komplekse verhoudings gemodelleer en het verhoudings openbaar wat deur die tweerigting-netwerk modelle gemis is. Verder is netwerk meta-modellering ondersoek waarby globalle netwerk en punt-vir-punt netwerk vergelykings tegnieke toegepas is, met die doel om die effek van die ooreenkoms-maatstaf wat gekies is op die topologie van verskeie tipes netwerke, insluitend transcriptomic en filogenomiese netwerke, te bepaal. Twee nuwe netwerk-vergelyking tegnieke is ontwikkel, naamlik "PCA of Topology Profiles" en"Cross-Network Topological Overlap". PCA van Topologie Profiele vergelyk netwerke gebaseer op 'n seleksie van netwerk topologie indekse, terwyl Cross-netwerk Topologiese Oorvleuel vergelyk twee netwerke op 'n punt-vir-punt vlak, en identifiseer punte in twee netwerke met soortgelyke lokale topologie en dus lê klem op gebiede van die netwerke met botsende topologieë. Hierdie netwerk-vergelyking metodes dui duidelik aan hoe die ooreenkoms maatstaf wat gekies is om die lyne van die netwerk gewig te gee, die gevolglike netwerk topologie beïnvloed, wat weer die biologiese interpretasie van die netwerke kan beïnvloed.