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Network analysis of a complex disease: the gut microbiota in the inflammatory bowel disease case

Hu, Mirko (2022) Network analysis of a complex disease: the gut microbiota in the inflammatory bowel disease case. Advisor: Gili, Prof. Tommaso. Coadvisor: Caldarelli, Prof. Guido . pp. 148. [IMT PhD Thesis]

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The gut microbiota contains hundreds of types of microbes and dysbiosis can lead to infammatory bowel diseases (IBD), which comprise Crohn’s disease (CD) and ulcerative colitis (UC). Due to the complex nature of the IBD, it is interest- ing to understand the differences between a control (NI) and an IBD gut microbiome by using new tools offered by net- work science. In particular, when metagenomic data are con- sidered, it is possible to build networks according to the co-variance, the co-occurrence and multiple layers of networks(multilayer networks). In addition to the construction of the networks, an analysis of the differential expressed pathways is carried out, several centrality measures are calculated, and community detection is performed to explore the topological differences between the diagnosis networks. The analysis of the correlation network topology highlights that, in IBD net-works, the pathway involving coenzyme A of the unclassifed species becomes central. Furthermore, the modularity in the IBD networks is higher. In both the correlation network and the co-occurrence network, the modules belonging to B. ova-tus and B. caccae are positioned differently in each diagnosis. Furthermore, the difference between the NI and the UC diag- nosis networks lies in a change in the wiring that preserves the centralities. Moreover, the fundamental role of two of the Roseburia species in the NI is evidenced. A further step will consist of identifying the minimum number of pathways on which it would be ideal to intervene to drive the system back to a healthy state by the precision medicine way of operating.

Item Type: IMT PhD Thesis
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
PhD Course: Computer science and systems engineering
Identification Number: https://doi.org/10.13118/imtlucca/e-theses/353/
NBN Number: urn:nbn:it:imtlucca-28311
Date Deposited: 17 Jun 2022 09:28
URI: http://e-theses.imtlucca.it/id/eprint/353

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