20.500.12556/RUL-159256 Pristopi za avtomatizirano analizo metabolnih modelov na nivoju genoma Towards automated analysis of genome-scale metabolic models Modeliranje metabolizma na nivoju genoma (angl. genome-scale metabolic modelling, GEM) je pristop, ki povezuje podatke različnih visokozmogljivih tehnologij zajemanja podatkov z modeliranjem in omogoča celovito razumevanje bioloških sistemov. To nam pomaga pri razumevanju kompleksnih bolezni, med katere spada hepatocelularni karcinom (angl. hepatocellular carcinoma, HCC), ki je najpogostejša oblika jetrnega raka in za katerim so oboleli preučevani pacienti. Za vsakega pacienta smo z algoritmi družine GIMME in iMAT rekonstruirali pare modelov tumor in netumor. Pri vrednotenju, katera metoda ekstrakcije modelov (angl. model extraction method, MEM) se je z našimi podatki najbolje izkazala, smo z večimi metodami in pristopi analizirali razlike med modeloma znotraj posameznega para. Na podlagi izvedenih analiz smo ugotovili, da ne moremo trditi, da smo našli algoritem MEM za ekstrakcijo kontekstno specifičnih modelov iz GEM-ov, za katerega bi lahko ugotovili, da je najbolj primeren za vsesplošno uporabo. Na preučevanih podatkih sta se najbolje izkazala algoritma iMAT in tINIT. Genome-scale metabolic modelling (GEM) is an approach that integrates data from different high-throughput sequencing techniques with modelling to provide a comprehensive understanding of biological systems. It helps us understand complex diseases such as Hepatocellular carcinoma (HCC), the most common form of liver cancer affecting also the patients studied. For each patient, we reconstructed a pair of models, namely a tumour model and a non-tumour model - using different algorithms from GIMME and iMAT families. We used a variety of methods and approaches to analyse the differences between the models within each pair to determine which model extraction method (MEM) performed best with our data. Based on the conducted analyses, we have concluded that we cannot claim to have found a MEM for extracting context-specific models from GEMs that we could conclude is the most suitable for general use. The iMAT and tINIT algorithms performed best on the data studied. Metabolni modeli na nivoju genoma visokozmogljive tehnologije zajemanja podatkov metode ekstrakcije modelov GIMME iMAT tINIT Hepatocelularni karcinom Genome-scale metabolic models High-throughput sequencing techniques Model extraction methods GIMME iMAT tINIT Hepatocellular carcinoma true false false Slovenski jezik Angleški jezik Magistrsko delo/naloga 2024-07-04 09:54:27 2024-07-04 09:54:29 2024-07-05 06:55:05 0000-00-00 00:00:00 2024 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 37206 200780547 Skof_Ziva_-_Pristopi_za_avtomatizirano_analizo_metabolnih_modelov_na_nivoju_genoma.pdf Skof_Ziva_-_Pristopi_za_avtomatizirano_analizo_metabolnih_modelov_na_nivoju_genoma.pdf 1 121C28EC32515E17F08BE66A10CBD775 2936fb0ccb5702482286a27e7b50cca6680e0359fb377735b85ca31fd728cd7a a418ef6c-39da-11ef-90ba-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=187216 Fakulteta za računalništvo in informatiko 0 0 0