20.500.12556/RUL-188485 Uporaba metabolnih modelov na osnovi genoma pri analizi podatkov scRNA-seq raka prostate Application of genome-scale metabolic models in the analysis of scRNA-seq data of prostate cancer Rak je ena najpogostejših in najbolj raznolikih bolezni današnjega sveta. Zaradi njegovega hitrega spreminjanja in rasti je učinkovitost zdravljenja pogosto omejena. Ena od glavnih lastnosti rakavih obolenj je visoka heterogenost celic tako znotraj tumorskega tkiva kot tudi med pacienti z enakim tipom raka. Z namenom raziskovanja tumorske heterogenosti smo na podlagi javno dostopnih podatkov scRNA-seq z algoritmom ftINIT (angl. fast task-driven integrative network inference for tissues) postavili kontekstnospecifične metabolne modele na podlagi genoma (GEM, angl. genome-scale metabolic models). Naš namen je bil preučiti razlike v metabolizmu različnih celičnih tipov v tumorskem tkivu in najti potencialne tarče za zdravljenje. To smo dosegli z uporabo različnih metod, ki so omogočale opazovanje spremenljivih metabolnih nalog, iskanje strukturnih razlik med postavljenimi modeli, identifikacijo podsistemov, ki so oz. niso izraženi v posameznem celičnem tipu rakavega tkiva in identifikacijo morebitnih tarčnih genov za zdravljenje bolezni. Celotna analiza nam je omogočila boljše razumevanje heterogenosti tumorskega tkiva in pokazala uporabnost pristopov sistemske biologije za izboljšanje razumevanje metabolizma raka. Cancer is one of the most common and diverse diseases in todays world. Due to its tendency for rapid change and growth, the effectiveness of treatment is often limited. One of its key characteristics is high heterogeneity, both within the tumor tissue itself and among patients with the same type of cancer. To investigate tumor heterogeneity, we generated context-specific genome-scale metabolic models (GEMs) using publicly available scRNA-seq data and the ftINIT algorithm (fast task-driven integrative network inference for tissues) which offered us greater insight into the metabolism of cancer tissue cells. Our aim was to analyze the tissue and identify potential therapeutic targets. This was achieved through various methods that enabled the observation of variable metabolic tasks, the identification of structural differences between the constructed models, the determination if subsystems are expressed or not expressed in specific cancer cell types, and, ultimately, the identification of potential target genes for treatment. The comprehensive analysis provided a better understanding of tumor tissue and demonstrated the utility of systems biology approaches in understanding the metabolic basis of certain diseases, serving as a foundation for experimental procedures. kontekstnospecifični metabolni modeli heterogenost ftINIT metabolizem rak context-specific metabolic models heterogeneity ftINIT metabolism cancer true false false Slovenski jezik Angleški jezik Diplomsko delo/naloga 2026-09-23 13:00:01 2026-09-23 13:00:10 2026-11-06 13:02:16 0000-00-00 00:00:00 2026 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 26162 304247811 Kolenc_Klara_-_Uporaba_metabolnih_modelov_na_osnovi_genoma_pri_analizi_podatkov_scRNA-seq_raka_p.pdf Kolenc_Klara_-_Uporaba_metabolnih_modelov_na_osnovi_genoma_pri_analizi_podatkov_scRNA-seq_raka_p.pdf 1 75013D50BFF15B39CE0D04F7256E43DF d6e352db7a41deb12b271c62e0457dd96ea6249dbf579f0518eadbc331745ff9 e09b450e-b73d-11f1-8bc5-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=248670 Fakulteta za kemijo in kemijsko tehnologijo 0 0 0