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This project was built upon a previous study (Johansson et al.2020) that tested multiple drugs on glioblastoma cell lines and found a big division between the drug response for proteasome inhibitors. The aim of this project was to try to obtain a better insight into the differences between the two drug response subgroups’ processes by creating and comparing two genome scale metabolic models (GEMs) of the two subgroups. To do this, genomescale metabolic models were made for each cell line and later merged after its proteasome inhibitor response to obtain two general models. After having multiple models for each cell line and two general drug response models, comparisons could be made. Overall, the differences between cell lines were larger than the differences between drug responses, but some differences could still be seen. Some differences in the number of reactions in subsystems were found between the two general GEMs, where the Ureacycle subsystem showed the largest difference between the two models. Another difference was in the metabolic activity of the models, where the sensitive model passed ten tasks which the resistant model could not. The last and the most important comparison was essentiality analysis which gave a multitude of essential genes but only twelve genes that were unique to the twogeneral GEMs. Nine genes for the resistant model and three for the sensitive. Out of these genes CYP51A1 and FDFT1, for the resistant model, and genes RBP1 and CYP27A1, for the sensitive model, had already been in at least one study regarding Glioblastoma or Proteasome Inhibitors. Since some of the found genes already seem to have been found interesting for PIs or glioblastoma treatment the unique genes from the essentiality analysis could be interesting to look more into in the future.

In recent years, the edible biomedicinal products called nutraceuticals have been becoming more popular among the pharmaceutical industries and the consumers. In the process of developing nutraceuticals, in silico approaches play an important role in structural elucidation, receptor-ligand interactions, drug designing etc., that critically help the laboratory experiments to avoid biological and financial risk. In this thesis, three nutraceuticals possessing antimicrobial and anticancer activities have been studied. Firstly, a tertiary structure was elucidated for a coagulant protein (MO2.1) of Moringa oleifera based on homology modeling and also studied its oligomerization that is believed to interfere with its medicinal properties. Secondly, the antimicrobial efficiency of a limonoid from neem tree called ‘azadirachtin’ was studied with a bacterial (Proteus mirabilis) detoxification agent, glutathione S-transferase, to propose it as a potent drug candidate for urinary tract infections. Thirdly, sequence specific binding activity was analyzed for a plant alkaloid called ‘palmatine’ for the purpose of developing intercalators in cancer therapy. Cumulatively, we have used in silico methods to propose the structure of an antimicrobial peptide and also to understand the interactions between protein and nucleic acids with these nutraceuticals. / <p>QC 20130531</p>

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In this study, we searched for significant hypo and hyper methylation CpG (5'-C-phosphate-G-3') probes from The Cancer Genome Atlas (TCGA) datasets. First, the relationship between hypo and hypermethylation pattern in significantly expressed genes associated in pancreatic ductal adenocarcinoma (PDAC) was analyzed using computational methodologies in R package. This was done by combining DNA methylation (DM) and gene expression (GE) information, and their corresponding metadata (i.e., clinical data and molecular subtypes) and saved as R files. Next, examination of differentially methylated CpG sites (DMCs) between two groups (normal vs tumor) was identified gene sets. From this analysis, we found nine (09) overexpressed hypomethylated and six (06) under expressed hypermethylated genes near significant CpG probes. Results from this work will shed light on the relationship between CpG methylation and gene expression associated with PDAC.

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