| Reference : Prediction of drug targets using metabolic modelling |
| Dissertations and theses : Bachelor/master dissertation | |||
| Life sciences : Multidisciplinary, general & others | |||
| Systems Biomedicine | |||
| http://hdl.handle.net/10993/39848 | |||
| Prediction of drug targets using metabolic modelling | |
| English | |
Bintener, Tamara Jean Rita  [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >] | |
| 15-Jul-2016 | |
| University of Luxembourg, Luxembourg | |
| Master in Integrated Systems Biology (Académique) | |
| 98 | |
| Pacheco, Maria | |
| Sauter, Thomas | |
| Letellier, Elisabeth | |
| Azuaje, Francisco | |
| [en] Metabolic Modelling ; cancer ; drug targets | |
| [en] Cancer, as one of the leading causes of death worldwide, is a disease characterized
by the abnormal and uncontrolled proliferation of cells. Currently available anti-cancer drugs come with a variety of different side effects reducing the quality of life of cancer patients. Due to these severe side effects in anti-cancer therapy it is important to find a compromise between killing the cancer cells (efficiency) and not affecting the healthy cells (toxicity) to improve the quality of life of those patients. There exist different methods of finding new drug targets in cancer such as the in vitro development of new drugs which is very time consuming and expensive. The in silico prediction of targets, on the other hand, is fast and cost effective and allows to make a pre-selection of drug targets based on candidate genes. In this work, I propose a new workflow which implements metabolic modelling for finding metabolic drug targets in cancer. Therefore, context-specific models for cancer (including primary and metastatic melanoma) and healthy controls were reconstructed from Recon 2 (a genome scale metabolic model) using FASTCORMICS and two different expression datasets. In silico single gene deletion was performed in the models to search for potential candidate genes which are essential in cancer (reduce biomass production by 50%) but not in healthy (do not affect ATP production). In a second step, (approved) drugs targeting metabolic genes and their side effects, were extracted from the DrugBank, STITCH and SIDER through data mining and mapped to the metabolic network. A total of 65 possible drug targets have been found. These targets include genes which are known targets for chemotherapeutic agents such as the thymidylate synthase (TYMS), the fatty acid synthase (FASN) or dihydrofolate reductase (DHFR). Furthermore, two anti-cancer agents have been predicted for FASN which have already been proposed for the treatment of cancer. | |
| Students | |
| http://hdl.handle.net/10993/39848 |
| File(s) associated to this reference | ||||||||||||||
|
Fulltext file(s):
| ||||||||||||||
All documents in ORBilu are protected by a user license.
