Reference : METABOLIC MODELLING BASED APPROACH TO IDENTIFY TAILORED METABOLIC DRUGS
Dissertations and theses : Doctoral thesis
Life sciences : Multidisciplinary, general & others
Systems Biomedicine
http://hdl.handle.net/10993/48524
METABOLIC MODELLING BASED APPROACH TO IDENTIFY TAILORED METABOLIC DRUGS
English
Bintener, Tamara Jean Rita mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM) >]
17-Dec-2020
University of Luxembourg, ​​Luxembourg
Docteur en Biologie
XXII, 198
Sauter, Thomas
Linster, Carole
Fondi, Marco
Wilmes, Paul
Mardinoglu, Adil
[en] Metabolic Modelling ; drug repositioning ; cancer
[en] Cancer is one of the leading causes of death worldwide with no efficient cure. Even though the currently existing cancer therapies show promising results in early detected cancers, the treatment of late state cancers and metastases still presents many obstacles. The development of treatment resistance and non-responsive cancer hampers the already time-consuming endeavour of drug development. To overcome these hurdles, researchers started to turn to computational approaches to unravel the mechanisms of cancer in terms of onset, development, resistance mechanism, metastasis, and immune invasion among others.
The advent of systems biology came not only with the ability to generate large amounts of data but also novel approaches and techniques to analyse them. For example, metabolic modelling approaches can integrate -omics data into a computational representation of metabolism and reconstruct context-specific metabolic models via model-building algorithm such as FASTCORE (Vlassis et al., 2014), FASTCORMICS (Pacheco et al., 2015), and rFASTCORMICS (Pacheco et al., 2019b). Since then, context-specific metabolic models have gained a large area of application that ranges from understanding basic metabolism of microbes, in silico engineering and optimisation of different bacterial strains to applications in human diseases such as biomarker identification and drug target prediction in several diseases, including cancer.
In this thesis, I give an overview on metabolic modelling approaches with a focus on cancer and different methods of drug discovery based on the prediction of in silico targets that enable finding cancer-specific targets to advance personalized medicine. To this end, I have developed a drug prediction workflow that has been successfully used to predict and validate three drugs for repurposing in colorectal cancer using context-specific metabolic models reconstructed via rFASTCORMICS (Pacheco et al., 2019b). Furthermore, we have reconstructed 10 005 context specific models for cancer and its controls to investigate the metabolic differences and rewiring strategies used in cancer. A second application of the workflow for melanoma is currently in progress, several drugs have been predicted for repurposing in melanoma and will be tested in vitro on different melanoma cell lines. Additionally, the most promising drugs will also be tested in combination with current melanoma treatment.
Department of Life Sciences and Medicine (DLSM)
Fonds National de la Recherche - FnR
Researchers
http://hdl.handle.net/10993/48524

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