PhD Project ESR13


Development of metabolic modelling tools to determine vulnerabilities associated with the metabolic reprogramming induced by MTA therapies on neurodegeneration models and in silico prediction of metabolic targets.

SUPERVISORS

Prof. Marta Cascante, University of Barcelona

Prof. Alexandre Varnek, University of Strasbourg (secondment)

INVOLVED UNIVERSITIES

INVOLED COMPANY

PhD COURSES

Ecole Doctorale des Sciences Chimiques (UNISTRA)
Biotechnology (UB)

OBJECTIVES

The aim of the project is to identify metabolic targets to improve the tolerance to MTA therapies using computational tools.

DESCRIPTION OF THE PROJECT 

The Early Stage researcher (ESR) will first develop the Genome Scale Metabolic Model (GSMM) needed for modelling the metabolic phenotype of the neuron cell models used in the project. With this aim, the ESR will integrate in a generic GSMM the RNAseq data and the metabolomics and other experimental data generated by ESR12, in order to generate specific GSMMs of healthy and neurodegenerative neurons treated or untreated with MTA. These GSMMs will be used by ESR to identify transcriptional networks and metabolic pathways relevant in: i) the development of the neurodegenerative disorders, and in ii) the effect of MTA treatment in neurons. The most promising metabolic targets to be inhibited in combination with MTA will be validated experimentally by another ESR involved in the project (ESR12). Next, the ESR will design novel inhibitors against the identified metabolic targets using fragment based molecular design techniques.

The ESR will be enrolled by the Universitat de Barcelona (www.ub.edu) under the supervision of Prof. Marta Cascante and will be awarded a Double Doctorate degree with the Université de Strasbourg (www.unistra.fr ), under the supervision of Prof. Alexandre Varnek.

The research will involve secondments to the Université de Strasbourg to design the novel inhibitors using fragment based molecular design techniques, and LifeGlimmer (www.lifeglimmer.com), Germany, to learn new computational tools that will help in the analysis of data and in the development of GSMMs.

The selected candidate will participate in the network’s training activities and work placements in the laboratories of the participating academic and industrial partners.

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