Dynamic gene network inference and prediction of biological intervention experiments in cancer cells

In most cancers, the accumulation of genetic aberrations progressively alters cellular gene programs, leading to aberrant behavior and uncontrolled cell proliferation. In collaboration with an Inserm team, we have collected temporal expression data of genes and proteins composing these abnormal cellular programs in human leukemia cells. This project aims at identifying target genes in these cellular programs. The modification of the expression of these target genes, for example by experimental silencing, should allow to modulate the program of these pathological cells and could lead to new therapeutic approaches in cancer. We wish to use a modeling approach, aiming at inferring a gene network model from gene and protein expression data, and then to make a prediction of the effects of a silencing experiment, in order to identify potential therapeutic targets that could be the object of new biological experiments. The thesis is part of this long-term project.

The candidate must have a good knowledge of statistics and data analysis, and be comfortable with numerical statistical tools. The candidate will be required to work in a multidisciplinary environment and must show a strong interest in biological applications.

Niveau de français requis: Intermédiaire: Vous pouvez parler la langue de manière compréhensible, cohérente et avec assurance sur des sujets de la vie courante qui vous sont familiers.

Niveau d’anglais requis: Intermédiaire supérieur: Vous pouvez utiliser la langue de manière efficace et vous exprimer précisément.