Context and Subject
Our CEA laboratory is involved in cancer research through the identification of key genes acting in the oncogenic process. To that end our laboratory uses RNAi-based screening approaches. Briefly, it consists in high throughput and high content analysis, of the phenotypic consequences of gene or microRNA depletion at single cell level. This leads to a list of genes / microRNAs implicated in the phenotype of interest (e.g. cell death of prostate cancer cells).
MicroRNAs are small non-coding RNAs (around 20 bases) which are important gene regulators in cells (human, animals, plants, etc.). Discovered in the 90s, more than a thousand human microRNAs are listed; however, their functions remain largely unknown. Our laboratory has developed a network based approach (biostatistics with R software, graph theory) to investigate microRNAs at a system level (that is considering all microRNAs). Thanks to this approach, we already identified 3 microRNAs implicated in cancer (Bhajun et al., Scientific Reports, 2015).
The goal of the proposed PhD thesis is to investigate the role of microRNAs with a system biology approach, in particular in the case of prostate cancer. The PhD student will integrate our high content screening data, and available data bases (data mining) to further identify microRNA implication in prostate cancer.
The work, realized in the bioinformatics team of a biological lab (where the data are generated), will be in collaboration with the center of bioinformatics in Mines ParisTech. It will be codirected by a biologist, a statistician, and co-supervised by a biostatistician.
Background of the student
Bioinformatics or Biostatistics / Applied mathematics / Computing, with a strong interest in biology and health application. Knowledge of R would be a plus.
Application (as early as possible, ideally before April 5th for first round, extended if no outstanding candidate applies)
Applicant must own a Master in bioinformatics, computing, statistics, applied mathematics or related. Applications must be submitted as one pdf file containing all materials. To apply, send an email to Laurent Guyon, and attach the following materials in English:
- A letter motivating the application (cover letter, max 1 page)
- Curriculum vitae (including at least two references)
- Grade transcripts and BSc/MSc diploma, including ranks whenever possible
microRNAs, system biology, cancer, network biology, data mining
- Bhajun, R., Guyon, L., Pitaval, A., Sulpice, E., Combe, S., Obeid, P., ... Gidrol, X. (2015). A statistically inferred microRNA network identifies breast cancer target miR-940 as an actin cytoskeleton regulator. Scientific reports, 5, 8336
- Wu, N., Sulpice, E., Obeid, P., Benzina, S., Kermarrec, F., Combe, S., & Gidrol, X. (2012). The miR-17 family links p63 protein to MAPK signaling to promote the onset of human keratinocyte differentiation. PloS one, 7(9), e45761
- Barrey, E., Saint-Auret, G., Bonnamy, B., Damas, D., Boyer, O., & Gidrol, X. (2011). Pre-microRNA and mature microRNA in human mitochondria. PloS one, 6(5), e20220