Description
Date depot: 5 avril 2018
Titre: Biological network reconstruction from a combination of perturbative and non-perturbative data
Directeur de thèse:
Hervé ISAMBERT (PC_Curie)
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Non defini
Resumé:
The objective of this PhD project is to develop and implement a novel information-theoretic method to recontruct biological networks from a combination of perturbative and non-perturbative data. Perturbative data are obtained by intervening on a biological system for instance through gene silencing or drug treatment. Our group has recently developed an efficient algorithmic approach, MIIC, to recontruct a broad range of causal and non-causal networks from non-perturbative data alone (Sella et al. Bioinformatics 2017, Verny et al. PLoS Comput Biol 2017). However, when available, controlled perturbation experiments may also provide useful information in many biological and medical contexts, such as silencing of gene expression or clinical assays for drug treatments.This PhD project aims at extending MIIC network reconstruction method to integrate both perturbative and non-perturbative data, and apply it to large scale gene expression and knockdown data from the literature and public databases as well as breast cancer genetic and clinical data from our collaborators at Institut Curie.References:Sella N, Verny L, Uguzzoni G, Affeldt S, Isambert H: MIIC online: a web server to reconstruct causal or non-causal networks from non-perturbative data. Bioinformatics (2017). [server accessible at https://miic.curie.fr]Verny L, Sella N, Affeldt S, Singh PP, Isambert H: Learning causal networks with latent variables from multivariate information in genomic data. PLoS Comput Biol 13(10):e1005662 (2017).
Doctorant.e: Li Honghao