Master 2 internship

Multilayer Biological Networks Exploration for Premature Aging Diseases

Premature aging (PA) syndromes, also called Progeroid syndromes, are a group of rare genetic disorders that phenotypically recapitulate some of the aspects of physiological aging at an early age. These syndromes are clinically and genetically heterogeneous. They are usually monogenic, i.e., caused by mutations in single genes, but affect different tissues, different loci can lead to similar diseases, and, contrarily, the phenotypes and severity can vary considerably across individuals carrying the same mutations. 

Genes and proteins do not act isolated but rather interact with each other to perform their functions in molecular complexes, pathways, and other biological processes. Mutations in genes and proteins will thereby affect their interactions and consequently the biological processes in which they are involved. Diseases hence arise from network perturbations, and studying the complex biological networks in which genes and proteins participate is a first step towards better understanding the genotype to phenotype relationships in diseases.

We propose a project in which we aim at systematically identifying the network modules associated with the different PA diseases and their causative genes. The student will hence get familiar with biological networks and graph theory. They will apply different methods from this field, such as random walks or community identification. They will also get their hands on the different types of biological networks, which can better describe the complexity of biological systems, like multiplex or heterogeneous networks. Moreover, the selected candidate will apply the acquired knowledge to a concrete biological question: obtaining new insights about premature aging diseases. Therefore, the student will learn about these syndromes and their potential links with physiological aging.     

Our group has extensive experience in the development of computational methods to extract the knowledge contained in biological networks (Didier et al., 2018; Novoa-del-Toro et al., 2020; Valdeolivas et al., 2019; Pio-Lopez et al. 2020; Baptista et al. 2021). We are based in the Faculty of Medicine, located next to the University Hospital of Marseille, La Timone.

 

Expected skills

  • Programming skills in Python or R are necessary.
  • Fluency in English and writing skills are required.
  • Familiarity with network science and/or omics data analysis is a plus. 
  • The candidate should have a background in one of the following domains: Biology, Medicine, Biochemistry, Biotechnology, Pharmacy, Veterinary studies, engineering studies, Chemistry, Physics or Mathematics or related degrees, complemented by basic knowledge in the other domains.

References

Baptista A, Gonzalez A, Baudot A. Universal Multilayer Network Exploration by Random Walk with Restart. arXiv:210704565. 2021 Jul 9

Didier, G., Valdeolivas, A., & Baudot, A. (2018). Identifying communities from multiplex biological networks by randomized optimization of modularity. F1000Research, 7, 1042.

Novoa-del-Toro, E.-M., Mezura-Montes, E., Vignes, M., Magdinier, F., Tichit, L., & Baudot, A. (2020). A Multi-Objective Genetic Algorithm to Find Active Modules in Multiplex Biological Networks. In Cold Spring Harbor Laboratory (p. 2020.05.25.114215).

Pio-Lopez L, Valdeolivas A, Tichit L, Remy É, Baudot A. MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach. Sci Rep. 2021 Dec;11(1):8794.

Valdeolivas, A., Tichit, L., Navarro, C., Perrin, S., Odelin, G., Levy, N., Cau, P., Remy, E., & Baudot, A. (2019). Random walk with restart on multiplex and heterogeneous biological networks. Bioinformatics , 35(3), 497–505.

 

Applications can be sent directly to “anais.baudot – at – univ-amu.fr”. Please make sure your application includes a cover letter and a CV.

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