Two financed PhD positions on modelling cell differentiation

Modelling the effect of noise in gene regulatory network governing early mammalian development (starting from 1 October 2020 or later – (1 +3) years positions)

We are seeking two PhD students to work in the Unit of Theoretical Chronobiology (Brussels) in conjunction with Yannick De Decker on a project related to the modelling of cell differentiation. This interdisciplinary project will focus on the relation between the structure of the gene regulatory network (GRN) governing cell differentiation and its sensitivity to noise.

One of the positions is devoted to the study of the sensitivity of realistic GRN configurations to noise, by making use of the theory of stochastic processes. The GRNs that will be explored account for the differentiation of cells of the inner cell mass (ICM) into cells of the epiblast (Epi) or of the primitive endoderm (PrE) in early mammalian development. Our aim is to identify key properties of the networks with respect to noise, by combining bifurcation analysis and stochastic simulations with Gillespie’s algorithm. These properties include the possibility of cells from a pool of common progenitors to evolve towards one of the multiple steady states, i.e., cell fates, and the ability of the GRNs to buffer noise and to lead to reproducible cell populations.

The subject of the second position relates to the initial source of ICM heterogeneity in the mouse embryo. This will be investigated in collaboration with the laboratory of C. Chazaud, GReD, Clermont University. Using single-cell transcriptomics data provided by this group, the candidate will extend our previously published models by incorporating plausible gene candidates in the model and assess if these extended networks are able to account for the emergence of heterogeneity from a population of identical ICM precursor cells, as well as for the properties of specification in terms of population robustness and flexibility towards exogenous treatments.

Applicants must hold a master’s degree in mathematical biology, chemistry, physics, bioengineering or equivalent. Experience with biological and/or stochastic modelling, and connecting models to real data, is a definite advantage. Applicants must be proficient in both written and oral English. Personal and relational qualities will be emphasized.

For information and application, please contact Geneviève Dupont (gdupont@ulb.ac.be).