Geneviève Dupont

Protein folding and protein maturation largely occur in the controlled environment of the Endoplasmic Reticulum (ER). Perturbation to the correct functioning of this organelle leads to altered proteostasis and accumulation of misfolded proteins in the ER lumen. This condition is commonly known as ER stress and is appearing as an important contributor in the pathogenesis of several human diseases. Monitoring of the quality control processes is mediated by the Unfolded Protein Response (UPR). This response consists in a complex network of signalling pathways that aim to restore protein folding and ER homeostasis. Conditions in which UPR is not able to overcome ER stress lead to a switch of the UPR signalling program from an adaptive to a pro‐apoptotic one, revealing a key role of UPR in modulating cell fate decisions. Because of its high complexity and its involvement in the regulation of different cellular outcomes, UPR has been the centre of the development of computational models, which tried to better dissect the role of UPR or of its specific components in several contexts. In this review, we go through the existing mathematical models of UPR. We emphasize how their study contributed to an improved characterization of the role of this intricate response in the modulation of cellular functions.

During development, cells from a population of common progenitors evolve towards different fates characterized by distinct levels of specific transcription factors, a process known as cell differentiation. This evolution is governed by gene regulatory networks modulated by intercellular signalling. In order to evolve towards distinct fates, cells forming the population of common progenitors must display some heterogeneity. We applied a modelling approach to obtain insights into the possible sources of cell-to-cell variability initiating the specification of cells of the inner cell mass into epiblast or primitive endoderm cells in early mammalian embryo. At the single-cell level, these cell fates correspond to three possible steady states of the model. A combination of numerical simulations and bifurcation analyses predicts that the behaviour of the model is preserved with respect to the source of variability and that cell–cell coupling induces the emergence of multiple steady states associated with various cell fate configurations, and to a distribution of the levels of expression of key transcription factors. Statistical analysis of these time-dependent distributions reveals differences in the evolutions of the variance-to-mean ratios of key variables of the system, depending on the simulated source of variability, and, by comparison with experimental data, points to the rate of synthesis of the key transcription factor NANOG as a likely initial source of heterogeneity.