Cell-based multi-scale modeling for systems and synthetic biology: from stochastic gene expression in single cells to spatially organized cell populations

Informations générales
Nom
Bertaux
Prénom
François
Diplôme
Thèse
Année
2016
Détails de la thèse/HDR
Université
Jury
Mark Chaplain
Paul Macklin
Emmanuel Barillot
Alessandra Carbone
Jérémie Roux
Directeur (pour les thèses)
Dirk Drasdo
Gregory Batt
Résumé en anglais
Cell-intrinsic, non-environmental sources of cell-to-cell variability, such as stochastic gene expression, are increasingly recognized to play an important role in the dynamics of tissues, tumors, microbial communities... However, they are usually ignored or oversimplified in theoretical models of cell populations.
In this thesis, we propose a cell-based (each cell is represented individually), multi-scale (cellular decisions are controlled by biochemical reaction pathways simulated in each cell) approach to model the dynamics of cell populations. The main novelty compared to traditional approaches is that the fluctuations of protein levels driven by stochastic gene expression are systematically accounted for (i.e., for every protein in the modeled pathways). This enables to investigate the joint effect of cell-intrinsic and environmental sources of cell-to-cell variability on cell population dynamics. Central to our approach is a parsimonious and principled parameterization strategy for stochastic gene expression models.
The approach is applied on two case studies. First, it is used to investigate the resistance of HeLa cells to the anti-cancer agent TRAIL, which can induce apoptosis specifically in cancer cells. A single-cell model of TRAIL-induced apoptosis is constructed and compared to existing quantitative, single-cell experimental data. The model explains fractional killing and correctly predicts transient cell fate inheritance and reversible resistance, two observed properties that are out of reach of previous models of TRAIL-induced apoptosis, which do not capture the dynamics of cell-to-cell variability. In a second step, we integrate this model into multi-cellular simulations to study TRAIL resistance in virtual scenarios constructed to help bridging the gap between standard in-vitro assays and the response of in-vivo tumors. More precisely, we consider the long-term response of multi-cellular spheroids to repeated TRAIL treatments. Analysis of model simulations points to a novel, mechanistic explanation for transient resistance acquisition, which involves the targeted degradation of activated proteins and a differential turnover between pro- and anti- apoptotic proteins.
Second, we apply our approach to a synthetic spatial patterning system in yeast cells developed by collaborators. Focusing first on a sensing circuit responding to a messenger molecule, we construct a single-cell model that accurately capture the response kinetics of the circuit as observed in flow cytometry data. We then integrate this model into multi-cellular simulations and show that the response of spatially-organized micro-colonies submitted to gradients of messenger molecules is correctly predicted. Finally, we incorporate a model of a killing circuit and compare the predicted patterns of dead or alive cells with experimental data, yielding insights into how the circuit parameters translate into multi-cellular organization phenotypes.
Our modeling approach has the potential to accelerate the obtention of more quantitative and predictive models of cell populations that encompass the molecular scale.