CHAMBON , Lucie

Positive and negative genetic feedback loops are two main and essential gene regulatory motifs, respectively responsible for cell differentiation, and the emergence of both homeostasis and biological oscillations. They are accurately modeled by highly non-linear ordinary differential equations whose dynamics properly capture their biological...

Genetic negative feedback loops are essential and recurrent biological motifs. They are traditionally described with N-dimensional competitive dynamical systems, composed of highly non-linear Hill functions. The stability property of their unique steady state usually determines the global dynamical behavior: homeostasis under stability or...

In the context of gene regulatory networks, a negative feedback loop is modelled byN-coupled ordinary differential equations, highly non-linear due to Hill functions.This classic dynamical system properly captures the two main biological behavioursarising from this type of recurrent network motif: homeostasis under global stabilityof the unique...

Genetic positive feedback loops are essential for cell decision making and cell differentiation. They are traditionally described by a two-dimensional smooth non-linear differential system composed of Hill functions. The bistability of this classic model properly captures the decision properties of these biological motifs. This paper designs a...

Genetic positive feedback loops are essential for cell differentiation processes. They are accurately modeled with N-dimensional non-linear monotone dynamical systems that display bi-stability: the two stable fixed points represent two distinct cell differentiated states, whereas the unstable fixed point is interpreted as a cell...

In the context of gene regulatory networks, a negative feedback loop is modeled by N-coupled ordinary differential equations. The resulting system is highly non-linear due to the use of smooth Hill functions. This classical dynamical system properly captures the two main biological behaviors arising from this type of recurrent network motif:...

IntroductionThe emergence of AI models for the analysis of 18FDG PET images has opened the way for automatic quantification of clinically-relevant parameters in metastatic cancer patients such as e.g. the number, metabolic volume, and dispersion of lesions. However, current approaches lack specificity as they do not provide information about...