Speaker
Description
An active area of research in pharmacology and drug discovery applies to biased signaling: the ability of a ligand to selectively activate some signal transduction pathways as compared to the native ligand acting at the same receptor. At the practical level, experimentalists seek to quantify ligand bias in order to classify ligands according to their selectivity. One popular method uses the so-called operational model to fit dose-response curves.
In this presentation, I will review the main limitations of this methodology, recently pointed out by our group and others. Our objective is then to design a method that fully take into account the kinetic nature of signaling pathways and as well as their possible cross-talks. Kinetic experiments, that measure the activity of several downstream effectors of a receptor after ligand binding with respect to time, are now widely available. I will explain how one can exploit such data and dynamical reaction network modeling with suitable statistical framework to provide a complete “bias map” of a ligand, compared to the native ligand, that successfully answer to our objective.
Going further, we can extend this methodology to take into account recently discovered compartmentalised signaling, which can eventually lead to spatial or location signaling bias. We have thus build a dynamical model that incorporate endocytosis/recycling event and spatial specificity, helping to quantify kinetic experiments under various receptor trafficking perturbations. This new methodology is formulated as either partial differential equations (PDE) or piecewise deterministic Markov processes (PDMP), which calls for interesting new developments in applied mathematics.
