Computational modeling of biased signaling in G protein-coupled receptors

Résumé

 The complexity of pharmacological efficacy at G protein-Coupled Receptors opens the possibility to selectively control their signaling pathways, i.e., the ability of a ligand to selectively activate some signal transduction pathways as compared to the native ligand acting at the same receptor. This potentially holds great opportunities in many fields of biology, including mammal’s reproduction. Kinetic experiments, that measure the activity of several downstream effectors of a receptor after ligand binding with respect to time, are now widely available. The many cross-talks, redundancy and regulatory mechanisms at play within signaling cascades prevent to use direct statistical approach to understand and infer ligand efficacy. This thesis project is about exploring how Boolean network methodology can help to compare different ligands between each other while considering the complexity of signaling pathways.

However, the sparsity of available data results in learning a family of Boolean networks instead of a single network. To reduce the number of networks, we need to get more experimental data, which is usually costly. So the idea is to identify conditions that help to reduce the number of inferred networks computationally without performing more experiments.

Our objectives are to :

1. develop a tool using logic programming (answer set programming) to infer networks which will be able to predict time series signals
2.  sample networks from large set of networks
3.  identify a way to characterize different dynamical behaviors among a set of networks
4. develop an algorithm based on the entropy measure to select informative conditions to make this set of dynamic behaviors less variable.

Contact :

• Pamela Romero Jofré