Mots-Clés

Artificial intelligence, computational biology, drug discovery, logic programming, Boolean networks

Description

Computational modeling of biased signaling in G protein-coupled receptors

Context

An active area of research is to study the pharmacological efficacy of GPCRs 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. Kinetic experiments, that measure the activity of several downstream effectors of a receptor after ligand binding with respect to time, are now widely available.

Network modeling of biochemical reactions makes it possible to understand the complexity of the functioning of the signaling pathways, to formalize and confront hypotheses with experiences, and to characterize the pharmacological action of new potential ligands, and to predict cellular responses at different scales. Allied with biological knowledge and quantitative measures, necessary for the model selection and calibration, modeling makes it possible to understand the functioning of signaling routes, and becomes an essential tool for the search for new pharmacological strategies.

In our team, we use different methodologies to tackle this problem, i.e., machine learning, differential equations. This thesis project is about exploring this problem through a different lens i.e., how Boolean network methodology may help to compare different ligands between each other while considering the complexity of signaling pathways. This approach will complement the machine learning approach by providing the signaling and networking information and differential equation approach by being able to scale up to larger systems.

Objectives:

The objectives of this PhD thesis are to : 1) develop a tool using logic programming to infer models, representing ligand efficacy, 2) develop or extend an algorithm to sample networks, providing a comprehensive view of the solution space, 3) identify a way to characterize different dynamical behaviors among a set of networks, improving the understanding of pathway variability, and 4) develop an algorithm to select informative conditions, reducing the variability among inferred networks.

The PhD student will implement this methodology on several practical scenarios on interest in the BIOS team, in particular to gain knowledge on the gonadotropins signaling networks and to characterize the pharmacological efficacy of innovative ligands currently developed in the team.

Required skills:

* Background in Computer Science or related fields

* Python, C++, logic programming

* Machine learning

* Linux, Latex, GitHub

* Ability to work as independent as well as a part of a team

* Creative and good communication skills

* English proficiency

Application procedure:

Contact misbah.razzaq@inrae.fr with one merged pdf file containing CV, motivation letter, transcripts (master and bachelor), and contact information of Referee(s)

References:

Razzaq, Misbah, Loic Pauleve, et al. (2018). “Computational discovery of dynamic cell line specific Boolean Networks from multiplex time-course data”. In: PLoS computational biology 14.10, e1006538.

Razzaq, Misbah, Roland Kaminski, et al. (2018). “Computing Diverse Boolean Networks from Phosphoproteomic Time Series Data”. In: International Conference on Computational Methods in Systems Biology. Springer, pp. 59–74.

F. De Pascali, M. A. Ayoub, et al. 2021 Pharmacological Characterization of Low Molecular Weight Biased Agonists at the Follicle Stimulating Hormone Receptor, International Journal of Molecular Sciences, vol. 22, p. 9850.