Phd Position F/m Exploring The Role of Gene

Le descriptif de l’offre ci-dessous est en Anglais_

**Type de contrat **:CDD

**Niveau de diplôme exigé **:Bac + 5 ou équivalent

**Fonction **:Doctorant

**A propos du centre ou de la direction fonctionnelle**:
The Inria research centre in Lyon is the 9th Inria research centre, formally created in January 2022. It brings together approximately 300 people in 17 research teams and research support services.

Its staff are distributed in Villeurbanne, Lyon Gerland, and Saint-Etienne.

The Lyon centre is active in the fields of software, distributed and high-performance computing, embedded systems, quantum computing and privacy in the digital world, but also in digital health and computational biology.

**Contexte et atouts du poste**:
Within a long-term collaboration of the BEAGLE team with Dr. A. Fleischmann and Dr. R. Singh at Brown University (USA), you will investigate the gene regulatory processes involved in learning of odor responses in mice. Your goal will be to use both numerical (computational) modeling and bioinformatic data analysis to understand better how gene regulation impacts neuronal and glial behavior during learning in the olfactory system of mice, and vice versa, how learning an odor-response association has consequences for gene expression and gene regulation.

This is a three-year position for a PhD student. You will be advised by Anton Crombach (Centre Inria de Lyon), with expert input from Hugues Berry (Centre Inria de Lyon) and whilst working together with other members of the project. You will be located at Inria’s La Doua site with opportunities to visit the collaborators at Brown University.

You will benefit from the expertise at Inria regarding bioinformatic data analysis and mathematical and computational modeling of biological systems; and you will profit from world-class knowledge at Brown University regarding neuro-biology and machine learning aspects of the project. Overall, the project will foster your career in terms of building up cutting-edge expertise on numerical simulation techniques, high-dimensional data analysis, network analysis, statistical and machine learning, and data visualization techniques - areas that are strongly demanded in academia and industry.

**Mission confiée**:
This PhD project addresses a fundamental question in biology: how do we learn? As an example of learning, we study how mice learn to associate an odor with a positive or negative reward (water with sucrose or quinine, respectively). Specifically, we are interested in the impact of learning on gene expression in the mouse olfactory cortex. To this end, our collaborators at Brown University have generated a rich single-cell RNA sequencing dataset of olfactory cortex (piriform cortex) across a variety of conditions. Complementing the experimental work, our goal is to provide a formal framework to reason about the interplay between neuronal circuitry and the gene regulatory dynamics inside neurons and glial cells. This means we have two main objectives:

- Create novel computational/mathematical models of olfactory neuronal circuits that interact with their associated gene regulatory networks in neurons and glia.
- Perform bioinformatic data analysis of single-cell data to explore the role of glial cells, e.g. astrocytes, in learning, and how they interact with neurons in this process.

We will explore the regulation of synaptic strengths by means of evolutionary simulations. Using evolutionary approaches allows one to discover not only which mechanisms function correctly, but it also gives us an understanding of evolutionary properties, such as evolvability and robustness. Simulation results will then be used to formulate novel hypotheses that we can test given our collaborator’s data, public datasets, and dedicated experiments.

Objective 2 will be done in close collaboration with the data analysis efforts by our collaborators at Brown University. They are focused on neurons that changed gene expression patterns due to learning. Complementing these efforts, we will focus on glial cells, exemplified here by astrocytes. Astrocytes are known to be involved in learning and our goal will be to describe, for olfactory cortex, how astrocytes change their expression dynamics. Next, by reaching out to local collaborators, Hugues Berry and Olivier Raineteau, we will investigate how interactions between neurons and astrocytes change due to learning. Insights from our data analysis efforts will allow us to go beyond mathematical models that are neuron-only and propose novel models that take into account the extensive cross-talk between neurons and glial cell types in the brain.

**Principales activités**:
Your main tasks are as follows:

- obtain a good overview of the relevant literature (“state of the art”) regarding gene regulation, neuronal circuits, interactions between neurons and glial, single-cell data analysis, numerical simulations of evolution. You will be reading literature f