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Internship - Estimation of Fungal Gene Regulatory
Il y a 4 mois
**At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where, Health for all, Hunger for none’ is no longer a dream, but a real possibility. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so many reasons to join us. If you’re eager to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.**
**Internship - Estimation of fungal gene regulatory networks**
**Internship - Estimation of fungal gene regulatory networks**
Bayer's La Dargoire Research Center is a unique site in France. Located in the city of Lyon, teams of biologists, chemists and biochemists use cutting-edge technologies to identify new ways of protecting crops against fungal diseases.
**When you're recruited by Bayer, it's because we have a real mission for you **
**Context and objectives**:
This internship will take place within the Computational Life Sciences (CLS) team in the Disease Control Indication of Bayer Crop Science Lyon, in close collaboration with the Biochemistry excellence of Disease Control.
The main goal of this internship is to contribute to the in-silico identification of essential genes for phytopathogenic fungi, which is of paramount importance for the development of the new generation of fungicide compounds. In this context, several approaches have recently gained traction, based on feature analysis of biological networks. Among such networks, the so-called Gene Regulatory Networks (GRNs) are often the most meaningful and commonly employed choice.
The ability of a computational model to predict the function of any given gene is thus directly dependent on the accuracy with which the underlying GRN is known. It is therefore of the utmost importance to develop reliable computational methods to infer a full GRN starting from experimental data.
For this project, complex high-density gene-expression data are available for three pathogens of interest, measuring the time variation of expression level after inducing cellular stress. In the first phase of the internship, these data will be used to test several modeling approaches to reconstruct GRNs, i.e., to infer the causal links between variations in expression levels of different genes. Guided by ground-truth knowledge and in interaction with wet-lab scientists, the most accurate modeling approach(es) will be selected.
In a second phase, using GRNs with the highest-possible quality, topological analyses on their structures will be performed to identify classes of genes and the most important features defining such classes. These results will complement other feature-extraction approaches to help establish an in-silico workbench for the characterization of gene essentiality.
**Profile**:
- Good understanding of machine-learning methods (regression, random forests, neural networks).
- Ability to write codes in at least one programming language (Python, R).
- Knowledge of system biology concepts and/or graph theory is a plus.
- Ability to discuss work and present results in English.
- Willingness to manipulate complex biological data.
**Complementary information**:
Location: Lyon - La Dargoire Research Center Position
Type: Internship
Duration: 6 months
Available: January 2024
**"For Bayer, equal opportunity is a major focus of the company's corporate social responsibility”**
**Lieu**:
France : Rhône : Lyon
**Division**:
Crop Science
**Code de référence**:
804897
