Description du sujet de thèse

In all living cells, genetic information governs the production of proteins—key elements for proper cellular function—via messenger RNA (mRNA). A cell's initial response to a changing environment relies on the regulation of mRNA abundance, either through its production or its degradation.

In plants, the disruption of key components involved in RNA degradation severely affects their development and resilience to environmental stresses. However, current tools for assessing, comparing, and quantifying RNA degradation remain limited to specific tissues and conditions, hindering our understanding of the underlying mechanisms across diverse biological contexts.

The first objective of this thesis will be to develop a statistical model describing the effect of different degradation mechanisms on the size of RNA fragments, which can be measured through direct RNA sequencing, and to propose associated inference algorithms.

In the second phase, the mechanisms of RNA deadenylation—occurring upstream of RNA degradation—will also be modeled using stochastic approaches, with the aim of understanding the key interactions between various players involved in the regulation of the RNA life cycle.
Using the model plant Arabidopsis thaliana, we will explore compensatory mechanisms that are activated when essential components of the main degradation pathway are impaired, particularly in response to environmental stresses.

Contexte de travail

The thesis is part of the MITI 80Prime Indegra Plantes funding program, in collaboration with plant biologists in Strasbourg and an RNA biologist in Australia. The candidate will begin by extending the already developed INDEGRA software to include additional degradation mechanisms and will propose model selection approaches to identify the most likely mechanisms. Bayesian models may be considered, allowing, for instance, the separation of technical degradation effects from biological degradation effects. Similarly, for de-adenylation mechanisms, the candidate will build on previously proposed deterministic models to develop a comprehensive stochastic model and investigate the interactions among the various regulatory components.

Contraintes et risques

No risk declared

Informations complémentaires

The candidate should hold a Master's degree in statistics or biostatistics. A strong interest in biology and a motivation for algorithm development are expected.