PhD Position F/M Computational analysis of mRNA degradation

The Centre Inria de l’Université de Grenoble groups together almost 600 people in 24 research teams and 9 research support departments.

Staff is present on three campuses in Grenoble, in close collaboration with other research and higher education institutions (Université Grenoble Alpes, CNRS, CEA, INRAE, …), but also with key economic players in the area.

The Centre Inria de l’Université Grenoble Alpes is active in the fields of high-performance computing, verification and embedded systems, modeling of the environment at multiple levels, and data science and artificial intelligence. The center is a top-level scientific institute with an extensive network of international collaborations in Europe and the rest of the world.

The Ph.D. project will be carried out in the project-team MICROCOSME at Inria – Univ Grenoble Alpes under the supervision of Delphine Ropers within the framework of the RECOM ANR project. MICROCOSME is an interdisciplinary team that includes applied mathematicians, engineers, computer scientists, computational biologists as well as experimentalists from the microbiology/biophysics team BIOP of the Université Grenoble Alpes.

Mechanistic models are essential to unravel the molecular mechanisms driving cellular responses. However, the integration of high-throughput data with mechanistic knowledge is limited by the availability of scalable computational approaches able to disentangle biological and technical sources of variation.

The objective of the PhD thesis is to combine mechanistic modelling and statistical inference approaches to analyse dynamical transcriptomics data in the context of mRNA degradation in the model bacterium Escherichia coli. The candidate will be able to build on the team's previous work showing how the combination of mechanistic and statistical modelling approaches can be used to infer kinetic parameters from dynamic transcriptomic data and identify novel regulatory mechanisms such as competitive effects in mRNA decay [1,2]. The thesis is part of the ANR project RECOM, which aims to assess the role and importance of this regulatory mechanism through mathematical modelling and experiments. In close collaboration with our biologist partner from the Toulouse Biotechnology Institute, the aim of the PhD thesis is to a) adapt or extend the mechanistic modelling and statistical framework to analyse new types of data and experiments performed within and outside the project [3,4] and, in close collaboration with the biologists, b) interpret the results biologically and c) propose experiments to validate the model predictions.

[1] T.A. Etienne, M. Cocaign-Bousquet & D. Ropers (2020). Competitive effects in bacterial mRNA decay. Journal of Theoretical Biology, 504, 110333.

[2] T.A. Etienne, C. Roux, E. Cinquemani, L. Girbal, M. Cocaign-Bousquet & D. Ropers (2022). A nonlinear mixed-effects approach for the mechanistic interpretation of time-series transcriptomics data. Preprint. https://inria.hal.science/hal-03652397/.

[3] T. Esquerre, S. Laguerre, C. Turlan, A.J. Carpousis, L. Girbal & M. Cocaign-Bousquet (2014). Dual role of transcription and transcript stability in the regulation of gene expression in Escherichia coli cells cultured on glucose at different growth rates. Nucleic Acids Research, 42(4), 2460-2472.

[4] L. Hamouche, L. Poljak & A.J. Carpousis (2021). Polyribosome-dependent clustering of membrane-anchored RNA degradosomes to form sites of mRNA degradation in Escherichia coli. Mbio, 12(5), 10-1128.

The PhD candidate will:

- Develop estimation strategies (simplification and/or reduction of mechanistic models of mRNA degradation, development of nonlinear mixed effects - NLME - models) to estimate degradation parameters from RNA-Seq datasets.

- Assess the utility of the NLME framework for medium- to low-throughput data sets (microfluidic RT-qPCR) and possibly propose alternative statistical inference approaches.

- Apply the different estimation strategies to various datasets obtained in the bacterium E. coli subjected to various perturbations, assess the quality of model estimation, and interpret the estimation results.

Interested candidates are expected to have a good background in statistical inference and mathematical modelling, and to be strongly interested in biological applications. Good relational skills and English skills are also important for the project

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage

2200 euros gross salary /month