PHD2020-05
Exploring the role of sRNA regulation through the modelling of the Fe-S biogenesis interaction network
Host laboratory and collaborators
Pierre Mandin / LCB / pmandin@imm.cnrs.fr
Elisabeth Remy / I2M / elisabeth.remy@univ-amu.fr
Abstract
Bacterial small non-coding RNAs (sRNAs) have tremendously emerged as major players in adaptive responses in the last two decades. Despite our now broader understanding of their molecular action, there is still a debate on the advantage of sRNA regulation over classical protein regulators. A major hypothesis is that sRNAs allow a more dynamic control of gene expression. We have newly found that three sRNAs dynamically control a life essential process, namely Fe-S biogenesis, in the model bacterium Escherichia coli. We thus propose to decipher how this unanticipated regulatory network has a profound impact on the cell dynamic adaptation to stress, including antibiotics resistance. To achieve this goal, we propose an interdisciplinary project that involves not only innovative experimental set-ups to quantify dynamics of biological systems, but also intimately couples it to a mathematical modelling and analysis to decipher the underlying mechanism of functioning.
Keywords
Regulatory networks, mathematical modelling, bacterial genetics, molecular biology, stress response
Objectives
This project is centered around two complementary objectives.
1) Determine how sRNA regulation provide a dynamical response to stress. Preliminary results indicate that sRNA regulation provides faster genetic responses. A first goal will be to decipher the molecular mechanisms at work behind these regulations in vivo.
2) Understand specific properties of mixed RNA/TF and the dynamical differences between RNA and TF regulation through a mathematical modelling approach.
Proposed approach (experimental / theoretical / computational)
The study of dynamic gene expression changes requires visualisation of gene expression in real time and at the single cell level. We will take advantage of a new microfluidic microscopic apparatus developed in the lab. The PhD student will determine how sRNAs dynamically control gene expression in vivo using a combination of mutants in presence of stress. Mathematical analyses will be key to analyze and interpret the experimental data produced through the approaches described above. We propose to build a comprehensive model of the genetic regulatory network to tackle the complexity of sRNA control. The analysis of this model will provide a capital understanding of the regulatory mechanisms controlling the dynamical behavior of the system and its adaptation to environment. The PhD student will propose well-adapted mathematical formalism to capture the different scales of dynamics of the regulation, and to specify generic model with single cell data.
Interdisciplinarity
This PhD project is intrinsically an interdisciplinary project that involves innovative experimental set-ups to quantify dynamics of biological systems, requires the development of specific mathematical tools to integrate fine aspects of the regulation, and intimately couples and integrates both aspects. Experimentally, we will make use of innovative techniques (microfluidics analysis, RNA seq…) to be able to follow the dynamics of regulation taking place with an unprecedented definition. Mathematical developments will be needed to capture and analyze the effect of the different nature of regulation, and then the study the RNA/TF regulation network will help to untangle and analyze the complex pathways at work and their impact on the dynamical behavior of the system.
Expected profile
The expected candidate should have an interdisciplinary profile and interest. For the biological part, the candidate should be familiar with basic biological notions, in particular on molecular biology, and should be highly motivated and display high scientific curiosity. Previous lab experience is a plus but it is not mandatory. Mathematical prerequisites are basic knowledge on Mathematical equations and logic reasoning, and an appetence in mathematical modelling.