Identification of transcription factors input signals throught metabolic and regulatory network analysis

Description:

Transcription factors (TF) control most cellular responses and many of them act as direct sensors of signal metabolites to induce physiological adaptation. However, we frequently lack information on their input signal, how it modulates their activity, and how signals are integrated into regulatory networks. Consequently, it is still challenging to accurately predict cellular responses to changing environments, an important limitation in many fields of biological research. This gap in understanding is due to the lack of effective methods for the global mapping and quantification of metabolite-TF interactions.

This project aims at setting up a computational approach that will allow the prediction of TFs metabolite input signals based on a network analysis approach. Briefly, the student will use several python tools to analyse the metabolic and regulatory networks of E. coli and extract key characteristics of known metabolite-TF interactions. This information will then be used to predict new interactions for TFs with no known input signals. After method development on E. coli networks, this method will be applied to two additional bacterial pathogens to generate hypotheses for key signals driving infections.

This project will involve coding in Python. Close mentoring will be available for the choice of tools to use, how to code and use them. A basic first version of the python pipeline has already been coded and will serve as basis for this project.

Duration: 3 or 6 months
Type: Computational

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