Breaking through the limits of gene regulatory networks inference: Resolution, Information, Dynamics & Analysis

Inference of networks in biology is a powerful approach to understand how biological mechanisms function, and to identify regulatory modules or genes that cannot be predicted by classical analysis. To date, knowledge arising from gene regulatory networks is still limited by a range of experimental or computational limitations. Here, we propose to perform a multidisciplinary project aiming at outperforming current approaches of regulatory networks reconstruction. This will be done by (i) increasing the resolution with production of cell type-specific data, (ii) improving the level of input information, especially with the integration of transcriptomic, epigenomic and transcription factors/chromatin interaction data, (iii) adopting temporal and dynamics approaches, and (iv) developing dedicated computational modelling tools. Using the model of plant's response to climate change and nutritional starvation, this project will be performed through an original collaboration between biologists, mathematicians and chemists.

Computational modelling, Dynamic networks, Regulatory circuits, Nutritional constraint,

Partenaires du projet

MARTIN Antoine
BPMP (UMR5004) Montpellier France
LEBRE Sophie
IMAG (UMR5149) France
Martin Antoine - Océane Cassan
Crédit photo : Martin Antoine - Océane Cassan