The ClimNet project’s aims at developing new mathematical methods to supervise (with validated experimental results of Gene-Gene interactions) Machine Learning (ML) algorithms to build Gene Regulatory Networks. These new methods will be applied in a biological context with very broad implications since they will be used to model molecular responses of plants to climate-change- and nutritional-related variables. Indeed, the industrial revolution has released an important amount of CO2 in the atmosphere, which modifies the climate, leading to a combination of drought, and high temperature episodes.
To date, the effect of these new environmental variables (CO2, Temperature, Drought) is poorly known in particular when studied in combination with other environmental variables important for plant growth and development (Nutrition for instance). Since, photosynthetic organisms (such as higher plants) are a major sink for carbon (CO2), knowing i) how they respond to such combination of signals as well as ii) determining key element of gene regulatory networks, would provide fundamental knowledge that might help mitigate the effect of global warming and potentially secure food production.
