One of the challenges of digital biology is to develop, compare and improve learning methods, adapting them to integrate multi-source data (omics, sensors, environment, data from participatory approaches). The modelling of biological and physiological processes to develop predictive approaches and the simulation of complex biological systems are also major challenges. In particular, the robustness of the models built in axis 1 must be tested by subjecting them to fluctuating conditions (internal or external).

This research includes predictive approaches based on modelling, data assimilation and the coupling of "data-driven" and "concept-driven" approaches, taking into account the essential dimensions of prediction, i.e. planning, quantification of uncertainties and evaluation of forecasts.

Research themes

• Genomic prediction;
• Take into account all transmissible factors - whether genetic or not - in predicting the potential of an individual to transmit its characteristics to its descendants;
• The application of these methods to identify bio-markers of the state or potential of organisms (level of resistance, level of stress, etc.), or genetic targets for the optimisation of metabolic networks;
• Predict the impacts of complex chemical and biological exposure scenarios on physiology (from the cell to the individual level);
• The mobilisation of information regarding the state (phenotype, genome, environment) and knowledge of processes at organism (and sub-organism) levels to predict the functioning and trajectories of populations at different spatial and/or temporal scales.

Methodological challenges

• Select the learning methods best suited to a given problem, determine their contributions and limitations for detecting complex structures or weak signals in the data;
• Build and simulate integrative models with good predictive capabilities, allowing to distinguish the effects of confounding factors in the data;
• Access unobservable quantities through model inversion (e.g. trait value estimation);
• Improve the quality and robustness of predictions (with the underlying issues of planning and sampling, uncertainty assessment, taking account of changing environments).