The aim is to describe, understand and model biological systems and to establish causal links within and between biological scales, integrating systemic effects such as stochasticity or retroactions as determinants of system dynamics and evolution. Beyond the study of processes under standardised conditions, it will also be necessary to characterise them in a diversity of environments (biotic, abiotic, contaminant) and contexts.

These challenges call for mathematical and computer modelling of dynamic systems, digital calculation, knowledge extraction and integration of heterogeneous data to calibrate and evaluate the quality of models. The integration of data from different sources requires progress on knowledge representation methods using ontologies, controlled vocabularies and networks, as well as on spatialization methods. Learning methods are also used.

Research themes

• Characterization of the principles of organization, functioning and evolution in genomes, cells, organs and organisms;
• Construction of molecular networks underlying biological functions and, in particular, multi-scale or multi-species metabolic networks;
• Establishing links between gene(s) and form(s) by integrating geometric and physical constraints and associated retroaction;
• Investigation of mechanisms underlying the adaptive responses of organisms and populations and the spatial and temporal scales of evolution of these responses.

Methodological challenges

• Extract and represent knowledge in forms that allow hypothesis formalization and integration into models;
• Search for causal relationships using statistical and automatic learning methods on large-scale data;
• Develop multi-scale modeling, inference rules for systems, and simulation studies of complex dynamic systems describing living organisms;
• Optimize the modeling and coupling of different models;
• Identify "meta-mechanisms" as an alternative to coupling models between successive levels of organization.