Section: Scientific Foundations
Analysis of interconnected systems

Algebraic analysis of linear systems
Study of the structural properties of linear differential timedelay systems and linear infinitedimensional systems (e.g. invariants, controllability, observability, flatness, reductions, decomposition, decoupling, equivalences) by means of constructive algebra, module theory, homological algebra, algebraic analysis and symbolic computation [8] , [9] , [104] , [125] , [105] , [108] .

Robust stability of linear systems
Within an interconnection context, lots of phenomena are modelled directly or after an approximation by delay systems. These systems might have fixed delays, timevarying delays, distributed delays...
For various infinitedimensional systems, particularly delay and fractional systems, inputoutput and timedomain methods are jointly developed in the team to characterize stability. This research is developed at four levels: analytic approaches (${H}_{\infty}$stability, BIBOstablity, robust stability, robustness metrics) [1] , [2] , [5] , [6] , symbolic computation approaches (SOS methods are used for determining easytocheck conditions which guarantee that the poles of a given linear system are not in the closed right halfplane, certified CAD techniques), numerical approaches (rootloci, continuation methods) and by means of softwares developed in the team [5] , [6] .

Robustness/fragility of biological systems
Deterministic biological models describing, for instance, species interactions, are frequently composed of equations with important disturbances and poorly known parameters. To evaluate the impact of the uncertainties, we use the techniques of designing of global strict Lyapunov functions or functional developed in the team.
However, for other biological systems, the notion of robustness may be different and this question is still in its infancy (see, e.g. [116] ). Unlike engineering problems where a major issue is to maintain stability in the presence of disturbances, a main issue here is to maintain the system response in the presence of disturbances. For instance, a biological network is required to keep its functioning in case of a failure of one of the nodes in the network. The team, which has a strong expertise in robustness for engineering problems, aims at contributing at the develpment of new robustness metrics in this biological context.