October 22, 2021, zoom

Some properties and emerging behaviours of a dynamical network, composed of several dynamical subsystems that interact according to an interconnection topology, are exclusively due to its structure (i.e., its topology along with qualitative assumptions) and are independent of parameter values, which are often uncertain, unknown or time-varying. Structural analysis is aimed at assessing properties that hold for a whole family of systems, characterised by a given structure, regardless of parameter values and precise functional expressions. We propose the BDC-decomposition as a tool for both a local and a global representation of a nonlinear dynamical network with an underlying structure. We show how the BDC-decomposition can help us structurally assess important properties, including stability, stabilisability and the sign of steady-state input-output influences in complex interconnected uncertain systems, discussing examples from biochemical reaction networks, as well as biological and ecological systems.

Giulia Giordano is currently an Assistant Professor at the University of Trento, Italy. She received the B.Sc. and M.Sc. degrees in electrical engineering and the Ph.D. degree in systems and control theory from the University of Udine, Italy, in 2010, 2012, and 2016, respectively. She visited the California Institute of Technology, Pasadena (CA), USA, in 2012, and the University of Stuttgart, Germany, in 2015. She was a Research Fellow at Lund University, Sweden, from 2016 to 2017, and an Assistant Professor at the Delft University of Technology, The Netherlands, from 2017 to 2019. She was recognised with the Outstanding Reviewer Letter from the IEEE Transactions on Automatic Control in 2016 and from the Annals of Internal Medicine in 2020. She received the EECI Ph.D. Award 2016, the NAHS Best Paper Prize 2017, and the SIAM Activity Group on Control and Systems Theory Prize 2021. Her main research interests include the analysis and the control of dynamical networks, with applications especially to biology and epidemiology.