1.2 The resilience of complex networks: identifying critical indicators for efficient targeted interventions
Collaborators outside U. Laval
The ability of a system to adjust its activity to retain its basic functionality under errors, failures and environmental changes, its resilience, is a defining property of many complex systems. However, and despite widespread consequences on human health, economy and the environment, events leading to loss of resilience, from cascading failures in technological systems to mass extinctions in ecological networks, are still rarely predictable and more than often irreversible. The North, with its diverse interconnected networks, is confronted with mounting challenges from rapid climatic, social and economic changes. We would do well to establish a comprehensive framework to deal with this vulnerable ecosystem. The network science (NS) approach offers such a theoretical and practical framework to address complex systems over microscopic (e.g. neural networks), mesoscopic (e.g. animal biodiversity), and macroscopic (e.g. human population/health and climate changes) scales. At the very least, it offers a common universal language and unifying concepts to apprehend the dynamical, nonlinear, adaptive and hierarchical (complex) systems that we will face in the North. The relationship structure-function will be a leitmotiv of our study. To confront our general methodology with experimental reality, we will combine NS with Systems Biology at the microscopic level to focus our attention on the larval Zebrafish. It is an ideal animal model (the fruit fly of neuroscience) for its small size, transparency, rapid development, and most importantly, its amenability to optogenetics. This will allow the neurophotonics members of our team to image the activity of Zebrafish brain circuits when progressively or suddenly submitted to external (temperature and light) and internal (optical stimulation of neuronal populations) perturbations. By combining recent methods from network analysis and simulations of dynamical systems, we will then compare the theoretical/numerical results with the Zebrafish experiments, as well as with other observable networks of the North.