My main research interests lie at the interface between Dynamical Systems and Control Theory and the Life Sciences. I am passionate about understanding and controlling complex systems in biology, ecology, epidemiology through the lenses of systems and control theory.
You can also read my interview with the IEEE Control Systems Magazine in the column People in Control.
Unveiling the secret mechanisms of nature. To contrast epidemics, fight antibiotic resistance, and devise new therapies and biotechnologies, we must understand complex behaviours that interconnected natural systems are able to preserve with astounding robustness, despite huge uncertainties and variations in their many parameters. We aim to develop a framework to analyse and control complex, uncertain dynamical systems in biology and epidemiology, so as to unravel the root causes of their behaviour and identify the best targets for interventions aimed at inducing a desired outcome. A novel integration of methodologies will offer deeper insight into natural mechanisms and help us identify therapeutic targets for healing diseases, design biomolecular feedback systems with guaranteed properties, and predict and control epidemics. Systems in nature are extremely robust, despite huge uncertainties and variability. Studying their nonlinear dynamic behaviour is challenging, due to their complexity and the many parameters at play, but crucial to understand important phenomena, such as cellular dynamics, onset of diseases, epidemic spreading. Parameter-dependent simulations can predict the behaviour of natural systems case by case. Yet, the exact models and parameter values are poorly known, while qualitative behaviours are often preserved even with huge parameter variations, because they rely on the system interconnection structure. Parameter-free structural approaches can check whether a property is preserved for a whole family of uncertain systems exclusively due to its structure. However, when an expected property fails to hold structurally, novel approaches are needed to understand why, which system features prevent it, which key parameters must be finely tuned to enforce it. We wish to develop a unifying framework to analyse and control families of uncertain dynamical systems in biology and epidemiology, which integrates for the first time structural, robust and probabilistic methods, tailored to the peculiarities of natural systems. Our goal is to provide: i) methodologies to assess (practically) structural properties and unveil the mechanisms that enable/prevent a property, identifying the key parameters or motifs; ii) control paradigms that leverage such an insight to guarantee a desired global property through targeted local interventions; iii) scaling and aggregation approaches that exploit the properties of subsystems to mitigate computational complexity. Mathematical theory as well as algorithms to analyse and control complex uncertain systems in nature will strongly support the analysis and design of biomolecular feedback systems with a desired behaviour, the identification of therapeutic targets, the prediction and control of epidemic phenomena.
Complex interconnections of interacting agents, each with its own dynamics, are ubiquitous in our daily life. We are part of social and economic networks, we use technological networks (power, transportation, telecommunication, computer networks), our organisms rely on extremely complex interactions of DNA, proteins and biomolecules (biochemical reaction networks, gene regulatory and metabolic networks, signalling pathways). Multi-agent robotics is also arising, aimed at employing swarms of robots, drones or unmanned aerial vehicles to perform critical collaborative tasks (cooperative manipulation and transportation, including human-robot interaction; patrolling; search and rescue).
Each of these systems can be modelled as a dynamical network: a complex dynamical system endowed with a network structure, composed of several dynamical sub-units that are interconnected according to a (possibly time-varying) network topology. This general class of models embraces natural and engineered complex systems, and is thus relevant in systems biology, social networks, ICT, autonomous systems and multi-agent robotics.
Structural (parameter-free) methods are particularly useful to deal with networked systems whose parameter values (and functional expressions, due to modelling choices) are varying, uncertain or unknown. Can a class of systems necessarily give rise to a particular qualitative behaviour, regardless of specific parameter values? Quite surprisingly, this is indeed the case for many natural systems: this reveals how the design principles selected by evolution have rooted specific qualitative behaviours (associated with specific motifs) in the system structure, allowing living cells, organs and organisms to robustly perform their task in spite of severe uncertainties, noise and environmental fluctuations. The main goal is to explain how dynamical networks in nature can ensure a global qualitative behaviour that exhibits an extraordinary robustness in spite of huge parameter variations and uncertainty. Structural analysis allows us to identify properties that pertain to a whole class of dynamical networks, due to its inherent structure.
When we manage complex large-scale engineered systems, our aim is to control or coordinate the overall system so as to achieve the desired global behaviour, even though we have limited local information and we can enforce local actions only.
As nature often adopts distributed strategies, so distributed approaches are fundamental when dealing with complex engineered networks. Distributed optimisation and estimation algorithms are fundamental in sensor networks, localisation problems, synchronisation and coordination of autonomous agents. The decentralised control of dynamical networks is crucial for applications spanning from traffic congestion problems, supply chains and inventory management to water and energy distribution, formation control and collision avoidance, coordination of robots and autonomous vehicles, power networks and smart grids, telecommunications, computer and mobile phone networks. These distributed strategies need to robustly face delays, saturations, topology changes, failures and unpredictable events. The main goal is to design network-decentralised control and estimation strategies for dynamical networks that enforce a global behaviour through local actions.
