In Silico Modelling, Analysis, and Control of Complex Diseases: Addressing Clinical Questions, Personalized Treatments, and Healthcare Management

Human diseases are complex and dynamic. Understanding and controlling diseases require interdisciplinary approaches, aided by advances in digital technology, data analysis, and computational power. Specifically, in his Ph.D. Thesis, Matteo Italia has developed in silico models to study cancers, Restless Legs Syndrome (RLS), and Covid-19. The goals are to answer clinical questions, optimize treatments, and manage healthcare. For cancers, the developed models suggest that dynamic and personalized protocols can overcome drug resistance more effectively than static protocols. For neuroblastoma, the MYCN gene’s role in treatment outcomes is explored. For melanoma, promising drug combinations are identified to overcome vemurafenib resistance. In RLS, the first mathematical model supports the hypothesis that a single neuronal generator triggers periodic leg movements, aiding disease understanding. For Covid-19, a new compartment model, including vaccination policies and protection waning, emphasizes the importance of global equitable vaccine access to mitigate the pandemic. Overall, this ensemble of works highlights the importance of a systematic computational methodology in healthcare, a sort of engineered modus operandi that combines data analysis, systems and control, mathematics, optimization, simulations, and coding, among others.