Network analysis and machine learning assist drug repurposing and safety assessment in neurological diseases

In recent decades, two prominent trends have influenced the data modeling field, namely network analysis and machine learning. This thesis explores the practical applications of these techniques within the domain of drug research, unveiling their multifaceted potential for advancing our comprehension of complex biological systems. The research undertaken during this PhD program is situated at the intersection of network theory, computational methods, and drug research. Across six projects presented herein, there is a gradual increase in model complexity. These projects traverse a diverse range of topics, with a specific emphasis on drug repurposing and safety in the context of neurological diseases. The aim of these projects is to leverage existing biomedical knowledge to develop innovative approaches that bolster drug research. The investigations have produced practical solutions, not only providing insights into the intricacies of biological systems, but also allowing the creation of valuable tools for their analysis. In short, the achievements are: • A novel computational algorithm to identify adverse events specific to fixed-dose drug combinations. • A web application that tracks the clinical drug research response to SARS-CoV-2. • A Python package for differential gene expression analysis and the identification of key regulatory "switch genes". • The identification of pivotal events causing drug-induced impulse control disorders linked to specific medications. • An automated pipeline for discovering potential drug repurposing opportunities. • The creation of a comprehensive knowledge graph and development of a graph machine learning model for predictions. Collectively, these projects illustrate diverse applications of data science and network-based methodologies, highlighting the profound impact they can have in supporting drug research activities.

The port-Hamiltonian framework as a comprehensive approach to model and simulate complex systems

This thesis describes modelling tools and methods suited for complex systems (systems that typically are represented by a plurality of models). The basic idea is that all models representing the system should be linked by well-defined model operations in order to build a structured repository of information, a hierarchy of models. The port-Hamiltonian framework is a good candidate to solve this kind of problems as it supports the most important model operations natively. The thesis in particular addresses the problem of integrating distributed parameter systems in a model hierarchy, and shows two possible mechanisms to do that: a finite-element discretization in port-Hamiltonian form, and a structure-preserving model order reduction for discretized models obtainable from commercial finite-element packages.

Evaluation of Human Exposure to Magnetic Fields Generated by Electric Power Systems in Complex Configurations

The international growing concern for the human exposure to magnetic fields generated by electric power lines has unavoidably led to imposing legal limits. Respecting these limits, implies being able to calculate easily and accurately the generated magnetic field also in complex configurations. Twisting of phase conductors is such a case. The consolidated exact and approximated theory regarding a single-circuit twisted three-phase power cable line has been reported along with the proposal of an innovative simplified formula obtained by means of an heuristic procedure. This formula, although being dramatically simpler, is proven to be a good approximation of the analytical formula and at the same time much more accurate than the approximated formula found in literature. The double-circuit twisted three-phase power cable line case has been studied following different approaches of increasing complexity and accuracy. In this framework, the effectiveness of the above-mentioned innovative formula is also examined. The experimental verification of the correctness of the twisted double-circuit theoretical analysis has permitted its extension to multiple-circuit twisted three-phase power cable lines. In addition, appropriate 2D and, in particularly, 3D numerical codes for simulating real existing overhead power lines for the calculation of the magnetic field in their vicinity have been created. Finally, an innovative ‘smart’ measurement and evaluation system of the magnetic field is being proposed, described and validated, which deals with the experimentally-based evaluation of the total magnetic field B generated by multiple sources in complex three-dimensional arrangements, carried out on the basis of the measurement of the three Cartesian field components and their correlation with the field currents via multilinear regression techniques. The ultimate goal is verifying that magnetic induction intensity is within the prescribed limits.

Novel investigation methods in Computational Social Dynamics and Complex Systems

In this thesis the evolution of the techno-social systems analysis methods will be reported, through the explanation of the various research experience directly faced. The first case presented is a research based on data mining of a dataset of words association named Human Brain Cloud: validation will be faced and, also through a non-trivial modeling, a better understanding of language properties will be presented. Then, a real complex system experiment will be introduced: the WideNoise experiment in the context of the EveryAware european project. The project and the experiment course will be illustrated and data analysis will be displayed. Then the Experimental Tribe platform for social computation will be introduced . It has been conceived to help researchers in the implementation of web experiments, and aims also to catalyze the cumulative growth of experimental methodologies and the standardization of tools cited above. In the last part, three other research experience which already took place on the Experimental Tribe platform will be discussed in detail, from the design of the experiment to the analysis of the results and, eventually, to the modeling of the systems involved. The experiments are: CityRace, about the measurement of human traffic-facing strategies; laPENSOcosì, aiming to unveil the political opinion structure; AirProbe, implemented again in the EveryAware project framework, which consisted in monitoring air quality opinion shift of a community informed about local air pollution. At the end, the evolution of the technosocial systems investigation methods shall emerge together with the opportunities and the threats offered by this new scientific path.

Dynamic food systems and climate-induced risks transmission for food and nutritional security

Agriculture market instability impedes achieving the global goal of sustainable and resilient food systems. Currently, the support to producers reaches the mammoth USD 540 billion a year and is projected to reach USD 1.8 trillion by 2030. This gigantic increase requires a repurposing agricultural support strategy (RASS), considering the market country-specific circumstances. These circumstances may vary with geographic locations, marketing structures, and product value chains. The fruit production system is crucial for health-conscious consumers and profit-oriented producers for food and nutritional security. Export is one of the main driving forces behind the expansion of the fruit sector, and during the year 2010-2018, trade significantly outpaced production increases. The previous literature states that irregular and unpredictable behaviour — Chaos — can arise from entirely rational economic decision-making within markets. Different markets' direct/indirect linkages through trade create trade hubs, and uncertainty may function as an avenue to transmit adverse shocks and increase vulnerability rather than contribute to resilience. Therefore, distinguishing Chaos into an endogenous and exogenous pattern of behaviour is cradled to formulate an effective RASS for resilient food systems and to understand global food crises. The present research is aimed at studying the market dynamics of three regional trade hubs, i.e., Brazil (South America), Italy (Europe), and Pakistan (Asia), each representing advanced to traditional value chains to control uncertainty (risks). The present research encompasses 1) a systematic review to highlight the research dynamism and identify grey-areas of research. Based on the findings, we have investigated the 2) nonlinear impacts of climate-induced price responsiveness in monopsony markets. Once we highlighted the importance of marketing structures/arrangements, 3) we developed a risk transmission framework to address the co-evolving impacts in complex dynamic interactions.