Applications of network-based approaches in drug research

Recent research trends in computer-aided drug design have shown an increasing interest towards the implementation of advanced approaches able to deal with large amount of data. This demand arose from the awareness of the complexity of biological systems and from the availability of data provided by high-throughput technologies. As a consequence, drug research has embraced this paradigm shift exploiting approaches such as that based on networks. Indeed, the process of drug discovery can benefit from the implementation of network-based methods at different steps from target identification to drug repurposing. From this broad range of opportunities, this thesis is focused on three main topics: (i) chemical space networks (CSNs), which are designed to represent and characterize bioactive compound data sets; (ii) drug-target interactions (DTIs) prediction through a network-based algorithm that predicts missing links; (iii) COVID-19 drug research which was explored implementing COVIDrugNet, a network-based tool for COVID-19 related drugs. The main highlight emerged from this thesis is that network-based approaches can be considered useful methodologies to tackle different issues in drug research. In detail, CSNs are valuable coordinate-free, graphically accessible representations of structure-activity relationships of bioactive compounds data sets especially for medium-large libraries of molecules. DTIs prediction through the random walk with restart algorithm on heterogeneous networks can be a helpful method for target identification. COVIDrugNet is an example of the usefulness of network-based approaches for studying drugs related to a specific condition, i.e., COVID-19, and the same ‘systems-based’ approaches can be used for other diseases. To conclude, network-based tools are proving to be suitable in many applications in drug research and provide the opportunity to model and analyze diverse drug-related data sets, even large ones, also integrating different multi-domain information.

Characterization of DNA sequence properties through network and statistical approaches

In this thesis we will see that the DNA sequence is constantly shaped by the interactions with its environment at multiple levels, showing footprints of DNA methylation, of its 3D organization and, in the case of bacteria, of the interaction with the host organisms. In the first chapter, we will see that analyzing the distribution of distances between consecutive dinucleotides of the same type along the sequence, we can detect epigenetic and structural footprints. In particular, we will see that CG distance distribution allows to distinguish among organisms of different biological complexity, depending on how much CG sites are involved in DNA methylation. Moreover, we will see that CG and TA can be described by the same fitting function, suggesting a relationship between the two. We will also provide an interpretation of the observed trend, simulating a positioning process guided by the presence and absence of memory. In the end, we will focus on TA distance distribution, characterizing deviations from the trend predicted by the best fitting function, and identifying specific patterns that might be related to peculiar mechanical properties of the DNA and also to epigenetic and structural processes. In the second chapter, we will see how we can map the 3D structure of the DNA onto its sequence. In particular, we devised a network-based algorithm that produces a genome assembly starting from its 3D configuration, using as inputs Hi-C contact maps. Specifically, we will see how we can identify the different chromosomes and reconstruct their sequences by exploiting the spectral properties of the Laplacian operator of a network. In the third chapter, we will see a novel method for source clustering and source attribution, based on a network approach, that allows to identify host-bacteria interaction starting from the detection of Single-Nucleotide Polymorphisms along the sequence of bacterial genomes.

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.

La cooperazione giudiziaria in rete nello spazio di liberta', sicurezza e giustizia

L’oggetto del lavoro si concentra sull’analisi in chiave giuridica del modello di cooperazione in rete tra le autorità nazionali degli Stati membri nel quadro dello Spazio LSG, allo scopo di valutarne il contributo, le prospettive e il potenziale. La trattazione si suddivide in due parti, precedute da una breve premessa teorica incentrata sull’analisi della nozione di rete e la sua valenza giuridica. La prima parte ricostruisce il percorso di maturazione della cooperazione in rete, dando risalto tanto ai fattori di ordine congiunturale quanto ai fattori giuridici e d’ordine strutturale che sono alla base del processo di retificazione dei settori giustizia e sicurezza. In particolare, vengono elaborati taluni rilievi critici, concernenti l’operatività degli strumenti giuridici che attuano il principio di mutuo riconoscimento e di quelli che danno applicazione al principio di disponibilità delle informazioni. Ciò allo scopo di evidenziare gli ostacoli che, di frequente, impediscono il buon esito delle procedure di cooperazione e di comprendere le potenzialità e le criticità derivanti dall’utilizzo della rete rispetto alla concreta applicazione di tali procedure. La seconda parte si focalizza sull’analisi delle principali reti attive in materia di giustizia e sicurezza, con particolare attenzione ai rispettivi meccanismi di funzionamento. La trattazione si suddivide in due distinte sezioni che si concentrano sulle a) reti che operano a supporto dell’applicazione delle procedure di assistenza giudiziaria e degli strumenti di mutuo riconoscimento e sulle b) reti che operano nel settore della cooperazione informativa e agevolano lo scambio di informazioni operative e tecniche nelle azioni di prevenzione e lotta alla criminalità - specialmente nel settore della protezione dell’economia lecita. La trattazione si conclude con la ricostruzione delle caratteristiche di un modello di rete europea e del ruolo che questo esercita rispetto all’esercizio delle competenze dell’Unione Europea in materia di giustizia e sicurezza.