Analyzing biological network parameters with CentiScaPe

Summary: The increasing availability of large network datasets along with the progresses in experimental high-throughput technologies have prompted the need for tools allowing easy integration of experimental data with data derived form network computational analysis. In order to enrich experimental data with network topological parameters, we have developed the Cytoscape plug-in CentiScaPe. The plug-in computes several network centrality parameters and allows the user to analyze existing relationships between experimental data provided by the users and node centrality values computed by the plug-in. CentiScaPe allows identifying network nodes that are relevant from both experimental and topological viewpoints. CentiScaPe also provides a Boolean logic-based tool that allows easy characterization of nodes whose topological relevance depends on more than one centrality. Finally, different graphic outputs and the included description of biological significance for each computed centrality facilitate the analysis by the end users not expert in graph theory, thus allowing easy node categorization and experimental prioritization

Computational Analysis of Biological networks

Caratterizzare, descrivere ed estrarre informazioni da un network, \ue8 sicuramente uno dei principali obbiettivi della scienza, dato che lo studio dei network interessa differenti campi della ricerca, come la biologia, l'economia, le scienze sociali, l'informatica e cos\uec via. Ci\uf2 che si vuole \ue8 riuscire ad estrarre le propriet\ue0 fondamentali dei network e comprenderne la funzionalit\ue0. Questa tesi riguarda sia l'analisi topologica che l' analisi dinamica dei network biologici, anche se i risultati possono essere applicati a diversi campi. Per quanto riguarda l'analisi topologica viene utilizzato un approccio orientato ai nodi, utilizzando le centralit\ue0 per individuare i nodi pi\uf9 rilevanti e integrando tali risultati con dati da laboratorio. Viene inoltre descritto CentiScaPe, un software implementato per effettuare tale tipo di analisi. Vengono inoltre introdotti i concetti di "interference" e "robustness" che permettono di comprendere come un network si riarrangia in seguito alla rimozione o all'aggiunta di nodi. Per quanto riguarda l'analisi dinamica, si mostra come l'abstract interpretation pu\uf2 essere utilizzata nella simulazione di pathways per ottenere i risultati di migliaia di simulazioni in breve tempo e come possibile soluzione del problema della stima dei parametri mancanti.This thesis, treating both topological and dynamic points of view, concerns several aspects of biological networks analysis. Regarding the topological analysis of biological networks, the main contribution is the node-oriented point of view of the analysis. It means that instead of concentrating on global properties of the networks, we analyze them in order to extract properties of single nodes. An excellent method to face this problem is to use node centralities. Node centralities allow to identify nodes in a network having a relevant role in the network structure. This can not be enough if we are dealing with a biological network, since the role of a protein depends also on its biological activity that can be detected with lab experiments. Our approach is to integrate centralities analysis and data from biological experiments. A protocol of analysis have been produced, and the CentiScaPe tool for computing network centralities and integrating topological analysis with biological data have been designed and implemented. CentiScaPe have been applied to a human kino-phosphatome network and according to our protocol, kinases and phosphatases with highest centralities values have been extracted creating a new subnetwork of most central kinases and phosphatases. A lab experiment established which of this proteins presented high activation level and through CentiScaPe the proteins with both high centrality values and high activation level have been easily identified. The notion of node centralities interference have also been introduced to deal with central role of nodes in a biological network. It allow to identify which are the nodes that are more affected by the remotion of a particular node measuring the variation on their centralities values when such a node is removed from the network. The application of node centralities interference to the human kino-phosphatome revealed that different proteins affect centralities values of different nodes. Similarly to node centralities interference, the notion of centrality robustness of a node is introduced. This notion reveals if the central role of a node depends on other particular nodes in the network or if the node is ``robust'' in the sense that even if we remove or add other nodes the central role of the node remains almost unchanged. The dynamic aspects of biological networks analysis have been treated from an abstract interpretation point of view. Abstract interpretation is a powerful framework for the analysis of software and is excellent in deriving numerical properties of programs. Dealing with pathways, abstract interpretation have been adapted to the analysis of pathways simulation. Intervals domain and constants domain have been succesfully used to automatically extract information about reactants concentration. The intervals domain allow to determine the range of concentration of the proteins, and the constants domain have been used to know if a protein concentration become constant after a certain time. The other domain of analysis used is the congruences domain that, if applied to pathways simulation can easily identify regular oscillating behaviour in reactants concentration. The use of abstract interpretation allows to execute thousands of simulation and to completely and automatically characterize the behaviour of the pathways. In such a way it can be used also to solve the problem of parameters estimation where missing parameters can be detected with a brute force algorithm combined with the abstract interpretation analysis. The abstract interpretation approach have been succesfully applied to the mitotic oscillator pathway, characterizing the behaviour of the pathway depending on some reactants. To help the analysis of relation between reactants in the network, the notions of variables interference and variables abstract interference have been introduced and adapted to biological pathways simulation. They allow to find relations between properties of different reactants of the pathway. Using the abstract interference techniques we can say, for instance, which range of concentration of a protein can induce an oscillating behaviour of the pathway

Type II migration strikes back – an old paradigm for planet migration in discs

In this paper, we analyse giant gap-opening planet migration in proto-planetary discs, focusing on the type II migration regime. According to standard type II theory, planets migrate at the same rate as the gas in the disc, as they are coupled to the disc viscous evolution; however, recent studies questioned this paradigm, suggesting that planets migrate faster than the disc material. We study the problem through 2D long-time simulations of systems consistent with type II regime, using the hydrodynamical grid code FARGO3D. Even though our simulations confirm the presence of an initial phase characterized by fast migration, they also reveal that the migration velocity slows down and eventually reaches the theoretical prediction if we allow the system to evolve for enough time. We find the same tendency to evolve towards the theoretical predictions at later times when we analyse the mass flow through the gap and the torques acting on the planet. This transient is related to the initial conditions of our (and previous) simulations, and is due to the fact that the shape of the gap has to adjust to a new profile, once the planet is set into motion. Secondly, we test whether the type II theory expectation that giant planet migration is driven by viscosity is consistent with our simulation by comparing simulations with the same viscosity and different disc masses (or vice versa). We find a good agreement with the theory, since when the discs are characterized by the same viscosity, the migration properties are the same

Finding the shortest path with PesCa: A tool for network reconstruction

The growing dimension and complexity of the available experimental data generating biological networks have increased the need for tools that help in categorizing nodes by their topological relevance. Here we present CentiScaPe, a Cytoscape app specifically designed to calculate centrality indexes used for the identification of the most important nodes in a network. CentiScaPe is a comprehensive suite of algorithms dedicated to network nodes centrality analysis, computing several centralities for undirected, directed and weighted networks. The results of the topological analysis can be integrated with data set from lab experiments, like expression or phosphorylation levels for each protein represented in the network. Our app opens new perspectives in the analysis of biological networks, since the integration of topological analysis with lab experimental data enhance the predictive power of the bioinformatics analysis

Artificial intelligence-based tools to control healthcare associated infections: A systematic review of the literature

Background: Healthcare-associated infections (HAIs) are the most frequent adverse events in healthcare and a global public health concern. Surveillance is the foundation for effective HAIs prevention and control. Manual surveillance is labor intensive, costly and lacks standardization. Artificial Intelligence (AI) and machine learning (ML) might support the development of HAI surveillance algorithms aimed at understanding HAIs risk factors, improve patient risk stratification, identification of transmission pathways, timely or real-time detection. Scant evidence is available on AI and ML implementation in the field of HAIs and no clear patterns emerges on its impact. Methods: We conducted a systematic review following the PRISMA guidelines to systematically retrieve, quantitatively pool and critically appraise the available evidence on the development, implementation, performance and impact of ML-based HAIs detection models. Results: Of 3445 identified citations, 27 studies were included in the review, the majority published in the US (n = 15, 55.6%) and on surgical site infections (SSI, n = 8, 29.6%). Only 1 randomized controlled trial was included. Within included studies, 17 (63%) ML approaches were classified as predictive and 10 (37%) as retrospective. Most of the studies compared ML algorithms' performance with non-ML logistic regression statistical algorithms, 18.5% compared different ML models' performance, 11.1% assessed ML algorithms' performance in comparison with clinical diagnosis scores, 11.1% with standard or automated surveillance models. Overall, there is moderate evidence that ML-based models perform equal or better as compared to non-ML approaches and that they reach relatively high-performance standards. However, heterogeneity amongst the studies is very high and did not dissipate significantly in subgroup analyses, by type of infection or type of outcome. Discussion: Available evidence mainly focuses on the development and testing of HAIs detection and prediction models, while their adoption and impact for research, healthcare quality improvement, or national surveillance purposes is still far from being explored