A network inference method for large-scale unsupervised identification of novel drug-drug interactions

Characterizing interactions between drugs is important to avoid potentially harmful combinations, to reduce off-target effects of treatments and to fight antibiotic resistant pathogens, among others. Here we present a network inference algorithm to predict uncharacterized drug-drug interactions. Our algorithm takes, as its only input, sets of previously reported interactions, and does not require any pharmacological or biochemical information about the drugs, their targets or their mechanisms of action. Because the models we use are abstract, our approach can deal with adverse interactions, synergistic/antagonistic/suppressing interactions, or any other type of drug interaction. We show that our method is able to accurately predict interactions, both in exhaustive pairwise interaction data between small sets of drugs, and in large-scale databases. We also demonstrate that our algorithm can be used efficiently to discover interactions of new drugs as part of the drug discovery process

Dynamical properties of model communication networks

We study the dynamical properties of a collection of models for communication processes, characterized by a single parameter $\xi$ representing the relation between information load of the nodes and its ability to deliver this information. The critical transition to congestion reported so far occurs only for $\xi=1$. This case is well analyzed for different network topologies. We focus of the properties of the order parameter, the susceptibility and the time correlations when approaching the critical point. For $\xi<1$ no transition to congestion is observed but it remains a cross-over from a low-density to a high-density state. For $\xi>1$ the transition to congestion is discontinuous and congestion nuclei arise.Comment: 8 pages, 8 figure

Predicting human preferences using the block structure of complex social networks

With ever-increasing available data, predicting individuals' preferences and helping them locate the most relevant information has become a pressing need. Understanding and predicting preferences is also important from a fundamental point of view, as part of what has been called a "new" computational social science. Here, we propose a novel approach based on stochastic block models, which have been developed by sociologists as plausible models of complex networks of social interactions. Our model is in the spirit of predicting individuals' preferences based on the preferences of others but, rather than fitting a particular model, we rely on a Bayesian approach that samples over the ensemble of all possible models. We show that our approach is considerably more accurate than leading recommender algorithms, with major relative improvements between 38% and 99% over industry-level algorithms. Besides, our approach sheds light on decision-making processes by identifying groups of individuals that have consistently similar preferences, and enabling the analysis of the characteristics of those groups

Investigating homeostatic disruption by constitutive signals during biological ageing

PhD ThesisAgeing and disease can be understood in terms of a loss in biological homeostasis. This will often manifest as a constitutive elevation in the basal levels of biological entities. Examples include chronic inflammation, hormonal imbalances and oxidative stress. The ability of reactive oxygen species (ROS) to cause molecular damage has meant that chronic oxidative stress has been mostly studied from the point of view of being a source of toxicity to the cell. However, the known duality of ROS molecules as both damaging agents and cellular redox signals implies another perspective in the study of sustained oxidative stress. This is a perspective of studying oxidative stress as a constitutive signal within the cell. In this work a computational modelling approach is undertaken to examine how chronic oxidative stress can interfere with signal processing by redox signalling pathways in the cell. A primary outcome of this study is that constitutive signals can give rise to a ‘molecular habituation’ effect that can prime for a gradual loss of biological function. Experimental results obtained highlight the difficulties in testing for this effect in cell lines exposed to oxidative stress. However, further analysis suggests this phenomenon is likely to occur in different signalling pathways exposed to persistent signals and potentially at different levels of biological organisation.Centre for Integrated Research into Musculoskeletal Ageing (CIMA) and through them, Arthritis Research UK and the Medical Research Counc

A comparative analysis of data mining algorithms to mitigate spurious detections in Gaia

Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2017, Tutora: Francesca FiguerasGaia is an ESA mission that observes about 50 million sources per day. A small part of these detections are considered spurious generated for example by cosmic rays. The main objective of this study is to perform a comparative analysis of several algorithms to automatically detect spurious detections. Successfully identifying these detections is important to prevent them from entering the cross-match stage where they create several problems and degrade resolution performance. We will use appropriate metrics to determine the execution and assess the algorithms. Finally, it will be discussed if any of these data mining algorithms could be a good solution to the spurious detection problem

A wet chemistry synthesis of silver nanoparticles from bulk material

Master of ScienceDepartment of PhysicsChristopher M. SorensenAn easier, cheaper and scalable method to obtain silver nanoparticles, AgNPs, directly from the bulk material has been obtained. Two different solvents were tried, water and ethylene glycol, the coating agent was polyvinylpyrrolidone, PVP, and two different silver sizes were used, micron size powder and silver shots, millimeter size. It was seen that changing the size of bulk silver, the temperature of reaction, the amount of oxygen, the concentration of PVP and its molecular weight all had an important influence in the synthesis of nanoparticles. Different morphologies could be obtained when these parameters were adjusted ranging from spheres to triangles and hexagons. A complex mechanism is proposed: during the first step, bulk silver is oxidized by oxygen in solution, forming a thin layer of oxidized silver on the surface. Then, PVP acts as a reducing agent at the oxidized surface, where silver becomes Ag⁰ again. At the same time that PVP reduces the oxidized silver back to metallic silver; it coordinates with the silver atoms acting as a protecting agent. That coordination between PVP and silver pulls out the atoms and produces a detachment of silver atoms from the bulk surface. These silver-PVP complexes in solution later combine to form silver nanospheres and evolve to rods first and then triangles and hexagon with longer reaction time