Detecting modules in biological networks by edge weights clustering and entropy significance

Detection of the modular structure of biological networks is of interest to researchers adopting a systems perspective for the analysis of omics data. Computational systems biology has provided a rich array of methods for network clustering. To date, the majority of approaches address this task through a network node classification based on topological or external quantifiable properties of network nodes. Conversely, numerical properties of network edges are underused, even though the information content which can be associated with network edges has augmented due to steady advances in molecular biology technology over the last decade. Properly accounting for network edges in the development of clustering approaches can become crucial to improve quantitative interpretation of omics data, finally resulting in more biologically plausible models. In this study, we present a novel technique for network module detection, named WG-Cluster (Weighted Graph CLUSTERing). WG-Cluster's notable features, compared to current approaches, lie in: (1) the simultaneous exploitation of network node and edge weights to improve the biological interpretability of the connected components detected, (2) the assessment of their statistical significance, and (3) the identification of emerging topological properties in the detected connected components. WG-Cluster utilizes three major steps: (i) an unsupervised version of k-means edge-based algorithm detects sub-graphs with similar edge weights, (ii) a fast-greedy algorithm detects connected components which are then scored and selected according to the statistical significance of their scores, and (iii) an analysis of the convolution between sub-graph mean edge weight and connected component score provides a summarizing view of the connected components. WG-Cluster can be applied to directed and undirected networks of different types of interacting entities and scales up to large omics data sets. Here, we show that WG-Cluster can be successfully used in the differential analysis of physical protein-protein interaction (PPI) networks. Specifically, applying WG-Cluster to a PPI network weighted by measurements of differential gene expression permits to explore the changes in network topology under two distinct (normal vs. tumor) conditions. WG-Cluster code is available at https://sites.google.com/site/paolaleccapersonalpage/

The external benefits of higher education

The private market benefits of education are widely studied at the micro level, although the magnitude of their macroeconomic impact is disputed. However, there are additional benefits of education, which are less well understood. In this paper the macroeconomic effects of external benefits of higher education are estimated using the “micro-to-macro” simulation approach. Two types of externalities are explored: technology spillovers and productivity spillovers in the labour market. These links are illustrated and the results suggest they could be very large. However, this is qualified by the dearth of microeconomic evidence, for which we hope to encourage further work

Inferring biochemical reaction pathways: the case of the gemcitabine pharmacokinetics.

Background The representation of a biochemical system as a network is the precursor of any mathematical model of the processes driving the dynamics of that system. Pharmacokinetics uses mathematical models to describe the interactions between drug, and drug metabolites and targets and through the simulation of these models predicts drug levels and/or dynamic behaviors of drug entities in the body. Therefore, the development of computational techniques for inferring the interaction network of the drug entities and its kinetic parameters from observational data is raising great interest in the scientic community of pharmacologists. In fact, the network inference is a set of mathematical procedures deducing the structure of a model from the experimental data associated to the nodes of the network of interactions. In this paper, we deal with the inference of a pharmacokinetic network from the concentrations of the drug and its metabolites observed at discrete time points. Results The method of network inference presented in this paper is inspired by the theory of time-lagged correlation inference with regard to the deduction of the interaction network, and on a maximum likelihood approach with regard to the estimation of the kinetic parameters of the network. Both network inference and parameter estimation have been designed specically to identify systems of biotransformations, at the biochemical level, from noisy time-resolved experimental data. We use our inference method to deduce the metabolic pathway of the gemcitabine. The inputs to our inference algorithm are the experimental time series of the concentration of gemcitabine and its metabolites. The output is the set of reactions of the metabolic network of the gemcitabine. Conclusions Time-lagged correlation based inference pairs up to a probabilistic model of parameter inference from metabolites time series allows the identication of the microscopic pharmacokinetics and pharmacodynamics of a drug with a minimal a priori knowledge. In fact, the inference model presented in this paper is completely unsupervised. It takes as input the time series of the concetrations of the parent drug and its metabolites. The method, applied to the case study of the gemcitabine pharmacokinetics, shows good accuracy and sensitivit

A Bayesian belief network for local air quality forecasting

This study is focused on the development of a Bayesian network for air quality assessment and aims at offering a pragmatic and scientifically credible approach to modelling complex systems where substantial uncertainties exist. In particular, the main object is the prediction of the occurrence of suitable conditions for the stagnation of pollutants in a given area. The analytical modeling of the network provides a set of independent nodes, represented by the outputs of a forecasting meteorological Limited Area Model, from which descend the conditions for the stagnation of pollutants in different areas of the city (through measurements of the heuristic pollutant from monitoring stations) and finally the global conditions. The urban area of Genoa (Italy) was selected in order to test the actual capability of the model prototype. Network training was performed by means of historical data resulting from significant statistical series of the past years by the air quality-monitoring network. The system used for data assimilation, construction and network learning is completely based on an open source statistical processing software

AQUAGRID: an extensible platform for collaborative problem solving in groundwater protection

AQUAGRID is the subsurface hydrology computational service of the Sardinian GRIDA3 infrastructure, designed to deliver complex environmental applications via a user-friendly Web portal. The service aims to provide to water professionals integrated modeling tools to solve water resources management problems and aid decision making for contaminated soil and groundwater. In this paper, the AQUAGRID application concept and enabling technologies are illustrated. At the heart of the service are the computational models to simulate complex and large groundwater flow and contaminant transport problems and geochemical speciation. AQUAGRID is built on top of compute-Grid technologies by means of the EnginFrame Grid framework. Distributed data management is provided by the Storage Resource Broker data-Grid middleware. The resulting environment allows end-users to perform groundwater simulations and to visualize and interact with their results, using graphs, 3D images and annotated maps. The problem solving capability of the platform is demonstrated using the results of two case studies deployed

The regional economic impact of more graduates in the labour market: a “micro-to-macro” analysis for Scotland

This paper explores the system-wide impact of graduates on the regional economy. Graduates enjoy a significant wage premium, often interpreted as reflecting their greater productivity relative to non-graduates. If this is so there is a clear and direct supply-side impact of HEI activities on regional economies. We use an HEI-disaggregated computable general equilibrium model of Scotland to estimate the impact of the growing proportion of graduates in the Scottish labour force that is implied by the current participation rate and demographic change, taking the graduate wage premium in Scotland as an indicator of productivity enhancement. While the detailed results vary with alternative assumptions about the extent to which wage premia reflect productivity, they do suggest that the long-term supply-side impacts of HEIs provide a significant boost to regional GDP. Furthermore, the results suggest that the supply-side impacts of HEIs are likely to be more important than the expenditure impacts that are the focus of most HEI impact studies

Submarine Geomorphology of the Southwestern Sardinian Continental Shelf (Mediterranean Sea): Insights into the Last Glacial Maximum Sea-Level Changes and Related Environments

During the lowstand sea-level phase of the Last Glacial Maximum (LGM), a large part of the current Mediterranean continental shelf emerged. Erosional and depositional processes shaped the coastal strips, while inland areas were affected by aeolian and fluvial processes. Evidence of both the lowstand phase and the subsequent phases of eustatic sea level rise can be observed on the continental shelf of Sardinia (Italy), including submerged palaeo-shorelines and landforms, and indicators of relict coastal palaeo-environments. This paper shows the results of a high-resolution survey on the continental shelf off San Pietro Island (southwestern Sardinia). Multisensor and multiscale data\u2014obtained by means of seismic sparker, sub-bottom profiler chirp, multibeam, side scan sonar, diving, and uncrewed aerial vehicles\u2014made it possible to reconstruct the morphological features shaped during the LGM at depths between 125 and 135 m. In particular, tectonic controlled palaeo-cliffs affected by landslides, the mouth of a deep palaeo-valley fossilized by marine sediments and a palaeo-lagoon containing a peri-littoral thanatocenosis (18,983 \ub1 268 cal BP) were detected. The Younger Dryas palaeo-shorelines were reconstructed, highlighted by a very well preserved beachrock. The coastal paleo-landscape with lagoon-barrier systems and retro-littoral dunes frequented by the Mesolithic populations was reconstructed