Results: Our approach MIMO (Molecular Interaction Maps Overlap) addresses the first problem by allowing the introduction of gaps and mismatches between query and template pathways and permits - when necessary-supervised queries incorporating a priori biological information. It then addresses the second issue by relying directly on the rich graph topology described in the Systems Biology Markup Language (SBML) standard, and uses multidigraphs to efficiently handle multiple queries on biological graph databases. The algorithm has been here successfully used to highlight the contact point between various human pathways in the Reactome database. Background: Molecular pathways represent an ensemble of interactions occurring among molecules within the cell and between cells. The identification of similarities between molecular pathways across organisms and functions has a critical role in understanding complex biological processes. For the inference of such novel information, the comparison of molecular pathways requires to account for imperfect matches (flexibility) and to efficiently handle complex network topologies. To date, these characteristics are only partially available in tools designed to compare molecular interaction maps.

Results: We address the latter of these two challenges by testing an integrated approach on a known cancer benchmark: the NCI-60 cell panel. Here, high-throughput screens for mRNA, miRNA and proteins are jointly analyzed using factor analysis, combined with linear discriminant analysis, to identify the molecular characteristics of cancer. Comparisons with separate (non-joint) analyses show that the proposed integrated approach can uncover deeper and more precise biological information. In particular, the integrated approach gives a more complete picture of the set of miRNAs identified and the Wnt pathway, which represents an important surrogate marker of melanoma progression. We further test the approach on a more challenging patient-dataset, for which we are able to identify clinically relevant markers. Background: High-throughput (omic) data have become more widespread in both quantity and frequency of use, thanks to technological advances, lower costs and higher precision. Consequently, computational scientists are confronted by two parallel challenges: on one side, the design of efficient methods to interpret each of these data in their own right (gene expression signatures, protein markers, etc.) and, on the other side, realization of a novel, pressing request from the biological field to design methodologies that allow for these data to be interpreted as a whole, i.e. not only as the union of relevant molecules in each of these layers, but as a complex molecular signature containing proteins, mRNAs and miRNAs, all of which must be directly associated in the results of analyses that are able to capture inter-layers connections and complexity.

The aim of this chapter is to present an overview of the main results for a well-known optimization problem and an emerging optimization area, as well as introducing a new problem which is related to both of them. The first part of the chapter presents an overview of the main existing results for the classical maximum flow problem. The maximum flow problem is one of the most studied optimization problems in the last decades. Besides its many practical applications, it also arises as a subproblem of several other complex problems (e.g., min cost flow, matching, covering on bipartite graphs). Subsequently, the chapter introduces some problems defined on edge-labeled graphs by reviewing the most relevant results in this field. Edge-labeled graphs are used to model situations where it is crucial to represent qualitative differences (instead of quantitative ones) among different regions of the graph itself. Finally, the maximum flow problem with the minimum number of labels (MF-ML) problem is presented and discussed. The aim is to maximize the network flow as well as the homogeneity of the solution on a capacitated network with logic attributes.

This report describes the development of a numerical optimization procedure for the analysis of marine propellers. The activity is performed by INSEAN in the framework of WP35 of the STREAMLINE Project and the present report is written in fulfilment of Deliverable D35.4. The work described in the report is aimed at developing and integrating numerical optimization tools to build an optimal design framework for the improvement of state-of-art screw propellers in real operating conditions. The optimization problem is recast as a shape optimization study and computational strategies to achieve an efficient and flexible parametrization of propeller blade geometry are reviewed. Classical Free Form Deformation techniques are used as a reference to develop a novel approach, called Conformal Free Form Deformation. The capability of this technique to perform propeller blade shape manipulations is demonstrated through examples. Alternative optimization frameworks are outlined and compared. The importance of introducing a Robust Design Optimization model is clarified in order to make possible the analysis of propeller performance over a representative range of variations of operating conditions instead of considering an isolated design point. In addition to that, different algorithms based on local optimization and global optimization strategies are compared. The impact of introducing design constraints into the procedure is then discussed through simple example describing propeller shape manipulation studies based on a limited number of parameters. Requirements for the selection of a propeller hydrodynamics model to be interfaced with the optimization model are analysed. The Boundary Element Method developed as part of WP34 activities is chosen as an adequate trade-off between computational efficiency and accuracy of numerical predictions of propeller performance. Finally, numerical examples are presented and results of simple propeller optimization studies are discussed to analyse the capabilities of the proposed methodology.

We give a simple convexity-based proof of the following fact: the only eigenfunction of the p-Laplacian that does not change sign is the first one. The method of proof covers also more general nonlinear eigenvalue problems. © 2012 Springer Basel.

Wireless sensor networks involve many different real-world contexts, such as monitoring and control tasks for traffic, surveillance, military and environmental applications, among others. Usually, these applications consider the use of a large number of low-cost sensing devices to monitor the activities occurring in a certain set of target locations. We want to individuate a set of covers (that is, subsets of sensors that can cover the whole set of targets) and appropriate activation times for each of them in order to maximize the total amount of time in which the monitoring activity can be performed (network lifetime), under the constraint given by the limited power of the battery contained in each sensor. A variant of this problem considers that each sensor can be activated in a certain number of alternative power levels, which determine different sensing ranges and power consumptions. We present some heuristic approaches and an exact approach based on the column generation technique. An extensive experimental phase proves the advantage in terms of solution quality of using adjustable sensing ranges with respect to the classical single range scheme. © 2012 Elsevier B.V. All rights reserved.

In recent years there has been a growing interest in frame based de-noising procedures. The advantage of frames with respect to classical orthonor- mal bases (e.g. wavelet, Fourier, polynomial) is that they can furnish an efficient representation of a more broad class of signals. For example, signals which have fast oscillating behavior as sonar, radar, EEG, stock market, audio and speech are much more well represented by a frame (with similar oscillating characteristic) than by a classical wavelet basis, although the frame representation for such kind of signals can be not properly sparse. In literature the frame based de-noising procedures can be divided into two classes: Bayesian approaches and variational approaches: both types promote sparseness through specific prior hypothesis or penalization term. A new frame based de-noising procedure is presented where no sparseness hypothesis is required on frame coefficients. In particular, the estimator is derived as the empirical version of the Wiener filter general- ized to the frame operator. An analytic expression of it is furnished so no searching strategy is required for the implementation. Results on standard and real test signals are presented.

A model problem of the flow under an air-cushion vessel is studied. Two different numerical techniques are used to determine the solution of the free-surface elevation and the wave resistance for a range of Froude number, Reynolds number, value of the pressure applied in the cushion, and depth of the water. The first numerical technique uses a velocity potential that satisfies linearized free-surface boundary conditions, whereas the second employs a finite-volume method to find a solution that satisfies the fully nonlinear free-surface boundary conditions. The results clearly show that for high Froude number and practical values of the cushion pressure, the linear-theory solution is in excellent agreement with the more exact nonlinear prediction. For lower Froude number the solution becomes unsteady, and the disagreement between the two methods is larger.

In this paper we present a functional Bayesian method for detecting genes which are temporally differentially expressed between several conditions. We identify the nature of differential expression (e.g., gene is differentially expressed between the first and the second sample but is not differentially expressed between the second and the third) and subsequently we estimate gene expression temporal profiles. The proposed procedure deals successfully with various technical difficulties which arise in microarray time-course experiments such as a small number of observations, non-uniform sampling intervals and presence of missing data or repeated measurements. The procedure allows to account for various types of errors, thus, offering a good compromise between nonparametric and normality assumption based techniques. In addition, all evaluations are carried out using analytic expressions, hence, the entire procedure requires very small computational effort. The performance of the procedure is studied using simulated data.

A kinetic description is proposed of a fluid species moving in a porous medium and chemically interacting with it. The porosity is included in the model by a modification of the standard kinetic equations for two gaseous species diffusing in a background medium of two solid species. The validity of the proposed kinetic model is assessed by comparing the resulting macroscopic model, obtained by Chapman-Enskog expansion, with macroscopic models present in literature.

A fully quantitative shape index relying upon the asymmetry of mass distribution of protein molecules along the three space dimensions is proposed. Multidimensional statistical analysis, based on principal component extraction and subsequent linear discriminant analysis, showed the presence of three major 'attractor forms' roughly correspondent to rod-like, discoidal and spherical shapes. This classification of protein shapes was in turn demonstrated to be strictly connected with topological features of proteins, as emerging from complex network invariants of their contact maps. © Arrigo et al.