Sharp and local L-1 a posteriori error estimates are established for so-called "well-balanced" BV (hence possibly discontinuous) numerical approximations of 2 x 2 space-dependent Jin-Xin relaxation systems under sub-characteristic condition. According to the strength of the relaxation process, one can distinguish between two complementary regimes: 1) a weak relaxation, where local L-1 errors are shown to be of first order in Delta x and uniform in time, 2) a strong relaxation, where numerical solutions are kept close to entropy solutions of the reduced scalar conservation law, and for which Kuznetsov's theory indicates a behavior of the L1 error in t center dot root Delta x. The uniformly first-order accuracy in weak relaxation regime is obtained by carefully studying interaction patterns and building up a seemingly original variant of Bressan Liu Yang's functional, able to handle BV solutions of arbitrary size for these particular inhomogeneous systems. The complementary estimate in strong relaxation regime is proven by means of a suitable extension of methods based on entropy dissipation for space-dependent problems. Preliminary numerical illustrations are provided.

International initiatives such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) are collecting multiple datasets at different genome-scales with the aim of identifying novel cancer biomarkers and predicting survival of patients. To analyze such data, several statistical methods have been applied, among them Cox regression models. Although these models provide a good statistical framework to analyze omic data, there is still a lack of studies that illustrate advantages and drawbacks in integrating biological information and selecting groups of biomarkers. In fact, classical Cox regression algorithms focus on the selection of a single biomarker, without taking into account the strong correlation between genes. Even though network-based Cox regression algorithms overcome such drawbacks, such network-based approaches are less widely used within the life science community. In this article, we aim to provide a clear methodological framework on the use of such approaches in order to turn cancer research results into clinical applications. Therefore, we first discuss the rationale and the practical usage of three recently proposed network-based Cox regression algorithms (i.e., Net-Cox, AdaLnet, and fastcox). Then, we show how to combine existing biological knowledge and available data with such algorithms to identify networks of cancer biomarkers and to estimate survival of patients. Finally, we describe in detail a new permutation-based approach to better validate the significance of the selection in terms of cancer gene signatures and pathway/networks identification. We illustrate the proposed methodology by means of both simulations and real case studies. Overall, the aim of our work is two-fold. Firstly, to show how network-based Cox regression models can be used to integrate biological knowledge (e.g., multi-omics data) for the analysis of survival data. Secondly, to provide a clear methodological and computational approach for investigating cancers regulatory networks. International initiatives such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) are collecting multiple datasets at different genome-scales with the aim of identifying novel cancer biomarkers and predicting survival of patients. To analyze such data, several statistical methods have been applied, among them Cox regression models. Although these models provide a good statistical framework to analyze omic data, there is still a lack of studies that illustrate advantages and drawbacks in integrating biological information and selecting groups of biomarkers. In fact, classical Cox regression algorithms focus on the selection of a single biomarker, without taking into account the strong correlation between genes. Even though network-based Cox regression algorithms overcome such drawbacks, such network-based approaches are less widely used within the life science community. In this article, we aim to provide a clear methodological framework on the use of such approaches in order to turn cancer research results into clinical applications. Therefore, we first discuss the rationale and the practical usage of three recently proposed network-based Cox regression algorithms (i.e., Net-Cox, AdaLnet, and fastcox). Then, we show how to combine existing biological knowledge and available data with such algorithms to identify networks of cancer biomarkers and to estimate survival of patients. Finally, we describe in detail a new permutation based approach to better validate the significance of the selection in terms of cancer gene signatures and pathway/networks identification. We illustrate the proposed methodology by means of both simulations and real case studies. Overall, the aim of our work is two-fold. Firstly, to show how network-based Cox regression models can be used to integrate biological knowledge (e.g., multi-omics data) for the analysis of survival data. Secondly, to provide a clear methodological and computational approach for investigating cancers regulatory networks. Keywords: cancer, Cox model, high-dimensionality, gene expression, network, regularization, survival

Results: Teleosts living in different environments (freshwater and seawater) and with different lifestyles (migratory and non-migratory) were analyzed studying three variables: routine metabolic rate, gill area and genomic GC-content, none of them showing a phylogenetic signal among fish species. Routine metabolic rate, specific gill area and average genomic GC were higher in seawater than freshwater species. The same trend was observed comparing migratory versus non-migratory species. Crossing salinity and lifestyle, the active migratory species living in seawater show coincidentally the highest routine metabolic rate, the highest specific gill area and the highest average genomic GC content. Background: The DNA base composition is well known to be highly variable among organisms. Bio-physic studies on the effect of the GC increments on the DNA structure have shown that GC-richer DNA sequences are more bendable. The result was the keystone of the hypothesis proposing the metabolic rate as the major force driving the GC content variability, since an increased resistance to the torsion stress is mainly required during the transcription process to avoid DNA breakage. Hence, the aim of the present work is to test if both salinity and migration, suggested to affect the metabolic rate of teleostean fishes, affect the average genomic GC content as well. Moreover, since the gill surface has been reported to be a major morphological expression of metabolic rate, this parameter was also analyzed in the light of the above hypothesis.

We present the advancements and novelties recently introduced in RNASeqGUI, a graphical user interface that helps biologists to handle and analyse large data collected in RNA-Seq experiments. This work focuses on the concept of reproducible research and shows how it has been incorporated in RNASeqGUI to provide reproducible (computational) results. The novel version of RNASeqGUI combines graphical interfaces with tools for reproducible research, such as literate statistical programming, human readable report, parallel executions, caching, and interactive and web-explorable tables of results. These features allow the user to analyse big datasets in a fast, efficient, and reproducible way. Moreover, this paper represents a proof of concept, showing a simple way to develop computational tools for Life Science in the spirit of reproducible research.

Experiments of cell migration and chemotaxis assays have been classically performed in the so-called Boyden Chambers. A recent technology, xCELLigence Real Time Cell Analysis, is now allowing to monitor the cell migration in real time. This technology measures impedance changes caused by the gradual increase of electrode surface occupation by cells during the course of time and provide a Cell Index which is proportional to cellular morphology, spreading, ruffling and adhesion quality as well as cell number. In this paper we propose a macroscopic mathematical model, based on advection-reaction-diffusion partial differential equations, describing the cell migration assay using the real-time technology. We carried out numerical simulations to compare simulated model dynamics with data of observed biological experiments on three different cell lines and in two experimental settings: absence of chemotactic signals (basal migration) and presence of a chemoattractant. Overall we conclude that our minimal mathematical model is able to describe the phenomenon in the real time scale and numerical results show a good agreement with the experimental evidences.

The human retina is a specialized tissue involved in light stimulus transduction. Despite its unique biology, an accurate reference transcriptome is still missing. Here, we performed gene expression analysis (RNA-seq) of 50 retinal samples from non-visually impaired post-mortem donors. We identified novel transcripts with high confidence (Observed Transcriptome (ObsT)) and quantified the expression level of known transcripts (Reference Transcriptome (RefT)). The ObsT included 77 623 transcripts (23 960 genes) covering 137 Mb (35 Mb new transcribed genome). Most of the transcripts (92%) were multi-exonic: 81% with known isoforms, 16% with new isoforms and 3% belonging to new genes. The RefT included 13 792 genes across 94 521 known transcripts. Mitochondrial genes were among the most highly expressed, accounting for about 10% of the reads. Of all the protein-coding genes in Gencode, 65% are expressed in the retina. We exploited inter-individual variability in gene expression to infer a gene co-expression network and to identify genes specifically expressed in photoreceptor cells. We experimentally validated the photoreceptors localization of three genes in human retina that had not been previously reported. RNA-seq data and the gene co-expression network are available online (http://retina.tigem.it).

Reproducible (computational) Research is crucial to produce transparent and high quality scientific papers. First, we illustrate the benefits that scientific community can receive from the adoption of Reproducible Research standards in the analysis of high-throughput omic data. Then, we describe several tools useful to researchers to increase the reproducibility of their works. Moreover, we face the advantages and limits of reproducible research and how they could be addressed and solved. Overall, this paper should be considered as a proof of concept on how and what characteristic - in our opinion - should be considered to conduct a study in the spirit of Reproducible Research. Therefore, the scope of this paper is two-fold. The first goal consists in presenting and discussing some easy-to-use instruments for data analysts to promote reproducible research in their analyses. The second aim is to encourage developers to incorporate automatic reproducibility features in their tools.

Prefazione al volume CIBB2015 edito da springer

Free-living bacteria grown under aerobic conditions were used to investigate, by next-generation RNA sequencing analysis, the transcriptional profiles of Sinorhizobium meliloti wild-type 1021 and its derivative, RD64, overproducing the main auxin indole-3-acetic acid (IAA). Among the upregulated genes in RD64 cells, we detected the main nitrogen-fixation regulator fixJ, the two intermediate regulators fixK and nifA, and several other genes known to be FixJ targets. The gene coding for the sigma factor RpoH1 and other genes involved in stress response, regulated in a RpoH1-dependent manner in S. meliloti, were also induced in RD64 cells. Under microaerobic condition, quantitative real-time polymerase chain reaction analysis revealed that the genes fixJL and nifA were up-regulated in RD64 cells as compared with 1021 cells. This work provided evidence that the overexpression of IAA in S. meliloti free-living cells induced many of the transcriptional changes that normally occur in nitrogen-fixing root nodule.

questa presentazione rivisiteremo il concetto di interazione geneambiente alla luce delle conoscenze scientifiche emergenti e dell'utilizzo delle tecnologie ad alta risoluzione, quali i moderni sequenziatori. In particolare, illustreremo l'importanza della stima di tale interazione per la comprensione delle patologie complesse. Quindi, forniremo una panoramica dei metodi esistenti per l'analisi di dati di NGS con particolare enfasi riguardo l'individuazione di varianti genomiche ed l'analisi dell'epigenomica e della trascrittomica. Infine, discuteremo come i dati multiomici possono essere utilizzati per migliorare in nostro grado di comprensione delle patologie e fornire nuove prospettive di ricerca In this talk, first, we review the concept of gene-environmental interaction on the light of emerging results and the use of modern high-throughput technologies; we illustrate its impact on the understanding of complex human diseases. Then, we provide an overview of the methods available to process NGS data with particular emphasis to the detection of genomic variants, the analysis of epigenomic and transcriptional data produced by modern sequencers. Finally, we discuss how multiomic data can be used to improve our way of studying complex diseases and can provide novel research perspectives.

Mass spectrometry is a set of technologies with many applications in characterizing biological samples. Due to the huge quantity of data, often biased and contaminated by different source of errors, and the amount of results that is possible to extract, an easy-to-learn and complete workflow is essential. GeenaR is a robust web tool for pre-processing, analysing, visualizing and comparing a set of MALDI-ToF mass spectra. It combines PHP, Perl and R languages and allows different levels of control over the parameters, in order to adapt the work to the needs and expertise of the users.

Vesicles are involved in a vast variety of transport processes in living organisms. Additionally, they serve as a model for the dynamics of cell suspensions. Predicting the rheological properties of their suspensions is still an open question, as even the interaction of pairs is yet to be fully understood. Here we analyse the effect of a single vesicle, undergoing tank-treading motion, on its surrounding shear flow by studying the induced disturbance field delta(V) over right arrow, the difference between the velocity field in its presence and absence. The comparison between experiments and numerical simulations reveals an impressive agreement. Tracking ridges in the disturbance field magnitude landscape, we identify the principal directions along which the velocity difference field is analysed in the vesicle vicinity. The disturbance magnitude is found to be significant up to about 4 vesicle radii and can be described by a power law decay with the distance d from the vesicle parallel to delta(V) over right arrow parallel to proportional to d(-3/2). This is consistent with previous experimental results on the separation distance between two interacting vesicles under similar conditions, for which their dynamics is altered. This is an indication of vesicles long-range effect via the disturbance field and calls for the proper incorporation of long-range hydrodynamic interactions when attempting to derive rheological properties of vesicle suspensions. Copyright (C) EPLA, 2016

A deep understanding of the dynamics and rheology of suspensions of vesicles, cells, and capsules is relevant for different applications, ranging from soft glasses to blood flow [1]. I will present the study of suspensions of fluid vesicles by a combination of molecular dynamics and mesoscale hydrodynamics simulations (multi-particle collision dynamics) in two dimensions [2], pointing out the big potential of the numerical method to address problems in soft matter. The flow behavior is studied as a function of the shear rate, the volume fraction of vesicles, and the viscosity ratio between inside and outside fluids. Results are obtained for the interactions of two vesicles, the intrinsic viscosity of the suspension, and the cell-free layer near the walls [3-4]. [1] D. Barthes-Biesel, Annu. Rev. Fluid Mech. 48, 25 (2016) [2] R. Finken, A. Lamura, U. Seifert, and G. Gompper, Eur. Phys. J. E 25, 309 (2008) [3] A. Lamura and G. Gompper, EPL 102, 28004 (2013) [4] A. Lamura and G. Gompper, Procedia IUTAM 16, 3 (2015)

We investigate front propagation in systems with diffusive and subdiffusive behavior. The scaling behavior of moments of the diffusive problem, both in the standard and in the anomalous cases, is not enough to determine the features of the reactive front. In fact, the shape of the bulk of the probability distribution of the transport process, which determines the diffusive properties, is important just for preasymptotic behavior of front propagation, while the precise shape of the tails of the probability distribution determines asymptotic behavior of front propagation.

The development of high-efficiency porous catalyst membranes critically depends on our understanding of where the majority of the chemical conversions occur within the porous structure. This requires mapping of chemical reactions and mass transport inside the complex nanoscale architecture of porous catalyst membranes which is a multiscale problem in both the temporal and spatial domains. To address this problem, we developed a multiscale mass transport computational framework based on the lattice Boltzmann method that allows us to account for catalytic reactions at the gas-solid interface by introducing a new boundary condition. In good agreement with experiments, the simulations reveal that most catalytic reactions occur near the gas-flow facing side of the catalyst membrane if chemical reactions are fast compared to mass transport within the porous catalyst membrane.

We study a nonlinear boundary value problem involving a nonlocal (integral) operator in the coefficients of the unknown function. Provided sufficient conditions for the existence and uniqueness of the solution, for its approximation, we propose a numerical method consisting of a classical discretization of the problem and an algorithm to solve the resulting nonlocal and nonlinear algebraic system by means of some iterative procedures. The second order of convergence is assured by different sufficient conditions, which can be alternatively used in dependence on the given data. The theoretical results are confirmed by several numerical tests. (C) 2015 Elsevier B.V. All rights reserved.

For the effective operation of sonar systems mounted inside the bulb of fast ships, it is important to reduce all the possible noise and vibration sources that radiate noise and interfere with sonar sensor response. In particular, pressure fluctuations induced by turbulent boundary layers on the sonar dome surface represent the major source of self-noise for on-board sensors. Reliable calculations of structural vibrations and noise radiated inside the dome require valid statistical descriptions of wall pressure fluctuations beneath the turbulent boundary layer. Previous research about wall pressure fluctuations deals with equilibrium turbulent boundary layers on flat plates in zero pressure gradient flow, for which scaling laws for power spectral densities and empirical models for the cross spectral densities are well established. On the contrary, turbulent boundary layers on bulbous bow exhibit the combined effects of three-dimensionality, streamline and spanwise curvatures and pressure gradients. In order to collect information about realistic configurations, wall pressure fluctuations were measured in an experimental campaign performed in a towing tank; data were collected at two different locations along a large scale model of a ship bulb and their spectral characteristics were investigated in terms of auto and cross spectral densities. Mean flow parameters of the boundary layer, required in the analysis, were obtained by a finite volume code that solves the Reynolds Averaged Navier Stokes Equations. The applicability of classical scaling laws for pressure spectra on zero pressure gradient flat plate was investigated, together with the spatial characterization of the wall pressure fluctuations in the space-frequency domain; parameters of some semi-empirical models available in the scientific literature were tuned to fit the measured pressure field.

Abstract

Systems and synthetic biology for health

Immunosenescence is thought to result from cellular aging and to reflect exposure to environmental stressors and antigens, including cytomegalovirus (CMV). However, not all of the features of immunosenescence are consistent with this view, and this has led to the emergence of the sister theory of "inflammaging". The recently discovered diffuse tissue distribution of resident memory T cells (TRM) which don't recirculate, calls these theories into question. These cells account for most T cells residing in barrier epithelia which sit in and travel through the extracellular matrix (ECM). With almost all studies to date carried out on peripheral blood, the age-related changes of the ECM and their consequences for T cell mobility, which is crucial for the function of these cells, have been largely ignored. We propose an update of the theoretical framework of immunosenescence, based on a novel hypothesis: the increasing stiffness and cross-linking of the senescent ECM lead to a progressive immunodeficiency due to an age-related decrease in T cell mobility and eventually the death of these cells. A key element of this mechanism is the mechanical stress to which the cell cytoplasm and nucleus are subjected during passage through the ECM. This hypothesis is based on an "evo-devo" perspective bringing together some major characteristics of aging, to create a single interpretive framework for immunosenescence.