We introduce a new class of finite difference schemes for approximating the solutions to an initial-boundary value problem on a bounded interval for a one-dimensional dissipative hyperbolic system with an external source term, which arises as a simple model of chemotaxis. Since the solutions to this problem may converge to nonconstant asymptotic states for large times, standard schemes usually fail to yield a good approximation. Therefore, we propose a new class of schemes, which use an asymptotic higher order correction, second and third order in our examples, to balance the effects of the source term and the influence of the asymptotic solutions. Special care is needed to deal with boundary conditions to avoid harmful loss of mass. Convergence results are proved for these new schemes, and several numerical tests are presented and discussed to verify the effectiveness of their behavior.

In this paper, by applying a variation of the backward Euler method, we propose a discrete-time SIR epidemic model whose discretization scheme preserves the global asymptotic stability of equilibria for a class of corresponding continuous-time SIR epidemic models. Using discrete-time analogue of Lyapunov functionals, the global asymptotic stability of the equilibria is fully determined by the basic reproduction number, when the infection incidence rate has a suitable monotone property.

In this paper, by constructing Lyapunov functionals, we consider the global dynamics of an SIRS epidemic model with a wide class of nonlinear incidence rates and distributed delays h 0 p(? )f (S(t), I (t - ?))d? under the condition that the total population converges to 1. By using a technical lemma which is derived from strong condition of strict monotonicity of functions f (S,I) and f (S,I)/I with respect to S >= 0 and I > 0, we extend the global stability result for an SIR epidemic model

Inflammation is part of a complex physiological response to harmful stimuli and pathogenic stress. The five components of the Nuclear Factor ?B (NF-?B) family are prominent mediators of inflammation, acting as key transcriptional regulators of hundreds of genes. Several signaling pathways activated by diverse stimuli converge on NF-?B activation, resulting in a regulatory system characterized by high complexity. It is increasingly recognized that the number of components that impinges upon phenotypic outcomes of signal transduction pathways may be higher than those taken into consideration from canonical pathway representations. Scope of the present analysis is to provide a wider, systemic picture of the NF-?B signaling system. Data from different sources such as literature, functional enrichment web resources, protein-protein interaction and pathway databases have been gathered, curated, integrated and analyzed in order to reconstruct a single, comprehensive picture of the proteins that interact with, and participate to the NF-?B activation system. Such a reconstruction shows that the NF-?B interactome is substantially different in quantity and quality of components with respect to canonical representations. The analysis highlights that several neglected but topologically central proteins may play a role in the activation of NF-?B mediated responses. Moreover the interactome structure fits with the characteristics of a bow tie architecture. This interactome is intended as an open network resource available for further development, refinement and analysis.

Spatially explicit models consisting of reaction-diffusion partial differential equations are considered in order to model prey-predator interactions, since it is known that the role of spatial processes reveals of great interest in the study of the effects of habitat fragmentation on biodiversity. As almost all of the realistic models in biology, these models are nonlinear and their solution is not knwon is closed form. Our aim is approximating the solution itself by means of exponential Runge-Kutta integrators. Moreover, we apply the shift-and-invert Krylov approach in order to evaluate the entire functions needed for implementing the exponential method. This numerical procedure reveals to be very efficient in avoiding numerical instability during the simulation, since it allows us to adopt high order in the accuracy.

A major goal of bioinformatics is the characterization of transcription factors and the transcriptional programs they regulate. Given the speed of genome sequencing, we would like to quickly annotate regulatory sequences in newly-sequenced genomes. In such cases, it would be helpful to predict sequence motifs by using experimental data from closely related model organism. Here we present a general algorithm that allow to identify transcription factor binding sites in one newly sequenced species by performing Bayesian regression on the annotated species. First we set the rationale of our method by applying it within the same species, then we extend it to use data available in closely related species. Finally, we generalise the method to handle the case when a certain number of experiments, from several species close to the species on which to make inference, are available. In order to show the performance of the method, we analyse three functionally related networks in the Ascomycota. Two gene network case studies are related to the G2/M phase of the Ascomycota cell cycle; the third is related to morphogenesis. We also compared the method with MatrixReduce and discuss other types of validation and tests. The first network is well known and provides a biological validation test of the method. The two cell cycle case studies, where the gene network size is conserved, demonstrate an effective utility in annotating new species sequences using all the available replicas from model species. The third case, where the gene network size varies among species, shows that the combination of information is less powerful but is still informative. Our methodology is quite general and could be extended to integrate other high-throughput data from model organisms.

Background: At present five evolutionary hypotheses have been proposed to explain the great variability of the genomic GC content among and within genomes: the mutational bias, the biased gene conversion, the DNA breakpoints distribution, the thermal stability and the metabolic rate. Several studies carried out on bacteria and teleostean fish pointed towards the critical role played by the environment on the metabolic rate in shaping the base composition of genomes. In mammals the debate is still open, and evidences have been produced in favor of each evolutionary hypothesis. Human genes were assigned to three large functional categories (as well as to the corresponding functional classes) according to the KOG database: (i) information storage and processing, (ii) cellular processes and signaling, and (iii) metabolism. The classification was extended to the organisms so far analyzed performing a reciprocal Blastp and selecting the best reciprocal hit. The base composition was calculated for each sequence of the whole CDS dataset. Results: The GC3 level of the above functional categories was increasing from (i) to (iii). This specific compositional pattern was found, as footprint, in all mammalian genomes, but not in frog and lizard ones. Comparative analysis of human versus both frog and lizard functional categories showed that genes involved in the metabolic processes underwent the highest GC3 increment. Analyzing the KOG functional classes of genes, again a well defined intragenomic pattern was found in all mammals. Not only genes of metabolic pathways, but also genes involved in chromatin structure and dynamics, transcription, signal transduction mechanisms and cytoskeleton, showed an average GC3 level higher than that of the whole genome. In the case of the human genome, the genes of the aforementioned functional categories showed a high probability to be associated with the chromosomal bands. Conclusions: In the light of different evolutionary hypotheses proposed so far, and contributing with different potential to the genome compositional heterogeneity of mammalian genomes, the one based on the metabolic rate seems to play not a minor role. Keeping in mind similar results reported in bacteria and in teleosts, the specific compositional patterns observed in mammals highlight metabolic rate as unifying factor that fits over a wide range of living organisms.

The realization of innovative transport services, require increasingly greater flexibility and inexpensiveness of the service. In many cases the solution is to realize a demand responsive transportation system, in which there are two main goals: minimize costs and maximize flexibility. In this work, we address a Demand Responsive Transport System capable of managing incoming transport demand using a solution based on an insertion heuristics to solve an On-Line dynamic DaRP instance. The solutions provided by the heuristics are simulated dynamically in a discrete events environment in which it is possible to reproduce the movement of the vehicles, the passengers' arrival to the stops, the delays due to the traffic congestion and possible anomalies in the behavior of the passengers. Finally, at the end of the simulation, a set of performance indicators summarize the solution planned by the heuristics.

The concept of innovation in transport systems requires the satisfaction of two main objectives: flexibility and costs minimization. The demand responsive transport systems (DRTS) seem to be the solution for the trade-off between flexibility and efficiency. They require the planning of travel paths (routing) and customers pick-up and drop-off times (scheduling) according to received requests and respecting the limited capacity of the fleet and time constraints (hard time windows) for each networks node. Even considering invariable conditions of the network a DRTS may operate according to a static or to a dynamic mode. In the dynamic mode, customers requests arrive when the service is already running and, consequently, the solution may change over time. In this work, we use an algorithm able to solve a dynamic multi-vehicle DaRP by managing incoming transport demand as fast as possible. The heuristics is a greedy method that tries to assign the requests to one of the fleets vehicles finding each time the local optimum. The usage of vehicles only when strictly necessary, provides to costs minimization. The work is enriched by a series of tests with different values of the fleets vehicles and their capacity, of time windows and of incoming requests number. The solutions provided by the heuristics are simulated in a discrete events environment in which its possible to reproduce the movement of the buses, the passengers' arrival to the stops, and in the next step the delays due to the traffic congestion and possible anomalies in the behaviour of the passengers. Finally, at the end of the simulation, a set of performance indicators evaluate the solution planned by the heuristics.

Time-course microarray experiments are an increasingly popular approach for understanding the dynamical behavior of a wide range of biological systems. In this paper we discuss some recently developed functional Bayesian methods specifically designed for time-course microarray data. The methods allow one to identify differentially expressed genes, to rank them, to estimate their expression profiles and to cluster the genes associated with the treatment according to their behavior across time. The methods successfully deal with various technical difficulties that arise in this type of experiments such as a large number of genes, a small number of observations, non-uniform sampling intervals, missing or multiple data and temporal dependence between observations for each gene. The procedures are illustrated using both simulated and real data.

Propeller modelling in CFD simulations is a key issue for the correct prediction of hull-propeller interactions, manoeuvring characteristics and the flow field in the stern region of a marine vehicle. From this point of view, actuator disk approaches have proved their reliability and computational efficiency; for these reasons, they are commonly used for the analysis of propulsive performance of a ship. Nevertheless, these models often neglect peculiar physical phenomena which characterise the operating propeller in off-design condition, namely the in-plane loads that are of paramount importance when considering non-standard or unusual propeller/rudder arrangements. In order to emphasize the importance of these components (in particular the propeller lateral force) and the need of a detailed propeller model for the correct prediction of the manoeuvring qualities of a ship, the turning circle manoeuvre of a self-propelled fully appended twin screw tanker-like ship model with a single rudder is simulated by the unsteady RANS solver ?navis developed at CNR-INSEAN; several propeller models able to include the effect of the strong oblique flow component encountered during a manoeuvre have been considered and compared. It is emphasized that, despite these models account for very complex and fundamental physical effects, which would be lost by a traditional actuator disk approach, the increase in computational resources is almost negligible. The accuracy of these models is assessed by comparison with experimental data from free running tests. The main features of the flow field, with particular attention to the vortical structures detached from the hull are presented as well.