Results: We use Factor Analysis coupled with pre-established knowledge as a theoretical base to achieve this goal. Our intention is to identify structures that contain information from both mRNAs and miRNAs, and that can explain the complexity of the data. Despite the small sample available, we can show that this approach permits identification of meaningful structures, in particular two polycistronic miRNA genes related to transcriptional activity and likely to be relevant in the discrimination between gliosarcomas and other brain tumors. Background: Advances in biotechnology offer a fast growing variety of high-throughput data for screening molecular activities of genomic, transcriptional, post-transcriptional and translational observations. However, to date, most computational and algorithmic efforts have been directed at mining data from each of these molecular levels (genomic, transcriptional, etc.) separately. In view of the rapid advances in technology (new generation sequencing, high-throughput proteomics) it is important to address the problem of analyzing these data as a whole, i.e. preserving the emergent properties that appear in the cellular system when all molecular levels are interacting. We analyzed one of the (currently) few datasets that provide both transcriptional and post-transcriptional data of the same samples to investigate the possibility to extract more information, using a joint analysis approach.

Community structures are found to exist ubiquitously in a number of systems conveniently represented as complex networks. Partitioning networks into communities is thus important and crucial to both capture and simplify these systems' complexity. The prevalent and standard approach to meet this goal is related to the maximization of a quality function, modularity, which measures the goodness of a partition of a network into communities. However, it has recently been found that modularity maximization suffers from a resolution limit, which prevents its effectiveness and range of applications. Even when neglecting the resolution limit, methods designed for detecting communities in undirected networks cannot always be easily extended, and even less directly applied, to directed networks (for which specifically designed community detection methods are very limited). Furthermore, real-world networks are frequently found to possess hierarchical structure and the problem of revealing such type of structure is far from being addressed. In this paper, we propose a scheme that partitions networks into communities by electing community leaders via message passing between nodes. Using random walk on networks, this scheme derives an effective similarity measure between nodes, which is closely related to community memberships of nodes. Importantly, this approach can be applied to a very broad range of networks types. In fact, the successful validation of the proposed scheme on real and synthetic networks shows that this approach can effectively (i) address the problem of resolution limit and (ii) find communities in both directed and undirected networks within a unified framework, including revealing multiple levels of robust community partitions.

Results: The algorithm was first validated on synthetic and real benchmarks. It was then applied to the reconstruction of the core of the amino acids metabolism in Bifidobacterium longum, an essential, yet poorly known player in the human gut intestinal microbiome, known to be related to the onset of important diseases, such as metabolic syndromes. Our results show how computational approaches can offer effective tools for applications with the identification of potential new biological information. Motivation: The reliable and reproducible identification of gene interaction networks represents one of the grand challenges of both modern molecular biology and computational sciences. Approaches based on careful collection of literature data and network topological analysis, applied to unicellular organisms, have proven to offer results applicable to medical therapies. However, when little a priori knowledge is available, other approaches, not relying so strongly on previous literature, must be used. We propose here a novel algorithm ( based on ordinary differential equations) able to infer the interactions occurring among genes, starting from gene expression steady state data.

This new book examines the latest research in the synthetic biology which refers to both: the design and fabrication of biological components and systems that do not already exist in the natural world the re-design and fabrication of existing biological systems. It also deals with Integrative biology is the study and research of biological systems. It does not simply involve one discipline, but integrates a wide variety of disciplines that work together to find answers to scientific questions.

Different iterative schemes based on collocation methods have been well studied and widely applied to the numerical solution of nonlinear hypersingular integral equations (Capobianco et al. 2005). In this paper we apply Newton's method and its modified version to solve the equations obtained by applying a collocation method to a nonlinear hypersingular integral equation of Prandtl's type. The corresponding convergence results are derived in suitable Sobolev spaces. Some numerical tests are also presented to validate the theoretical results.

Background: In a systems biology perspective, protein-protein interactions (PPI) are encoded in machine-readable formats to avoid issues encountered in their retrieval for the reconstruction of comprehensive interaction maps and biological pathways. However, the information stored in electronic formats currently used doesn't allow a valid automatic reconstruction of biological pathways.Results: We propose a logical model of PPI that takes into account the "state" of proteins before and after the interaction. This information is necessary for proper reconstruction of the pathway.Conclusions: The adoption of the proposed model, which can be easily integrated into existing machine-readable formats used to store the PPI data, would facilitate the automatic or semi-automated reconstruction of biological pathways.Reviewers: This article was reviewed by Dr. Wen-Yu Chung (nominated by Kateryna Makova), Dr. Carl Herrmann (nominated by Dr. Purificación López-García) and Dr. Arcady Mushegian. © 2010 Termanini et al; licensee BioMed Central Ltd.

Recently, the network paradigm, an application of graph theory to biology, has proven to be a powerful approach to gaining insights into biological complexity, and has catalyzed the advancement of systems biology. In this perspective and focusing on the immune system, we propose here a more comprehensive view to go beyond the concept of network. We start from the concept of degeneracy, one of the most prominent characteristic of biological complexity, defined as the ability of structurally different elements to perform the same function, and we show that degeneracy is highly intertwined with another recently-proposed organizational principle, i.e. 'bow tie architecture'. The simultaneous consideration of concepts such as degeneracy, bow tie architecture and network results in a powerful new interpretative tool that takes into account the constructive role of noise (stochastic fluctuations) and is able to grasp the major characteristics of biological complexity, i.e. the capacity to turn an apparently chaotic and highly dynamic set of signals into functional information. © 2010 Tieri et al; licensee BioMed Central Ltd.

Human aging and longevity are complex and multi-factorial traits that result from a combination of environmental, genetic, epigenetic and stochastic factors, each contributing to the overall phenotype. The multi-factorial process of aging acts at different levels of complexity, from molecule to cell, from organ to organ systems and finally to organism, giving rise to the dynamic "aging mosaic". At present, an increasing amount of experimental data on genetics, genomics, proteomics and other -omics are available thanks to new highthroughput technologies but a comprehensive model for the study of human aging and longevity is still lacking. Systems biology represents a strategy to integrate and quantify the existing knowledge from different sources into predictive models, to be later tested and then implemented with new experimental data for validation and refinement in a recursive process. The ultimate goal is to compact the new acquired knowledge into a single picture, ideally able to characterize the phenotype at systemic/organism level. In this review we will briefly discuss the aging phenotype in a systems biology perspective, showing four specific examples at different levels of complexity, from a systemic process (inflammation) to a cascade-process pathways (coagulation) and from cellular organelle (proteasome) to single gene-network (PON-1), which could also represent targets for anti-aging strategies. © 2010 Bentham Science Publishers Ltd.

We investigate the switching dynamics of multistable nematic liquid crystal devices. In particular, we identify a remarkably simple two-dimensional device which exploits hybrid alignment at the surfaces to yield a bistable response. We also consider a three-dimensional tristable nematic device with patterned anchoring, recently implemented in practice, and discuss how the director and disclination patterns change during switching.

We consider the Dirichlet eigenvalue problem, (the eigenfunction) and ? > 0 (the eigen value), ? is an arbitrary domain in RN with finite measure, 1 < p < ?, 1 < q < p*, p* = Np/(N - p) if 1 < p < N and p* = ? if p >= N. We study several existence and uniqueness results as well as some properties of the solutions. Moreover, we indicate how to extend to the general case some proofs known in the classical case p = q. © 2010 Texas State University - San Marcos.

It is observed in vitro and in vivo that when two populations of different types of cells come near to each other, the rate of proliferation of most cells decreases. This phenomenon is often called contact inhibition of growth between two cells. In this paper, we consider a simplified 1-dimensional PDE-model for normal and abnormal cells, motivated by a paper of Chaplain, Graziano and Preziosi. We show that if the two populations are initially segregated, then they remain segregated due to the contact inhibition mechanism. In this case the system of PDE's can be formulated as a free boundary problem.

Many real life problems can he reduced to the solution of a complex exponentials approximation problem which is usually ill-posed. Recently a new transform for solving this problem, formulated as a specific moments problem in the plane, has been proposed in a theoretical framework. In this work some computational issues are addressed to make this new tool useful in practice. An algorithm is developed and used to solve a Nuclear Magnetic Resonance spectrometry problem, two time series interpolation and extrapolation problems and a shape from moments problem.

A spinning particle in the Schwarzschild spacetime deviates from geodesic behavior because of its spin. A spinless particle also deviates from geodesic behavior when a test radiation field is superimposed on the Schwarzschild background: in fact the interaction with the radiation field, i.e., the absorption and re-emission of radiation, leads to a friction-like drag force responsible for the well known effect which exists already in Newtonian gravity, the Poynting-Robertson effect. Here the Poynting-Robertson effect is extended to the case of spinning particles by modifying the Mathisson-Papapetrou model describing the motion of spinning test particles to account for the contribution of the radiation force. The resulting equations are numerically integrated and some typical orbits are shown in comparison with the spinless case. Furthermore, the interplay between spin and radiation forces is discussed by analyzing the deviation from circular geodesic motion on the equatorial plane when the contribution due to the radiation can also be treated as a small perturbation. Finally the estimate of the amount of radial variation from the geodesic radius is shown to be measurable in principle.

Most physical phenomena are described by time-dependent Hamiltonian systems with qualitative features that should be preserved by numerical integrators used for approximating their dynamics. The initial energy of the system together with the energy added or subtracted by the outside forces, represent a conserved quantity of the motion. For a class of time-dependent Hamiltonian systems [8] this invariant can be defined by means of an auxiliary function whose dynamics has to be integrated simultaneously with the system's equations. We propose splitting procedures featured by a SB3A property that allows to construct composition methods with a reduced number of determining order equations and to provide the same high accuracy for both the dynamics and the preservation of the invariant quantity.