A generic algorithm for layout of biological networks

BackgroundBiological networks are widely used to represent processes in biological systems and to capture interactions and dependencies between biological entities. Their size and complexity is steadily increasing due to the ongoing growth of knowledge in the life sciences. To aid understanding of biological networks several algorithms for laying out and graphically representing networks and network analysis results have been developed. However, current algorithms are specialized to particular layout styles and therefore different algorithms are required for each kind of network and/or style of layout. This increases implementation effort and means that new algorithms must be developed for new layout styles. Furthermore, additional effort is necessary to compose different layout conventions in the same diagram. Also the user cannot usually customize the placement of nodes to tailor the layout to their particular need or task and there is little support for interactive network exploration.ResultsWe present a novel algorithm to visualize different biological networks and network analysis results in meaningful ways depending on network types and analysis outcome. Our method is based on constrained graph layout and we demonstrate how it can handle the drawing conventions used in biological networks.ConclusionThe presented algorithm offers the ability to produce many of the fundamental popular drawing styles while allowing the exibility of constraints to further tailor these layouts.publishe

In situ guided tissue regeneration in musculoskeletal diseases and aging: Implementing pathology into tailored tissue engineering strategies

In situ guided tissue regeneration, also addressed as in situ tissue engineering or endogenous regeneration, has a great potential for population-wide “minimal invasive” applications. During the last two decades, tissue engineering has been developed with remarkable in vitro and preclinical success but still the number of applications in clinical routine is extremely small. Moreover, the vision of population-wide applications of ex vivo tissue engineered constructs based on cells, growth and differentiation factors and scaffolds, must probably be deemed unrealistic for economic and regulation-related issues. Hence, the progress made in this respect will be mostly applicable to a fraction of post-traumatic or post-surgery situations such as big tissue defects due to tumor manifestation. Minimally invasive procedures would probably qualify for a broader application and ideally would only require off the shelf standardized products without cells. Such products should mimic the microenvironment of regenerating tissues and make use of the endogenous tissue regeneration capacities. Functionally, the chemotaxis of regenerative cells, their amplification as a transient amplifying pool and their concerted differentiation and remodeling should be addressed. This is especially important because the main target populations for such applications are the elderly and diseased. The quality of regenerative cells is impaired in such organisms and high levels of inhibitors also interfere with regeneration and healing. In metabolic bone diseases like osteoporosis, it is already known that antagonists for inhibitors such as activin and sclerostin enhance bone formation. Implementing such strategies into applications for in situ guided tissue regeneration should greatly enhance the efficacy of tailored procedures in the future

Modularization of biochemical networks based on classification of Petri net t-invariants

<p>Abstract</p> <p>Background</p> <p>Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior.</p> <p>With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system.</p> <p>Methods</p> <p>Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied.</p> <p>Results</p> <p>We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in <it>Saccharomyces cerevisiae</it>) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability.</p> <p>Conclusion</p> <p>We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis.</p

Bioactive Electrospun Scaffolds Delivering Growth Factors and Genes for Tissue Engineering Applications

A biomaterial scaffold is one of the key factors for successful tissue engineering. In recent years, an increasing tendency has been observed toward the combination of scaffolds and biomolecules, e.g. growth factors and therapeutic genes, to achieve bioactive scaffolds, which not only provide physical support but also express biological signals to modulate tissue regeneration. Huge efforts have been made on the exploration of strategies to prepare bioactive scaffolds. Within the past five years, electrospun scaffolds have gained an exponentially increasing popularity in this area because of their ultrathin fiber diameter and large surface-volume ratio, which is favored for biomolecule delivery. This paper reviews current techniques that can be used to prepare bioactive electrospun scaffolds, including physical adsorption, blend electrospinning, coaxial electrospinning, and covalent immobilization. In addition, this paper also analyzes the existing challenges (i.e., protein instability, low gene transfection efficiency, and difficulties in accurate kinetics prediction) to achieve biomolecule release from electrospun scaffolds, which necessitate further research to fully exploit the biomedical applications of these bioactive scaffolds

Zea mays iRS1563: A Comprehensive Genome-Scale Metabolic Reconstruction of Maize Metabolism

The scope and breadth of genome-scale metabolic reconstructions have continued to expand over the last decade. Herein, we introduce a genome-scale model for a plant with direct applications to food and bioenergy production (i.e., maize). Maize annotation is still underway, which introduces significant challenges in the association of metabolic functions to genes. The developed model is designed to meet rigorous standards on gene-protein-reaction (GPR) associations, elementally and charged balanced reactions and a biomass reaction abstracting the relative contribution of all biomass constituents. The metabolic network contains 1,563 genes and 1,825 metabolites involved in 1,985 reactions from primary and secondary maize metabolism. For approximately 42% of the reactions direct literature evidence for the participation of the reaction in maize was found. As many as 445 reactions and 369 metabolites are unique to the maize model compared to the AraGEM model for A. thaliana. 674 metabolites and 893 reactions are present in Zea mays iRS1563 that are not accounted for in maize C4GEM. All reactions are elementally and charged balanced and localized into six different compartments (i.e., cytoplasm, mitochondrion, plastid, peroxisome, vacuole and extracellular). GPR associations are also established based on the functional annotation information and homology prediction accounting for monofunctional, multifunctional and multimeric proteins, isozymes and protein complexes. We describe results from performing flux balance analysis under different physiological conditions, (i.e., photosynthesis, photorespiration and respiration) of a C4 plant and also explore model predictions against experimental observations for two naturally occurring mutants (i.e., bm1 and bm3). The developed model corresponds to the largest and more complete to-date effort at cataloguing metabolism for a plant species

Group-based worry intervention for persecutory delusions: an initial feasibility study

Background: A one-to-one cognitive behavioural therapy intervention targeting worry significantly reduces both worry and persecutory delusions (Freeman et al., 2015). Aim: To adapt this intervention for group delivery and conduct a feasibility trial within routine clinical practice. Method: Thirteen participants were randomized to a weekly 8-session worry intervention group (n = 7) or wait-list control (n = 6). Results: All but one participant completed measures at all time points. Participants attended an average of six therapy sessions. Conclusions: Recruitment, retention and therapy uptake were feasible. Observed treatment effects were in the expected direction, but may be diluted compared with one-to-one interventions