Combined Ring–Mesh Optical Transport Networks

To date in Sonet and DWDM transport environments, we see two dominant principles for network survivability: ring and mesh. Although both are used, they are virtually always deployed and operated on geographically separate networks or regions, or applied on a ring-access and mesh-core principle. We describe a new approach for optimized design of ring and mesh network components in a single hybrid transport network. A key concept in design of these hybrids is the clipping of forcer spans in the mesh network design by strategically added rings. We present a formal optimization model and a heuristic to form sych hybrid designs with lower total cost than a pure-mesh design. These ideas on optical network architecture are especially relevant to industry practice with the advent of OXC systems with integrated add–drop multiplexer functionality and by the development of ultra long reach (ULR) optics. This integration of OXC-OADM functionality effectively removes any extra costs associated with transitions from a ring to mesh environment and ULR optics permit all-optical bypass of OXC nodes which enhances the cost-effectiveness of the hybrid architectures.     

Infections on Temporal Networks—A Matrix-Based Approach

We extend the concept of accessibility in temporal networks to model infections with a finite infectious period such as the susceptible-infected-recovered (SIR) model. This approach is entirely based on elementary matrix operations and unifies the disease and network dynamics within one algebraic framework. We demonstrate the potential of this formalism for three examples of networks with high temporal resolution: networks of social contacts, sexual contacts, and livestock-trade. Our investigations provide a new methodological framework that can be used, for instance, to estimate the epidemic threshold, a quantity that determines disease parameters, for which a large-scale outbreak can be expected.     

Protein Interaction Networks—More Than Mere Modules

It is widely believed that the modular organization of cellular function is reflected in a modular structure of molecular networks. A common view is that a “module” in a network is a cohesively linked group of nodes, densely connected internally and sparsely interacting with the rest of the network. Many algorithms try to identify functional modules in protein-interaction networks (PIN) by searching for such cohesive groups of proteins. Here, we present an alternative approach independent of any prior definition of what actually constitutes a “module”. In a self-consistent manner, proteins are grouped into “functional roles” if they interact in similar ways with other proteins according to their functional roles. Such grouping may well result in cohesive modules again, but only if the network structure actually supports this. We applied our method to the PIN from the Human Protein Reference Database (HPRD) and found that a representation of the network in terms of cohesive modules, at least on a global scale, does not optimally represent the network''s structure because it focuses on finding independent groups of proteins. In contrast, a decomposition into functional roles is able to depict the structure much better as it also takes into account the interdependencies between roles and even allows groupings based on the absence of interactions between proteins in the same functional role. This, for example, is the case for transmembrane proteins, which could never be recognized as a cohesive group of nodes in a PIN. When mapping experimental methods onto the groups, we identified profound differences in the coverage suggesting that our method is able to capture experimental bias in the data, too. For example yeast-two-hybrid data were highly overrepresented in one particular group. Thus, there is more structure in protein-interaction networks than cohesive modules alone and we believe this finding can significantly improve automated function prediction algorithms.     

Restricted Trees: Simplifying Networks with Bottlenecks

Suppose N is a phylogenetic network indicating a complicated relationship among individuals and taxa. Often of interest is a much simpler network, for example, a species tree T, that summarizes the most fundamental relationships. The meaning of a species tree is made more complicated by the recent discovery of the importance of hybridizations and lateral gene transfers. Hence, it is desirable to describe uniform well-defined procedures that yield a tree given a network N.     

Vision First? The Development of Primary Visual Cortical Networks Is More Rapid Than the Development of Primary Motor Networks in Humans

The development of cortical functions and the capacity of the mature brain to learn are largely determined by the establishment and maintenance of neocortical networks. Here we address the human development of long-range connectivity in primary visual and motor cortices, using well-established behavioral measures--a Contour Integration test and a Finger-tapping task--that have been shown to be related to these specific primary areas, and the long-range neural connectivity within those. Possible confounding factors, such as different task requirements (complexity, cognitive load) are eliminated by using these tasks in a learning paradigm. We find that there is a temporal lag between the developmental timing of primary sensory vs. motor areas with an advantage of visual development; we also confirm that human development is very slow in both cases, and that there is a retained capacity for practice induced plastic changes in adults. This pattern of results seems to point to human-specific development of the "canonical circuits" of primary sensory and motor cortices, probably reflecting the ecological requirements of human life.     

What Can Causal Networks Tell Us about Metabolic Pathways?

Graphical models describe the linear correlation structure of data and have been used to establish causal relationships among phenotypes in genetic mapping populations. Data are typically collected at a single point in time. Biological processes on the other hand are often non-linear and display time varying dynamics. The extent to which graphical models can recapitulate the architecture of an underlying biological processes is not well understood. We consider metabolic networks with known stoichiometry to address the fundamental question: “What can causal networks tell us about metabolic pathways?”. Using data from an Arabidopsis BaySha population and simulated data from dynamic models of pathway motifs, we assess our ability to reconstruct metabolic pathways using graphical models. Our results highlight the necessity of non-genetic residual biological variation for reliable inference. Recovery of the ordering within a pathway is possible, but should not be expected. Causal inference is sensitive to subtle patterns in the correlation structure that may be driven by a variety of factors, which may not emphasize the substrate-product relationship. We illustrate the effects of metabolic pathway architecture, epistasis and stochastic variation on correlation structure and graphical model-derived networks. We conclude that graphical models should be interpreted cautiously, especially if the implied causal relationships are to be used in the design of intervention strategies.     

Theoretical Analysis of Time-to-Peak Responses in Biological Reaction Networks

Processing of information by signaling networks is characterized by properties of the induced kinetics of the activated pathway components. The maximal extent of pathway activation (maximum amplitude) and the time-to-peak-response (position) are key determinants of biological responses that have been linked to specific outcomes. We investigate how the maximum amplitude of pathway activation and its position depend on the input and wiring of a signaling network. For this purpose, we consider a simple reaction A→B that is regulated by a transient input and extended this to include back-reaction and additional partners. In particular, we show that a unique maximum of B(t) exists. Moreover, we prove that the position of the maximum is independent of the applied input but regulated by degradation reactions of B. Indeed, the time-to-peak-response decreases with increasing degradation rate, which we prove for small models and show in simulations for more complex ones. The identified dependencies provide insights into design principles that facilitate the realization dynamical characteristics like constant position of maximal pathway activation and thereby guide the characterization of unknown kinetics within larger protein networks.     

Glioma Cell Death: Cell–Cell Interactions and Signalling Networks

The prognosis for patients with malignant gliomas is poor, but improvements may emerge from a better understanding of the pathophysiology of glioma signalling. Recent therapeutic developments have implicated lipid signalling in glioma cell death. Stress signalling in glioma cell death involves mitochondria and endoplasmic reticulum. Lipid mediators also signal via extrinsic pathways in glioma cell proliferation, migration and interaction with endothelial and microglial cells. Glioma cell death and tumour regression have been reported using polyunsaturated fatty acids in animal models, human ex vivo explants, glioma cell preparations and in clinical case reports involving intratumoral infusion. Cell death signalling was associated with generation of reactive oxygen intermediates and mitochondrial and other signalling pathways. In this review, evidence for mitochondrial responses to stress signals, including polyunsaturated fatty acids, peroxidising agents and calcium is presented. Additionally, evidence for interaction of glioma cells with primary brain endothelial cells is described, modulating human glioma peroxidative signalling. Glioma responses to potential therapeutic agents should be analysed in systems reflecting tumour connectivity and CNS structural and functional integrity. Future insights may also be derived from studies of signalling in glioma-derived tumour stem cells.     

EEG-Based Functional Brain Networks: Does the Network Size Matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.     

Tropomodulin Protects α-Catenin-Dependent Junctional-Actin Networks under Stress during Epithelial Morphogenesis

α-catenin is central to recruitment of actin networks to the cadherin-catenin complex [1, 2], but how such networks are subsequently stabilized against stress applied during morphogenesis is poorly understood. To identify proteins that functionally interact with α-catenin in this process, we performed enhancer screening using a weak allele of the C.?elegans α-catenin, hmp-1, thereby identifying UNC-94/tropomodulin. Tropomodulins (Tmods) cap the minus ends of F-actin in sarcomeres [3]. They also regulate lamellipodia [4], can promote actin nucleation [5], and are required for normal cardiovascular development [6, 7] and neuronal growth-cone morphology [8]. Tmods regulate the morphology of cultured epithelial cells [9], but their role in epithelia in?vivo remains unexplored. We find that UNC-94 is?enriched within a HMP-1-dependent junctional-actin network at epidermal adherens junctions subject to stress during morphogenesis. Loss of UNC-94 leads to discontinuity of this network, and high-speed filming of hmp-1(fe4);unc-94(RNAi) embryos reveals large junctional displacements that depend on the Rho pathway. In?vitro, UNC-94 acts in combination with HMP-1, leading to longer actin bundles than with HMP-1 alone. Our data suggest that Tmods protect actin filaments recruited by α-catenin from minus-end subunit loss, enabling them to withstand the stresses of morphogenesis.     

Do Brain Networks Evolve by Maximizing Their Information Flow Capacity?

We propose a working hypothesis supported by numerical simulations that brain networks evolve based on the principle of the maximization of their internal information flow capacity. We find that synchronous behavior and capacity of information flow of the evolved networks reproduce well the same behaviors observed in the brain dynamical networks of Caenorhabditis elegans and humans, networks of Hindmarsh-Rose neurons with graphs given by these brain networks. We make a strong case to verify our hypothesis by showing that the neural networks with the closest graph distance to the brain networks of Caenorhabditis elegans and humans are the Hindmarsh-Rose neural networks evolved with coupling strengths that maximize information flow capacity. Surprisingly, we find that global neural synchronization levels decrease during brain evolution, reflecting on an underlying global no Hebbian-like evolution process, which is driven by no Hebbian-like learning behaviors for some of the clusters during evolution, and Hebbian-like learning rules for clusters where neurons increase their synchronization.     

Do Food Web Models Reproduce the Structure of Mutualistic Networks?

Background

Simple models inspired by processes shaping consumer-resource interactions have helped to establish the primary processes underlying the organization of food webs, networks of trophic interactions among species. Because other ecological interactions such as mutualisms between plants and their pollinators and seed dispersers are inherently based in consumer-resource relationships we hypothesize that processes shaping food webs should organize mutualistic relationships as well.

Methodology/Principal Findings

We used a likelihood-based model selection approach to compare the performance of food web models and that of a model designed for mutualisms, in reproducing the structure of networks depicting mutualistic relationships. Our results show that these food web models are able to reproduce the structure of most of the mutualistic networks and even the simplest among the food web models, the cascade model, often reproduce overall structural properties of real mutualistic networks.

Conclusions/Significance

Based on our results we hypothesize that processes leading to feeding hierarchy, which is a characteristic shared by all food web models, might be a fundamental aspect in the assembly of mutualisms. These findings suggest that similar underlying ecological processes might be important in organizing different types of interactions.     

How Difficult Is Inference of Mammalian Causal Gene Regulatory Networks?

Gene regulatory networks (GRNs) play a central role in systems biology, especially in the study of mammalian organ development. One key question remains largely unanswered: Is it possible to infer mammalian causal GRNs using observable gene co-expression patterns alone? We assembled two mouse GRN datasets (embryonic tooth and heart) and matching microarray gene expression profiles to systematically investigate the difficulties of mammalian causal GRN inference. The GRNs were assembled based on pieces of experimental genetic perturbation evidence from manually reading primary research articles. Each piece of perturbation evidence records the qualitative change of the expression of one gene following knock-down or over-expression of another gene. Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect), which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges. Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult. Nonetheless, if we have expression profiling data from genetic or molecular perturbation experiments, such as gene knock-out or signalling stimulation, it is possible to use the set of differentially expressed genes to recover causal regulatory relationships with good sensitivity and specificity. Our result supports the importance of using perturbation experimental data in causal network reconstruction. Furthermore, we showed that causal gene regulatory relationship can be highly cell type or developmental stage specific, suggesting the importance of employing expression profiles from homogeneous cell populations. This study provides essential datasets and empirical evidence to guide the development of new GRN inference methods for mammalian organ development.     

α-Actinin and Filamin Cooperatively Enhance the Stiffness of Actin Filament Networks

Background

The close subcellular proximity of different actin filament crosslinking proteins suggests that these proteins may cooperate to organize F-actin structures to drive complex cellular functions during cell adhesion, motility and division. Here we hypothesize that α-actinin and filamin, two major F-actin crosslinking proteins that are both present in the lamella of adherent cells, display synergistic mechanical functions.

Methodology/Principal Findings

Using quantitative rheology, we find that combining α-actinin and filamin is much more effective at producing elastic, solid-like actin filament networks than α-actinin and filamin separately. Moreover, F-actin networks assembled in the presence of α-actinin and filamin strain-harden more readily than networks in the presence of either α-actinin or filamin.

Significance

These results suggest that cells combine auxiliary proteins with similar ability to crosslink filaments to generate stiff cytoskeletal structures, which are required for the production of internal propulsive forces for cell migration, and that these proteins do not have redundant mechanical functions.