About BIONIS Network

The BIONIS network was set up in spring 2002 by Prof. George Jeronimidis, Prof. Julian Vincent and Phil Sheppard. Membership is now over 300, with members from academia and industry in more than 40 countries. The mission of the network is to promote the application of Biomimetics in products and services and its use in education and training. It is currently supported by Swedish Biomimetics 3000 and hosted by the University of Reading.[第一段]     

Network Entrez分子信息检索系统

本文介绍了在人类基因组计划,分子进化学研究等生命科学前沿领域中广泛应用于的一种Ｉｎｔｅｒｎｅｔ上的数据库软系统：ＮｅｔｗｏｒｋＥｎｔｒｅｚ分子信息检索系统。     

Call Characteristic Network Reveal Geographical Patterns of Call Similarity:Applying Network Analysis to Frog’s Call Research

Individual’s phenotypic traits are the results of adaptation to ecological conditions.Therefore,different selection pressures caused by heterogeneous environments may result in phenotypic difference,especially for individuals in different geographical populations.Here,we illustrated for the first time to use social network analysis(SNA)for examining whether geographical proximity predicts the similarity patterns in call characteristics among populations of an anuran species.We recorded calls from 150 male dorsal-striped opposite-fingered treefrogs(Chiromantis doriae)at 11 populations in Hainan Province and one population in Guangdong Province in China's Mainland,and we measured eight acoustic variables for each male.Mantel test didn’t show a correlation between geographical proximity and the similarity in call characteristics among populations.In addition,we failed to find correlations between a population’s eigenvector centrality and the distance to its nearest neighbor,nor between the coefficient of variation of similarity in call characteristics of a population and the average distance to all other populations.Nevertheless,three acoustic clusters were identified by the Girvan-Newman algorithm,and clustering was partially associated with geography.Furthermore,the most central populations were included in the same cluster,but the top betweenness populations were located within different clusters,suggesting that centrality populations are not necessary bridging between clusters.These results demonstrate the potential usefulness of the SNA toolbox and indicate that SNA helps to uncover the patterns that often overlooked in other analytical methods.By using SNA in frog’s call studies,researchers could further uncover the potential relationship in call characteristics between geographical populations,further reveal the effects of ecological factors on call characteristics,and probably enhance our understanding of the adaptive evolution of acoustic signals.     

Pain: A Distributed Brain Information Network?

Understanding how pain is processed in the brain has been an enduring puzzle, because there doesn''t appear to be a single “pain cortex” that directly codes the subjective perception of pain. An emerging concept is that, instead, pain might emerge from the coordinated activity of an integrated brain network. In support of this view, Woo and colleagues present evidence that distinct brain networks support the subjective changes in pain that result from nociceptive input and self-directed cognitive modulation. This evidence for the sensitivity of distinct neural subsystems to different aspects of pain opens up the way to more formal computational network theories of pain.On the surface, pain should have been one of the easier brain systems to understand. Its fundamental importance in organism defence means that its anatomy should be well conserved across species, unlike systems for language, for instance. And its relatively simple scalar signal (from less pain to more pain) should not require extensive computational processing, unlike sound or vision. However, since Penfield''s failure to convincingly locate a “pain cortex” during his classic awake brain stimulation studies in the 1950s [1], trying to piece together the pain system in the brain has been a story of frustration and debate.     

Special Issue on “Gene Regulatory Network”

The journal Genomics Proteomics & Bioinformatics (GPB) is now inviting submissions for a special issue (to be published around Dec 2012) on the topic of "Gene Regulatory Network". The Gene Regulatory Network (GRN) is a collection of DNA sequences which interact with each other and other cellular components, thereby governing the rates at which genes in the network are transcribed to change the genomic activity. GRNs are evolutionally-conserved and play critical roles in various biological processes and attract more and more research interest.     

Is My Network Module Preserved and Reproducible?

In many applications, one is interested in determining which of the properties of a network module change across conditions. For example, to validate the existence of a module, it is desirable to show that it is reproducible (or preserved) in an independent test network. Here we study several types of network preservation statistics that do not require a module assignment in the test network. We distinguish network preservation statistics by the type of the underlying network. Some preservation statistics are defined for a general network (defined by an adjacency matrix) while others are only defined for a correlation network (constructed on the basis of pairwise correlations between numeric variables). Our applications show that the correlation structure facilitates the definition of particularly powerful module preservation statistics. We illustrate that evaluating module preservation is in general different from evaluating cluster preservation. We find that it is advantageous to aggregate multiple preservation statistics into summary preservation statistics. We illustrate the use of these methods in six gene co-expression network applications including 1) preservation of cholesterol biosynthesis pathway in mouse tissues, 2) comparison of human and chimpanzee brain networks, 3) preservation of selected KEGG pathways between human and chimpanzee brain networks, 4) sex differences in human cortical networks, 5) sex differences in mouse liver networks. While we find no evidence for sex specific modules in human cortical networks, we find that several human cortical modules are less preserved in chimpanzees. In particular, apoptosis genes are differentially co-expressed between humans and chimpanzees. Our simulation studies and applications show that module preservation statistics are useful for studying differences between the modular structure of networks. Data, R software and accompanying tutorials can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/ModulePreservation.     

Does Habitat Variability Really Promote Metabolic Network Modularity?

The hypothesis that variability in natural habitats promotes modular organization is widely accepted for cellular networks. However, results of some data analyses and theoretical studies have begun to cast doubt on the impact of habitat variability on modularity in metabolic networks. Therefore, we re-evaluated this hypothesis using statistical data analysis and current metabolic information. We were unable to conclude that an increase in modularity was the result of habitat variability. Although horizontal gene transfer was also considered because it may contribute for survival in a variety of environments, closely related to habitat variability, and is known to be positively correlated with network modularity, such a positive correlation was not concluded in the latest version of metabolic networks. Furthermore, we demonstrated that the previously observed increase in network modularity due to habitat variability and horizontal gene transfer was probably due to a lack of available data on metabolic reactions. Instead, we determined that modularity in metabolic networks is dependent on species growth conditions. These results may not entirely discount the impact of habitat variability and horizontal gene transfer. Rather, they highlight the need for a more suitable definition of habitat variability and a more careful examination of relationships of the network modularity with horizontal gene transfer, habitats, and environments.     

A Second-generation Protein–Protein Interaction Network of Helicobacter pylori

Helicobacter pylori infections cause gastric ulcers and play a major role in the development of gastric cancer. In 2001, the first protein interactome was published for this species, revealing over 1500 binary protein interactions resulting from 261 yeast two-hybrid screens. Here we roughly double the number of previously published interactions using an ORFeome-based, proteome-wide yeast two-hybrid screening strategy. We identified a total of 1515 protein–protein interactions, of which 1461 are new. The integration of all the interactions reported in H. pylori results in 3004 unique interactions that connect about 70% of its proteome. Excluding interactions of promiscuous proteins we derived from our new data a core network consisting of 908 interactions. We compared our data set to several other bacterial interactomes and experimentally benchmarked the conservation of interactions using 365 protein pairs (interologs) of E. coli of which one third turned out to be conserved in both species.Helicobacter pylori is a Gram-negative, microaerophilic bacterium that colonizes the stomach, an unusual highly acidic niche for microorganisms. In 1983, Warren and Marshall found it to be associated with gastric inflammation and duodenal ulcer disease (1, 2). A chronic infection with H. pylori can lead to development of stomach carcinoma and MALT lymphoma (reviewed in (3)). Hence, the World Health Organization has classified H. pylori as a class I carcinogen (4). It is estimated that half of the world′s population harbors H. pylori but with large variations in the geographical and socioeconomic distribution while causing annually 700,000 deaths worldwide (reviewed in (5)).The pathogenesis of H. pylori has been extensively studied, including the effector CagA, cytotoxin VacA, its adhesins and urease (reviewed in (3, 5–7)). The latter allows the bacterium to neutralize the stomach acid through ammonia production. However, H. pylori is not a classical model organism and thus many gaps in our knowledge still exist.The genome of H. pylori reference strain 26695 was completely sequenced in 1997 (8) and encodes 1587 proteins of which about 950 (61%) have been assigned functions (excluding “putatives”; Uniprot, CMR (9)). These numbers indicate that a large fraction of the proteins of H. pylori has not been functionally characterized.Protein–protein interactions (PPIs)¹ are required for nearly all biological processes. Unbiased interactomes are helpful to understand proteins or pathways and how they are linking poorly or uncharacterized proteins via their interactions. For instance, our study of the Treponema pallidum interactome (10) has led to the characterization of several previously “unknown” proteins such as YbeB, a ribosomal silencing factor (11), or TP0658, a regulator of flagellar translation and assembly (12, 13). However, only a few other comprehensive bacterial interactome studies have been published to date, including Campylobacter jejuni (14), Synechocystis sp. (15), Mycobacterium tuberculosis (16), Mesorhizobium loti (17), and recently Escherichia coli (18). In addition, partial interactomes are available for Bacillus subtilis (19) and H. pylori (20). Most of them used the yeast two-hybrid (Y2H) screening technology (21) which allows the pairwise detection of PPIs. Furthermore, a few other studies (22–25) systematically identified protein complexes and their compositions in bacteria.In 2001, Rain and colleagues have established a partial interactome of H. pylori, the first published protein interaction network of a bacterium (20). In this study, 261 bait constructs were screened against a random prey pool library resulting in the detection of over 1500 PPIs. Although this network likely represents a small fraction of all PPIs that occur in H. pylori, many downstream studies were motivated by these results (see below).Recent studies have disproved the notion that Y2H data sets are of poor quality (26, 27). Similarly, a high false-negative rate can be avoided by multiple Y2H expression vector systems (28–30) or protein fragments as opposed to full-length constructs (31). The aim of this study was to systematically screen the H. pylori proteome for binary protein interactions using a complementary approach to that of Rain et al. to produce an extended protein–protein interaction map of H. pylori. As a result, we have roughly doubled the number of known binary protein–protein interactions for H. pylori in this study.     

Trans-Golgi Network—An Intersection of Trafficking Cell Wall Components

The cell wall, a crucial cell compartment, is composed of a network of polysaccharides and proteins, providing structural support and protection from external stimuli. While the cell wall structure and biosynthesis have been extensively studied, very little is known about the transport of polysaccharides and other components into the developing cell wall. This review focuses on endomembrane trafficking pathways involved in cell wall deposition. Cellulose synthase complexes are assembled in the Golgi, and are transported in vesicles to the plasma membrane. Non-cellulosic polysaccharides are synthesized in the Golgi apparatus, whereas cellulose is produced by enzyme complexes at the plasma membrane. Polysaccharides and enzymes that are involved in cell wall modification and assembly are transported by distinct vesicle types to their destinations; however, the precise mechanisms involved in selection, sorting and delivery remain to be identified. The endomembrane system orchestrates the delivery of Golgi-derived and possibly endocytic vesicles carrying cell wall and cell membrane components to the newly-formed cell plate. However, the nature of these vesicles, their membrane compositions, and the timing of their delivery are largely unknown. Emerging technologies such as chemical genomics and proteomics are promising avenues to gain insight into the trafficking of cell wall components.     

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.     

Targeting the mTOR Signaling Network for Alzheimer’s Disease Therapy

The mammalian target of rapamycin (mTOR) is a highly conserved serine/threonine kinase that can sense environmental stimuli such as growth factors, energy state, and nutrients. It is essential for cell growth, proliferation, and metabolism, but dysregulation of mTOR signaling pathway is also associated with a number of human diseases. Encouraging data from experiments have provided sufficient evidence for the relationship between the mTOR signaling pathway and Alzheimer’s disease (AD). Upregulation of mTOR signaling pathway is thought to play an important role in major pathological processes of AD. The mTOR inhibitors such as rapamycin have been proven to ameliorate the AD-like pathology and cognitive deficits effectively in a broad range of animal models. Application of mTOR inhibitors indicates the potential value of reducing mTOR activity as an innovative therapeutic strategy for AD. In this review, we will focus on the recent process in understanding mTOR signaling pathway and the vital involvement of this signaling pathway in the pathology of AD, and discuss the application of mTOR inhibitors as potential therapeutic agents for the treatment of AD.     

Directed Network Motifs in Alzheimer’s Disease and Mild Cognitive Impairment

Directed network motifs are the building blocks of complex networks, such as human brain networks, and capture deep connectivity information that is not contained in standard network measures. In this paper we present the first application of directed network motifs in vivo to human brain networks, utilizing recently developed directed progression networks which are built upon rates of cortical thickness changes between brain regions. This is in contrast to previous studies which have relied on simulations and in vitro analysis of non-human brains. We show that frequencies of specific directed network motifs can be used to distinguish between patients with Alzheimer’s disease (AD) and normal control (NC) subjects. Especially interesting from a clinical standpoint, these motif frequencies can also distinguish between subjects with mild cognitive impairment who remained stable over three years (MCI) and those who converted to AD (CONV). Furthermore, we find that the entropy of the distribution of directed network motifs increased from MCI to CONV to AD, implying that the distribution of pathology is more structured in MCI but becomes less so as it progresses to CONV and further to AD. Thus, directed network motifs frequencies and distributional properties provide new insights into the progression of Alzheimer’s disease as well as new imaging markers for distinguishing between normal controls, stable mild cognitive impairment, MCI converters and Alzheimer’s disease.     

A Mechanism for Ultra-Slow Oscillations in the Cortical Default Network

When the brain is in its noncognitive “idling” state, functional MRI measurements reveal the activation of default cortical networks whose activity is suppressed during cognitive processing. This default or background mode is characterized by ultra-slow BOLD oscillations (∼0.05 Hz), signaling extremely slow cycling in cortical metabolic demand across distinct cortical regions. Here we describe a model of the cortex which predicts that slow cycling of cortical activity can arise naturally as a result of nonlinear interactions between temporal (Hopf) and spatial (Turing) instabilities. The Hopf instability is triggered by delays in the inhibitory postsynaptic response, while the Turing instability is precipitated by increases in the strength of the gap-junction coupling between interneurons. We comment on possible implications for slow dendritic computation and information processing.