[show abstract][hide abstract] ABSTRACT: Many complex networks demonstrate a phenomenon of striking degree
correlations, i.e., a node tends to link to other nodes with similar (or
dissimilar) degrees. From the perspective of degree correlations, this paper
attempts to characterize topological structures of urban street networks. We
adopted six urban street networks (three European and three North American),
and converted them into network topologies in which nodes and edges
respectively represent individual streets and street intersections, and
compared the network topologies to three reference network topologies
(biological, technological, and social). The urban street network topologies
(with the exception of Manhattan) showed a consistent pattern that distinctly
differs from the three reference networks. The topologies of urban street
networks lack striking degree correlations in general. Through reshuffling the
network topologies towards for example maximum or minimum degree correlations
while retaining the initial degree distributions, we found that all the
surrogate topologies of the urban street networks, as well as the reference
ones, tended to deviate from small world properties. This implies that the
initial degree correlations do not have any positive or negative effect on the
networks' performance or functions.
Keywords: Scale free, small world, rewiring, rich club effect, reshuffle, and
[show abstract][hide abstract] ABSTRACT: Huang-Lian-Jie-Du-Tang (HLJDT) is a classic TCM formula to clear "heat" and "poison" that exhibits antirheumatic activity. Here we investigated the therapeutic mechanisms of HLJDT at protein network level using bioinformatics approach. It was found that HLJDT shares 5 target proteins with 3 types of anti-RA drugs, and several pathways in immune system and bone formation are significantly regulated by HLJDT's components, suggesting the therapeutic effect of HLJDT on RA. By defining an antirheumatic effect score to quantitatively measure the therapeutic effect, we found that the score of each HLJDT's component is very low, while the whole HLJDT achieves a much higher effect score, suggesting a synergistic effect of HLJDT achieved by its multiple components acting on multiple targets. At last, topological analysis on the RA-associated PPI network was conducted to illustrate key roles of HLJDT's target proteins on this network. Integrating our findings with TCM theory suggests that HLJDT targets on hub nodes and main pathway in the Hot ZENG network, and thus it could be applied as adjuvant treatment for Hot-ZENG-related RA. This study may facilitate our understanding of antirheumatic effect of HLJDT and it may suggest new approach for the study of TCM pharmacology.
Evidence-based Complementary and Alternative Medicine 01/2013; 2013:245357. · 1.72 Impact Factor
[show abstract][hide abstract] ABSTRACT: With the growth of aging population all over the world, a rising incidence of Alzheimer's disease (AD) has been recently observed. In contrast to FDA-approved western drugs, herbal medicines, featured as abundant ingredients and multi-targeting, have been acknowledged with notable anti-AD effects although the mechanism of action (MOA) is unknown. Investigating the possible MOA for these herbs can not only refresh but also extend the current knowledge of AD pathogenesis. In this study, clinically tested anti-AD herbs, their ingredients as well as their corresponding target proteins were systematically reviewed together with applicable bioinformatics resources and methodologies. Based on above information and resources, we present a systematically target network analysis framework to explore the mechanism of anti-AD herb ingredients. Our results indicated that, in addition to the binding of those symptom-relieving targets as the FDA-approved drugs usually do, ingredients of anti-AD herbs also interact closely with a variety of successful therapeutic targets related to other diseases, such as inflammation, cancer and diabetes, suggesting the possible cross-talks between these complicated diseases. Furthermore, pathways of Ca(2+) equilibrium maintaining upstream of cell proliferation and inflammation were densely targeted by the anti-AD herbal ingredients with rigorous statistic evaluation. In addition to the holistic understanding of the pathogenesis of AD, the integrated network analysis on the MOA of herbal ingredients may also suggest new clues for the future disease modifying strategies.
Briefings in Bioinformatics 08/2012; · 5.30 Impact Factor
[show abstract][hide abstract] ABSTRACT: Astragaloside IV (AGS-IV) is a main active ingredient of Astragalus membranaceus Bunge, a medicinal herb used for cardiovascular diseases (CVD). In this work, we investigated the therapeutic mechanisms of AGS-IV at a network level by computer-assisted target identification with the in silico inverse docking program (INVDOCK). Targets included in the analysis covered all signaling pathways thought to be implicated in the therapeutic actions of all CVD drugs approved by US FDA. A total of 39 putative targets were identified. Three of these targets, calcineurin (CN), angiotensin-converting enzyme (ACE), and c-Jun N-terminal kinase (JNK), were experimentally validated at a molecular level. Protective effects of AGS-IV were also compared with the CN inhibitor cyclosporin A (CsA) in cultured cardiomyocytes exposed to adriamycin. Network analysis of protein-protein interactions (PPI) was carried out with reference to the therapeutic profiles of approved CVD drugs. The results suggested that the therapeutic effects of AGS-IV are based upon a combination of blocking calcium influx, vasodilation, anti-thrombosis, anti-oxidation, anti-inflammation and immune regulation.
PLoS ONE 01/2012; 7(9):e44938. · 3.73 Impact Factor
[show abstract][hide abstract] ABSTRACT: Huang-Lian-Jie-Du-Tang (HLJDT) is a traditional Chinese medicine with anti-inflammatory use. In the present study, the effects of its component herbs and pure components were observed on eicosanoid generation to find out the contributory components and their precise targets on arachidonic acid (AA) cascade.
By monitoring leukotriene B(4) (LTB(4)), 5-hydroxyeicosatetraenoic acid (5-HETE), and 12-hydroxy-5,8,10-heptadecatrienoic acid (12-HHT), we compared the effects of HLJDT, HLJDT free of one or two component herbs, and water extract of four single component herbs of HLJDT (Rhizoma coptidis, Radix scutellariae, Cortex phellodendri and Fructus gardeniae) on eicosanoid generation in rat elicited peritoneal macrophages. In addition, thirteen pure compounds from HLJDT (baicalin, baicalein, wogonoside, wogonin, berberine, magnoflorine, phellodendrine, coptisine, palmatine, jateorrhizine, crocin, chlorogenic acid, and geniposide) were tested in the macrophages. Furthermore, the efficacies of these thirteen compounds were evaluated on cell-free purified enzymes: leukotriene A(4) hydrolase (LTA(4)H), 5-, 15-lipoxygenase (5-, 15-LO), and cyclo-oxygenase-1/2 (COX-1/2). Moreover, the possible synergetic effect on LO pathway derived LTB(4) generation between the active components was also tested in rat peritoneal macrophages.
Our experiments showed that Rhizoma coptidis and Radix scutellariae were responsible for the suppressive effect of HLJDT on eicosanoid generation. Some of the pure components including baicalein, baicalin, wogonoside, wogonin, coptisine, and magnoflorine inhibited eicosanoid generation in rat macrophages via LO pathway of AA cascade. Further experiments on cell-free purified enzymes confirmed that Radix scutellariae derived baicalein and baicalin showed significant inhibition on 5-LO and 15-LO, while Rhizoma coptidis derived coptisine showed medium inhibition on LTA(4)H. On the other hand, no significant inhibition of thirteen components on COX-1/2 was observed. Moreover, the slight synergetic inhibition on LTB(4) between baicalein and coptisine was proved in the rat peritoneal macrophages.
Baicalein and coptisine, the active components of HLJDT, for the first time are found to interfere with arachidonic acid cascade via inhibition on different points of LO pathway. This finding makes the mechanism of HLJDT clearer and achieves its safer therapeutic application.
Journal of ethnopharmacology 04/2011; 135(2):561-8. · 2.32 Impact Factor
[show abstract][hide abstract] ABSTRACT: Liver is the largest internal organ in the body that takes central roles in metabolic homeostasis, detoxification of various substances, as well as in the synthesis and storage of nutrients. To fulfill these complex tasks, thousands of biochemical reactions are going on in liver to cope with a wide range of foods and environmental variations, which are densely interconnected into an intricate metabolic network. Here, the first human liver-specific metabolic network was reconstructed according to proteomics data from Chinese Human Liver Proteome Project (CNHLPP), and then investigated in the context of the genome-scale metabolic network of Homo sapiens. Topological analysis shows that this organ-specific metabolic network exhibits similar features as organism-specific networks, such as power-law degree distribution, small-world property, and bow-tie structure. Furthermore, the structure of liver network exhibits a modular organization where the modules are formed around precursors from primary metabolism or hub metabolites from derivative metabolism, respectively. Most of the modules are dominated by one major category of metabolisms, while enzymes within same modules have a tendency of being expressed concertedly at protein level. Network decomposition and comparison suggest that the liver network overlays a predominant area in the global metabolic network of H. sapiens genome; meanwhile the human network may develop extra modules to gain more specialized functionality out of liver. The results of this study would permit a high-level interpretation of the metabolite information flow in human liver and provide a basis for modeling the physiological and pathological metabolic states of liver.
Journal of Proteome Research 02/2010; 9(4):1648-58. · 5.06 Impact Factor
[show abstract][hide abstract] ABSTRACT: Species of the genus Streptomyces are major bacteria responsible for producing most natural antibiotics. Streptomyces coelicolor A3(2) and Streptomyces avermitilis were sequenced in 2002 and 2003, respectively. Two-component signal transduction systems (TCSs), consisting of a histidine sensor kinase (SK) and a cognate response regulator (RR), form the most common mechanism of transmembrane signal transduction in prokaryotes. TCSs in S. coelicolor A3(2) have been analyzed in detail. Here, we identify and classify the SK and RR of S. avermitilis and compare the TCSs with those of S. coelicolor A3(2) by computational approaches. Phylogenetic analysis of the cognate SK-RR pairs of the two species indicated that the cognate SK-RR pairs fall into four classes according to the distribution of their orthologs in other organisms. In addition to the cognate SK-RR pairs, some potential partners of non-cognate SK-RR were found, including those of unpaired SK and orphan RR and the cross-talk between different components in either strain. Our study provides new clues for further exploration of the molecular regulation mechanism of streptomycetes with industrial importance.
[show abstract][hide abstract] ABSTRACT: Exploring the structural topology of genome-based large-scale metabolic network is essential for investigating possible relations
between structure and functionality. Visualization would be helpful for obtaining immediate information about structural organization.
In this work, metabolic networks of 75 organisms were investigated from a topological point of view. A spread bow-tie model
was proposed to give a clear visualization of the bow-tie structure for metabolic networks. The revealed topological pattern
helps to design more efficient algorithm specifically for metabolic networks. This coarsegrained graph also visualizes the
vulnerable connections in the network, and thus could have important implication for disease studies and drug target identifications.
In addition, analysis on the reciprocal links and main cores in the GSC part of bow-tie also reveals that the bow-tie structure
of metabolic networks has its own intrinsic and significant features which are significantly different from those of random
Chinese Science Bulletin 03/2007; 52(8):1036-1045. · 1.32 Impact Factor
[show abstract][hide abstract] ABSTRACT: The architecture of biological networks has been reported to exhibit high level of modularity, and to some extent, topological modules of networks overlap with known functional modules. However, how the modular topology of the molecular network affects the evolution of its member proteins remains unclear.
In this work, the functional and evolutionary modularity of Homo sapiens (H. sapiens) metabolic network were investigated from a topological point of view. Network decomposition shows that the metabolic network is organized in a highly modular core-periphery way, in which the core modules are tightly linked together and perform basic metabolism functions, whereas the periphery modules only interact with few modules and accomplish relatively independent and specialized functions. Moreover, over half of the modules exhibit co-evolutionary feature and belong to specific evolutionary ages. Peripheral modules tend to evolve more cohesively and faster than core modules do.
The correlation between functional, evolutionary and topological modularity suggests that the evolutionary history and functional requirements of metabolic systems have been imprinted in the architecture of metabolic networks. Such systems level analysis could demonstrate how the evolution of genes may be placed in a genome-scale network context, giving a novel perspective on molecular evolution.
[show abstract][hide abstract] ABSTRACT: One of the main tasks of post-genomic informatics is to systematically investigate all molecules and their interactions within
a living cell so as to understand how these molecules and the interactions between them relate to the function of the organism,
while networks are appropriate abstract description of all kinds of interactions. In the past few years, great achievement
has been made in developing theory of complex networks for revealing the organizing principles that govern the formation and
evolution of various complex biological, technological and social networks. This paper reviews the accomplishments in constructing
genome-based metabolic networks and describes how the theory of complex networks is applied to analyze metabolic networks.
Chinese Science Bulletin 01/2006; 51(13):1529-1537. · 1.32 Impact Factor
[show abstract][hide abstract] ABSTRACT: The exploration of the structural topology and the organizing principles of genome-based large-scale metabolic networks is essential for studying possible relations between structure and functionality of metabolic networks. Topological analysis of graph models has often been applied to study the structural characteristics of complex metabolic networks.
In this work, metabolic networks of 75 organisms were investigated from a topological point of view. Network decomposition of three microbes (Escherichia coli, Aeropyrum pernix and Saccharomyces cerevisiae) shows that almost all of the sub-networks exhibit a highly modularized bow-tie topological pattern similar to that of the global metabolic networks. Moreover, these small bow-ties are hierarchically nested into larger ones and collectively integrated into a large metabolic network, and important features of this modularity are not observed in the random shuffled network. In addition, such a bow-tie pattern appears to be present in certain chemically isolated functional modules and spatially separated modules including carbohydrate metabolism, cytosol and mitochondrion respectively.
The highly modularized bow-tie pattern is present at different levels and scales, and in different chemical and spatial modules of metabolic networks, which is likely the result of the evolutionary process rather than a random accident. Identification and analysis of such a pattern is helpful for understanding the design principles and facilitate the modelling of metabolic networks.
[show abstract][hide abstract] ABSTRACT: Prediction of protein structural class from primary structure is studied in this paper. Wavelet packet transform is used to
decompose the corresponding numerical signal of protein into several sub-signals at different resolution scales. The auto-correlation
functions based on the sub-signals are used as feature vectors of the protein. The Bayes decision rule is used as classification
algorithm. Experiments show that for the same datasets, the prediction accuracy is improved compared with the existed methods.
Computational and Information Science, First International Symposium, CIS 2004, Shanghai, China, December 16-18, 2004, Proceedings; 01/2004
[show abstract][hide abstract] ABSTRACT: In this paper, a novel WNN, multi-input and multi-output feedforward wavelet neural network is constructed. In the hidden
layer, wavelet basis functions are used as activate function instead of the sigmoid function of feedforward network. The training
formulas based on BP algorithm are mathematically derived and training algorithm is presented. A numerical experiment is given
to validate the application of this wavelet neural network in multi-variable functional approximation.
Computational and Information Science, First International Symposium, CIS 2004, Shanghai, China, December 16-18, 2004, Proceedings; 01/2004