一个文本搜索引擎的实现

##1 Title: The metabolic world of Escherichia coli is not small Abstract: To elucidate the organizational and evolutionary principles of the metabolism of living organisms, recent studies have addressed the graph-theoretic analysis of large biochemical networks responsible for the synthesis and degradation of cellular building blocks [Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. \& Barab\{\'a\}si, A. L. (2000) Nature 407, 651-654; Wagner, A. \& Fell, D. A. (2001) Proc. R. Soc. London Ser. B 268, 1803-1810; and Ma, H.-W. \& Zeng, A.-P. (2003) Bioinformatics 19, 270-277]. In such studies, the global properties of the network are computed by considering enzymatic reactions as links between metabolites. However, the pathways computed in this manner do not conserve their structural moieties and therefore do not correspond to biochemical pathways on the traditional metabolic map. In this work, we reassessed earlier results by digitizing carbon atomic traces in metabolic reactions annotated for Escherichia coli. Our analysis revealed that the average path length of its metabolism is much longer than previously thought and that the metabolic world of this organism is not small in terms of biosynthesis and degradation." ##2 Title: Reverse Engineering of Biological Complexity Abstract: Advanced technologies and biology have extremely different physical implementations, but they are far more alike in systems-level organization than is widely appreciated. {C}onvergent evolution in both domains produces modular architectures that are composed of elaborate hierarchies of protocols and layers of feedback regulation, are driven by demand for robustness to uncertain environments, and use often imprecise components. {T}his complexity may be largely hidden in idealized laboratory settings and in normal operation, becoming conspicuous only when contributing to rare cascading failures. {T}hese puzzling and paradoxical features are neither accidental nor artificial, but derive from a deep and necessary interplay between complexity and robustness, modularity, feedback, and fragility. {T}his review describes insights from engineering theory and practice that can shed some light on biological complexity." ##3 Title: Exploring complex networks Abstract: The study of networks pervades all of science, from neurobiology to statistical physics. {T}he most basic issues are structural: how does one characterize the wiring diagram of a food web or the {I}nternet or the metabolic network of the bacterium {E}scherichia coli? {A}re there any unifying principles underlying their topology? {F}rom the perspective of nonlinear dynamics, we would also like to understand how an enormous network of interacting dynamical systems-be they neurons, power stations or lasers-will behave collectively, given their individual dynamics and coupling architecture. {R}esearchers are only now beginning to unravel the structure and dynamics of complex networks." ##4 Title: Comparative assessment of large-scale data sets of protein-protein interactions. Abstract: Comprehensive protein protein interaction maps promise to reveal many aspects of the complex regulatory network underlying cellular function. Recently, large-scale approaches have predicted many new protein interactions in yeast. To measure their accuracy and potential as well as to identify biases, strengths and weaknesses, we compare the methods with each other and with a reference set of previously reported protein interactions." ##5 Title: Navigation in a small world Abstract: The small-world phenomenon �� the principle that most of us are linked by short chains of acquaintances �� was first investigated as a question in sociology1, 2 and is a feature of a range of networks arising in nature and technology3, 4, 5. Experimental study of the phenomenon1 revealed that it has two fundamental components: first, such short chains are ubiquitous, and second, individuals operating with purely local information are very adept at finding these chains. The first issue has been analysed2, 3, 4, and here I investigate the second by modelling how individuals can find short chains in a large social network." ##6 Title: Random graphs with arbitrary degree distributions and their applications. Abstract: Recent work on the structure of social networks and the internet has focussed attention on graphs with distributions of vertex degree that are significantly different from the Poisson degree distributions that have been widely studied in the past. In this paper we develop in detail the theory of random graphs with arbitrary degree distributions. In addition to simple undirected, unipartite graphs, we examine the properties of directed and bipartite graphs. Among other results, we derive exact expressions for the position of the phase transition at which a giant component first forms, the mean component size, the size of the giant component if there is one, the mean number of vertices a certain distance away from a randomly chosen vertex, and the average vertex-vertex distance within a graph. We apply our theory to some real-world graphs, including the world-wide web and collaboration graphs of scientists and Fortune 1000 company directors. We demonstrate that in some cases random graphs with appropriate distributions of vertex degree predict with surprising accuracy the behavior of the real world, while in others there is a measurable discrepancy between theory and reality, perhaps indicating the presence of additional social structure in the network that is not captured by the random graph." ##7 Title: Artificial gene networks for objective comparison of analysis algorithms Abstract: Motivation: Large-scale gene expression profiling generates data sets that are rich in observed features but poor in numbers of observations. The analysis of such data sets is a challenge that has been object of vigorous research. The algorithms in use for this purpose have been poorly documented and rarely compared objectively, posing a problem of uncertainty about the outcomes of the analyses. One way to objectively test such analysis algorithms is to apply them on computational gene network models for which the mechanisms are completely know. Results: We present a system that generates random artificial gene networks according to well-defined topological and kinetic properties. These are used to run in silico experiments simulating real laboratory microarray experiments. Noise with controlled properties is added to the simulation results several times emulating measurement replicates, before expression ratios are calculated. Availability: The data sets and kinetic models described here are available from http://www.vbi.vt.edu/~mendes/AGN/as biochemical dynamic models in SBML and Gepasi formats. Contact: mendes@vt.edu 10.1093/bioinformatics/btg1069" ##8 Title: The segment polarity network is a robust developmental module Abstract: All insects possess homologous segments, but segment specification differs radically among insect orders. {I}n {D}rosophila, maternal morphogens control the patterned activation of gap genes, which encode transcriptional regulators that shape the patterned expression of pair-rule genes. {T}his patterning cascade takes place before cellularization. {P}air-rule gene products subsequently 'imprint' segment polarity genes with reiterated patterns, thus defining the primordial segments. {T}his mechanism must be greatly modified in insect groups in which many segments emerge only after cellularization. {I}n beetles and parasitic wasps, for instance, pair-rule homologues are expressed in patterns consistent with roles during segmentation, but these patterns emerge within cellular fields. {I}n contrast, although in locusts pair-rule homologues may not control segmentation, some segment polarity genes and their interactions are conserved. {P}erhaps segmentation is modular, with each module autonomously expr