Metabolomic screening and star pattern recognition by urinary amino acid profile analysis from bladder cancer patients

Metabolomic analysis of urinary amino acids (AAs) from patients with bladder cancer was performed by gas chromatography–mass spectrometry. The β-aminoisobutyric acid (P < 0.05) and pyroglutamic acid (P < 0.03) levels among 21 AAs had relatively low P-values in the cancer group. The distorted star pattern of the cancer group was different from the heneicosagonal shape of the control mean. The present metabolomic screening combined with star pattern recognition method as clinical monitoring tool might be useful for the visual discrimination of bladder cancer group from normal group.     

Three-dimensional pattern recognition: an approach to automated interpretation of electron density maps of proteins

A procedure is outlined for reducing the high resolution electron density map of a protein to a set of connected thin lines which follow the density. The side chain representations are removed from this skeleton leaving primarily main chain, disulfide bridges and very strong hydrogen bonds. Crystallographic and local operators are used to separate one protein molecule from the neighboring chains in the crystal. Provisional α-carbon positions along the skeletal main chain are derived by application of the “4 Å rule”.The application of these methods to the 2.0 Å electron density map of ribonuclease S (Wyckoff et al., 1970) is described. The skeleton of the isolated molecule that is produced in this fashion provides a good over-all view of the three-dimensional folding of the protein. The results suggest that the skeleton representation can be a valuable supplement to the present methods of map interpretation and a significant step towards complete automation of the interpretation process.The three-dimensional pattern recognition procedures described may have much broader applications than the protein structure problem for which they have been developed.     

Biological networks: from physical principles to biological insights

A report on the Fourth Georgia Tech and UGA International Conference on Bioinformatics 'Biological Networks: From Genomics to Epidemiology', Atlanta, USA, 13-16 November 2003.     

A model for pattern recognition by cell networks

A study is made of certain properties of a model cell network based on lateral inhibition. It is demonstrated that specific patterns of input activity, termed eigenpatterns, will pass through the network without distortion. Under certain conditions the network will show selectivity for one particular eigenpattern, and the degree of this selectivity is under the control of non-specific parameters of the network.     

History of lectins: from hemagglutinins to biological recognition molecules

The occurrence in nature of erythrocyte-agglutinating proteins has been known since the turn of the 19th century. By the 1960s it became apparent that such proteins also agglutinate other types of cells, and that many of them are sugar-specific. These cell-agglutinating and sugar-specific proteins have been named lectins. Although shown to occur widely in plants and to some extent also in invertebrates, very few lectins had been isolated until the early 1970s, and they had attracted little attention. This attitude changed with the demonstration that lectins are extremely useful tools for the investigation of carbohydrates on cell surfaces, in particular of the changes that the latter undergo in malignancy, as well as for the isolation and characterization of glycoproteins. In subsequent years numerous lectins have been isolated from plants as well as from microorganisms and animals, and during the past two decades the structures of hundreds of them have been established. Concurrently, it was shown that lectins function as recognition molecules in cell-molecule and cell-cell interactions in a variety of biological systems. Here we present a brief account of 100-plus years of lectin research and show how these proteins have become the focus of intense interest for biologists and in particular for the glycobiologists among them.     

Self-organizing hierarchic networks for pattern recognition in protein sequence.

We present a method based on hierarchical self-organizing maps (SOMs) for recognizing patterns in protein sequences. The method is fully automatic, does not require prealigned sequences, is insensitive to redundancy in the training set, and works surprisingly well even with small learning sets. Because it uses unsupervised neural networks, it is able to extract patterns that are not present in all of the unaligned sequences of the learning set. The identification of these patterns in sequence databases is sensitive and efficient. The procedure comprises three main training stages. In the first stage, one SOM is trained to extract common features from the set of unaligned learning sequences. A feature is a number of ungapped sequence segments (usually 4-16 residues long) that are similar to segments in most of the sequences of the learning set according to an initial similarity matrix. In the second training stage, the recognition of each individual feature is refined by selecting an optimal weighting matrix out of a variety of existing amino acid similarity matrices. In a third stage of the SOM procedure, the position of the features in the individual sequences is learned. This allows for variants with feature repeats and feature shuffling. The procedure has been successfully applied to a number of notoriously difficult cases with distinct recognition problems: helix-turn-helix motifs in DNA-binding proteins, the CUB domain of developmentally regulated proteins, and the superfamily of ribokinases. A comparison with the established database search procedure PROFILE (and with several others) led to the conclusion that the new automatic method performs satisfactorily.     

Gene networks capable of pattern formation: from induction to reaction-diffusion

One of the main aims of developmental biology is to understand how a single and apparently homogeneous egg cell achieves the intricate complexity of the adult. Here we present two models to explain the generation of developmental patterns through interactions at the gene level. One model considers direct-contact induction between cells while the other takes into account diffusion of hormones. We show that sets of cells involving identical gene networks and communicating through hormones spontaneously exhibit ordered patterns. We have characterized these patterns and the specific networks responsible for them. The models allow to (i) compare diffusion and direct-contact induction processes as mechanisms of pattern generation; (ii) identify the possible range of behaviour of real gene networks and (iii) suggest causal mechanisms to generate known patterns. The evolutionary implications are discussed.     

Amino acids-nucleotides biomolecular recognition: from biological occurrence to affinity chromatography

In this review, the protein-DNA interactions are discussed considering different perspectives, and the biological occurrence of this interaction is explained at atomic level. The evaluation of the amino acid-nucleotide recognition has been investigated analysing datasets for predicting the association preferences and the geometry that favours the interaction. Based on this knowledge, an affinity chromatographic method was developed also exploiting this biological favoured contact. In fact, the implementation of this technique brings the possibility to apply the concept of molecular interactions to the development of new purification methodologies. In addition, the integration of the information recovered by all the different perspectives can bring new insights about some biological mechanisms, though not totally clarified.     

From gene to biomolecular networks: a review of evidences for understanding complex biological function in plants

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Expansion of pathogen recognition specificity in plants using pattern recognition receptors and artificially designed decoys

Pathogen/microbe-associated molecular patterns(PAMPs/MAMPs) are recognized by plant pattern recognition receptors(PRRs)localized on the cell surface to activate immune responses.This PAMP-triggered immunity(PTI) confers resistance to a broad range of pathogenic microbes and,therefore,has a great potential for genetically engineering broad-spectrum resistance by transferring PRRs across plant families.Pathogenic effectors secreted by phytopathogens often directly target and inhibit key components of PTI signaling pathways via diverse biochemical mechanisms.In some cases,plants have evolved to produce decoy proteins that mimic the direct virulence target,which senses the biochemical activities of pathogenic effectors.This kind of perception traps the effectors of erroneous targeting and results in the activation of effector-triggered immunity(ETI) instead of suppressing PTI.This mechanism suggests that artificially designed decoy proteins could be used to generate new recognition specificities in a particular plant.In this review,we summarize recent advances in research investigating PAMP recognition by PRRs and virulence effector surveillance by decoy proteins.Successful expansion of recognition specificities,conferred by the transgenic expression of EF-Tu receptor(EFR) and AvrPphB susceptible 1(PBS1) decoys,has highlighted the considerable potential of PRRs and artificially designed decoys to expand plant resistance spectra and the need to further identify novel PRRs and decoys.     

Modelling biological systems from molecules to dynamical networks

A report of the 5^th IEEE International Conference on Systems Biology (IEEE ISB2011), 2-4 September 2011, Zhuhai, China.     

Evolution of biological interaction networks: from models to real data

We are beginning to uncover common mechanisms leading to the evolution of biological networks. The driving force behind these advances is the increasing availability of comparative data in several species.     

Network Analysis Tools: from biological networks to clusters and pathways

Network Analysis Tools (NeAT) is a suite of computer tools that integrate various algorithms for the analysis of biological networks: comparison between graphs, between clusters, or between graphs and clusters; network randomization; analysis of degree distribution; network-based clustering and path finding. The tools are interconnected to enable a stepwise analysis of the network through a complete analytical workflow. In this protocol, we present a typical case of utilization, where the tasks above are combined to decipher a protein-protein interaction network retrieved from the STRING database. The results returned by NeAT are typically subnetworks, networks enriched with additional information (i.e., clusters or paths) or tables displaying statistics. Typical networks comprising several thousands of nodes and arcs can be analyzed within a few minutes. The complete protocol can be read and executed in approximately 1 h.     

A comparative study of electronic and neural networks involved in pattern recognition

The comparative study of electronic and neural networks involved in pattern recognition starts with the analogies of structure and function which exist between the electronic “basic integrative unit” and the neuron. Both elements represent the basic components in each system of networks and may be considered as functionally equivalent.According to the kind of response given to a standard input signal, four types of integrative units, either electronic or neural, may be distinguished: the fixed, the accommodative, the signal prolongating and the adaptive type.The integrative units perform many different functions. Those involved in pattern recognition, however, can all be grouped into three categories according to one of the following functions they perform: contrast detection, pattern detection and pattern discrimination. A “contrast detecting unit” gives responses in two senses, positive or negative, according to the position of the stimulus over its receptive field. A “pattern detecting unit” gives responses in one sense only, with a maximum for a pattern having the spatial distribution corresponding to the positive acting receptors of its receptive field. For performing the function of discrimination, which leads to reliable identification of any pattern, a network arrangement called a “maximum amplitude filter” is necessary. Examples of such units and arrangements existing in the nervous system are provided.It is concluded that a “logical analysis of neural networks” based on engineering principles is possible and that this could provide a new tool to the neurophysiologist in the study of the nervous system.     

Clade perseverance from Mesozoic to present: a multidisciplinary approach to interpretation of pattern and process

Two clades of marine bryozoans, cyclostomes and cheilostomes, exemplify the benefits of applying a multidisciplinary approach to the interpretation of long-term evolutionary patterns. The cyclostome bryozoans were dominant in the Mesozoic; since that era, they have decreased in absolute terms and the cheilostomes have come to exceed them in both abundance and diversity. Many studies of living assemblages of the encrusting members of these two clades indicate that cheilostomes are superior space competitors, but paleontological studies suggest that competition between the two taxa has not been escalating over geological time. Both clades occur throughout the world's oceans and seas, and recent work in the geographical extremes has shown that the relative success of the clades varies markedly from place to place. In this study, the importance of differential patterns of recruitment and cumulative space occupation in the two clades was evaluated over four years and in two environments, one temperate and one polar. In both of these environments, peaks of recruitment and space occupation by the two clades were out of phase. The different strategies and outcomes of spatial competition are examined, largely using data from the literature. Only recently has it been realized that tied outcomes of competition are stable alternative results and not simply transitory phases. Many competitive encounters involving cyclostomes result in ties, implying that their strategy is based on persistence rather than dominance. When different indices and models are used to analyze competition data from the two clades, the interpretation varies markedly with methodology. The differences in patterns of recruitment, space occupation, and spatial competition have influenced both our understanding of how the two clades have persisted alongside each other and our perception of cheilostome superiority. Analysis of fluid dynamics has shown that small differences in the mechanical structure of typical members of each clade lead to fundamental differences in water movement. For animals that rely on water motion for transport of nutritional and excretory elements (suspension feeders), small changes in current velocity and direction can have a major impact. Preliminary chemical analysis of the excurrent stream leaving cheilostome colonies has shown it to be laden with excretory products, which can interfere and mix with a neighbor's feeding currents. Clearly, spatial competition involves more than a simple mechanical "showdown."     

Qualitative networks: a symbolic approach to analyze biological signaling networks

Background  

A central goal of Systems Biology is to model and analyze biological signaling pathways that interact with one another to form complex networks. Here we introduce Qualitative networks, an extension of Boolean networks. With this framework, we use formal verification methods to check whether a model is consistent with the laboratory experimental observations on which it is based. If the model does not conform to the data, we suggest a revised model and the new hypotheses are tested in-silico.     

寄生植物对寄主植物的化学识别

植物间寄生关系的研究近年来受到了广泛的重视。大量的研究表明,寄主释放的次生物质对植物间寄生关系的建立和维持起了重要的调节作用。寄主植物的次生物质对寄生植物的化学防御和昆虫授粉等生态功能起重要的作用,寄主植物次生物质对寄生植物生理与生态的调节作用是受寄生植物基因调节的。更为重要的是寄主植物释放的次生物质成为寄生植物的种子萌发和吸器发生的异源识别物质。能够刺激寄生植物种子萌发的次生物质主要是倍半萜和氢醌类物质,而诱导吸器发生的物质则是酚酸、醌和黄酮类化合物,诱导吸器发生的核心结构是对苯醌。这些异源识别物质大多是寄主植物释放的化感抑制物质,显示寄生植物在化学防御方面要比寄主植物高级。异源识别化合物的活性与其氧化潜力显著相关。由于寄生植物中存在一抑制异源识别物质诱导吸器发生的调节过程,因此吸器的产生与寄生植物根部接触异源识别物质的浓度与时间呈正相关关系,这一调节过程对寄生植物准确识别寄主并寄生其上是十分重要的。对寄生植物和寄主植物间的化学识别关系的揭示有助于人们防治有害寄生植物和开发利用有价值的寄生植物资源。     

Drawing inspiration from biological dendrites to empower artificial neural networks

This article highlights specific features of biological neurons and their dendritic trees, whose adoption may help advance artificial neural networks used in various machine learning applications. Advancements could take the form of increased computational capabilities and/or reduced power consumption. Proposed features include dendritic anatomy, dendritic nonlinearities, and compartmentalized plasticity rules, all of which shape learning and information processing in biological networks. We discuss the computational benefits provided by these features in biological neurons and suggest ways to adopt them in artificial neurons in order to exploit the respective benefits in machine learning.