2010年, 第23卷, 第5期　刊出日期：2010-10-25

  

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  Editorial

  Luonan CHEN;Xiang-Sun ZHANG

  Journal of Systems Science and Complexity. 2010, 23(5): 883-883. https://doi.org/10.1007/s11424-010-0003-8

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  MIP FORMULATIONS AND HEURISTICS FOR SOLVING PARALLELBATCHING PROBLEMS

  Udo BUSCHER;Liji SHEN

  Journal of Systems Science and Complexity. 2010, 23(5): 884-895. https://doi.org/10.1007/s11424-010-0210-3

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  NONLINEAR LANGEVIN MODEL WITH PRODUCT STOCHASTICITY FORBIOLOGICAL NETWORKS: THE CASE OF THE SCHNAKENBERG MODEL

  Youfang CAO;Jie LIANG

  Journal of Systems Science and Complexity. 2010, 23(5): 896-905. https://doi.org/10.1007/s11424-010-0213-0

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  EFFICIENT ALGORITHMS FOR IDENTIFYING ORTHOLOGOUS SIMPLESEQUENCE REPEATS OF DISEASE GENES

  Chienming CHEN;Chihchia CHEN;Tsanhuang SHIH;Tunwen PAI;Chinhua HU;Wenshyong TZOU

  Journal of Systems Science and Complexity. 2010, 23(5): 906-916. https://doi.org/10.1007/s11424-010-0203-2

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  Dynamic mutations of simple sequence repeats (SSRs) have been demonstrated to affect normal gene function and cause different genetic disorders. Several conserved and even partial functional SSR patterns are discovered in inherited orthologous disease genes. To explore a wide range of SSRs in genetic diseases, a comprehensive system focusing on identifying orthologous SSRs of disease genes
  through a comparative genomics mechanism is constructed and accomplished by adopting online Mendelian inheritance in man (OMIM) and NCBI HomoloGene databases as the fundamental resources of human genetic diseases and homologous gene information. In addition, an efficient and effective algorithm for searching SSR patterns is also developed for providing annotated SSR information among various model species. By integrating these data resources and mining
  technologies, biologists and doctors can systematically retrieve novel and important conserved SSR information among orthologous disease genes. The proposed ystem, Orthologous SSR for Disease Genes (OSDG), is the first comprehensive
  framework for identifying orthologous SSRs as potential causative factors of genetic disorders and is freely available at http://osdg.cs.ntou.edu.tw/.
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  ON NETWORK-BASED KERNEL METHODS FOR PROTEIN-PROTEININTERACTIONS WITH APPLICATIONS IN PROTEIN FUNCTIONS PREDICTION

  Limin LI;Waiki CHING;Yatming CHAN;Hiroshi MAMITSUKA

  Journal of Systems Science and Complexity. 2010, 23(5): 917-930. https://doi.org/10.1007/s11424-010-0207-y

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  Predicting protein functions is an important issue in the post-genomic era. This paper studies several network-based kernels including local linear embedding (LLE) kernel method, diffusion kernel and laplacian kernel to uncover the relationship between proteins functions and protein-protein interactions (PPI). The author first construct kernels based on PPI networks, then apply support vector machine (SVM) techniques to classify proteins into different functional groups. The 5-fold cross validation is then applied to the selected 359 GO terms to compare the performance of different kernels and guilt-by-association methods including
  neighbor counting methods and Chi-square methods. Finally, the authors conduct predictions of functions of some unknown genes and verify the preciseness of our prediction in part by the information of other data source.
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  SELF-ORGANIZING MAP OF COMPLEX NETWORKS FOR COMMUNITYDETECTION

  Zhenping LI;Ruisheng WANG;Xiang-Sun ZHANG;Luonan CHEN

  Journal of Systems Science and Complexity. 2010, 23(5): 931-941. https://doi.org/10.1007/s11424-010-0202-3

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  Detecting communities from complex networks is an important
  issue and has attracted attention of researchers in many fields. It
  is relevant to social tasks, biological inquiries, and technological
  problems since various networks exist in these systems. This paper
  proposes a new self-organizing map (SOM) based approach to community
  detection. By adopting a new operation and a new weight-updating
  scheme, a complex network can be organized into dense subgraphs
  according to the topological connection of each node by the SOM
  algorithm. Extensive numerical experiments show that the performance
  of the SOM algorithm is good. It can identify communities more
  accurately than existing methods. This method can be used to detect
  communities not only in undirected networks, but also in directed
  networks and bipartite networks.
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  THE EFFECT OF COUPLED FEEDBACK ON NOISE FILTERING IN SIGNALTRANSDUCTION NETWORKS

  Dengyu LIU;Xiao CHANG;Zengrong LIU;Luonan CHEN;Ruiqi WANG

  Journal of Systems Science and Complexity. 2010, 23(5): 942-950. https://doi.org/10.1007/s11424-010-0209-9

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  Many interacting biomolecular components in cells form different
  positive or negative feedback loops. When biological signals transduce through cascades consisting of various loops they will be affected or even distorted. Especially, how to process various signals buried in various intrinsic and extrinsic
  noises is an important issue. This paper analyzes how the response time influences noise filtering ability and how to enhance the ability by coupling different feedback loops. A parameter to measure the response time of the signal transduction,
  i.e., $\tau_{0.9}$, and its relationship between the response time and noise filtering will be discussed. The authors show clearly that the longer the response time is, the better the ability to filter noises will be. Therefore, to enhance the ability to filter noises, the authors can prolong the response time by coupling
  different positive or negative feedback loops. The results provide a possible approach to enhance the ability to filter noises in larger biomolecular networks.
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  THE METHOD OF COLORING IN GRAPHS AND ITS APPLICATION

  Guizhen LIU;Jianfeng HOU

  Journal of Systems Science and Complexity. 2010, 23(5): 951-960. https://doi.org/10.1007/s11424-010-0211-2

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  Graph coloring has interesting real life applications in optimization and network design. In this paper some new results on the acyclic-edge coloring, $f$-edge coloring, $g$-edge cover coloring, $(g, f)$-coloring and equitable edge-coloring of graphs are introduced. In particular, some new results related to the
  above colorings obtained by the authors are given. Some new problems and conjectures are presented.
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  THE RECONSTRUCTION OF AN HERMITIAN TOEPLITZ MATRIX WITH PRESCRIBED EIGENPAIRS

  Zhongyun LIU;Lu CHEN;Yulin ZHANG

  Journal of Systems Science and Complexity. 2010, 23(5): 961-970. https://doi.org/10.1007/s11424-010-0212-1

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  This paper concerns the reconstruction of an hermitian Toeplitz matrix with prescribed eigenpairs. Based on the fact that every centrohermitian matrix can be reduced to a real matrix by a simple similarity transformation, the authors first
  consider the eigenstructure of hermitian Toeplitz matrices and then
  discuss a related reconstruction problem. The authors show that the
  dimension of the subspace of hermitian Toeplitz matrices with two
  given eigenvectors is at least two and independent of the size of
  the matrix, and the solution of the reconstruction problem of an
  hermitian Toeplitz matrix with two given eigenpairs is unique.
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  A LINEAR PROGRAMMING MODEL BASED ON NETWORK FLOW FOR PATHWAYINFERENCE

  Xianwen REN;Xiang-Sun ZHANG

  Journal of Systems Science and Complexity. 2010, 23(5): 971-977. https://doi.org/10.1007/s11424-010-0206-z

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  Signal transduction pathways play important roles in various
  biological processes such as cell cycle, apoptosis, proliferation,
  differentiation and responses to the external stimuli. Efficient
  computational methods are of great demands to map signaling pathways
  systematically based on the interactome and microarray data in the
  post-genome era. This paper proposes a novel approach to infer the
  pathways based on the network flow well studied in the operation
  research. The authors define a potentiality variable for each
  protein to denote the extent to which it contributes to the
  objective pathway. And the capacity on each edge is not a constant
  but a function of the potentiality variables of the corresponding
  two proteins. The total potentiality of all proteins is given an
  upper bound. The approach is formulated to a linear programming
  model and solved by the simplex method. Experiments on the yeast
  sporulation data suggest this novel approach recreats successfully
  the backbone of the MAPK signaling pathway with a low upper bound of
  the total potentiality. By increasing the upper bound, the approach
  successfully predicts all the members of the Mitogen-activated
  protein kinases (MAPK) pathway responding to the pheromone. This
  simple but effective approach can also be used to infer the genetic
  information processing pathways underlying the expression
  quantitative trait loci (eQTL) associations, illustrated by the
  second example.
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  STOCHASTIC SYNCHRONIZATION OF CIRCADIAN RHYTHMS

  RajKumar Brojen SINGH;Vikram SINGH;Ram RAMASWAMY

  Journal of Systems Science and Complexity. 2010, 23(5): 978-988. https://doi.org/10.1007/s11424-010-0208-x

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  Models of circadian genetic oscillators involving interlinked
  feedback processes in molecular level genetic networks in
  Drosophila melanogaster and Neurospora crassa are studied, and
  mechanisms whereby synchronization can arise in an assembly of
  cells are examined. The individual subcellular circadian
  oscillatory processes are stochastic in nature due to the small
  numbers of molecules that are involved, and are subject to large
  fluctuations. The authors investigate and present the simulations
  of the stochastic dynamics of ensembles of clock--regulating
  proteins in different nuclei that communicate via ancillary small
  molecules, environmental parameters, additive cellular noise, or
  through diffusive processes. The results show that the emergence
  of collective oscillations is a macroscopic observable which has
  its origins in the microscopic coupling between distinct cellular
  oscillators.
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  A NEW APPROACH OF AVAILABLE TRANSFER CAPABILITYINCORPORATING WIND GENERATION

  Xiaojiao TONG;Chunping LIU;Xiao LUO;Renjun ZHOU

  Journal of Systems Science and Complexity. 2010, 23(5): 989-998. https://doi.org/10.1007/s11424-010-0215-y

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  The approach of available transfer capability (denoted as
  ATC) incorporating wind generation has been paid very high
  attention since the development of wind generation. Based on the
  maximum function, this paper presents an ATC model. The
  characteristic of the new model is twofold. First, it considers
  wind turbines connected to power system and static security of
  power system simultaneously. Second, it is a system of semismooth
  equations and can be solved easily. By using the smoothing
  strategy, a smoothing Newton method is adopted for solving the
  proposed new ATC model. Numerical simulation results of the IEEE
  30-bus and 118-bus system show that the new model and algorithm
  are feasible and effective. The impact of wind turbines connected
  to power system on ATC is also analyzed.
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  ANALYSIS FOR GENE NETWORKS BASED ON LOGIC RELATIONSHIPS

  Shudong WANG;Yan CHEN ;Qingyun WANG;Eryan LI;Yansen SU;Dazhi MENG

  Journal of Systems Science and Complexity. 2010, 23(5): 999-1011. https://doi.org/10.1007/s11424-010-0205-0

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  The reverse construction and analysis of the networks of
  molecular interactions are essential for understanding their
  functions within cells. In this paper, a logic network model is
  constructed to investigate the complicated regulation mechanism of
  shoot genes of Arabidopsis Thaliana in response to stimuli. The
  dynamics of the complicated logic network is analyzed, discussed,
  and simulated. The simulation results show that the logic network
  of the active genes of shoot eventually evolves into eleven
  attractors under the stimuli, including five $1$-periodic and six
  $2$-periodic attractors. Our work provides valuable reference and
  guidance for biologists to understand and explain Arabidopsis'
  response to external stimuli by experiments.
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  SEQUENCE-BASED PROTEIN-PROTEIN INTERACTION PREDICTION VIASUPPORT VECTOR MACHINE

  Yongcui WANG;Jiguang WANG;Zhixia YANG;Naiyang DENG

  Journal of Systems Science and Complexity. 2010, 23(5): 1012-1023. https://doi.org/10.1007/s11424-010-0214-z

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  This paper develops sequence-based methods for identifying
  novel protein-protein interactions (PPIs) by means of support
  vector machines (SVMs). The authors encode proteins ont only in
  the gene level but also in the amino acid level, and design a
  procedure to select negative training set for dealing with the
  training dataset imbalance problem, i.e., the number of
  interacting protein pairs is scarce relative to large scale
  non-interacting protein pairs. The proposed methods are validated
  on PPIs data of Plasmodium falciparum and Escherichia coli, and
  yields the predictive accuracy of $93.8\%$ and $95.3\%$,
  respectively. The functional annotation analysis and database
  search indicate that our novel predictions are worthy of future
  experimental validation.The new methods will be useful supplementary tools for the future proteomics studies.
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  DETECTING COMMUNITY STRUCTURE: FROM PARSIMONY TO WEIGHTEDPARSIMONY

  Junhua ZHANG;Yuqing QIU;Xiang-Sun ZHANG

  Journal of Systems Science and Complexity. 2010, 23(5): 1024-1036. https://doi.org/10.1007/s11424-010-0204-1

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  Community detection has attracted a great deal of attention in
  recent years. A parsimony criterion for detecting this structure
  means that as minimal as possible number of inserted and deleted
  edges is needed when we make the network considered become a
  disjoint union of cliques. However, many small groups of nodes are
  obtained by directly using this criterion to some networks
  especially for sparse ones. In this paper we propose a weighted
  parsimony model in which a weight coefficient is introduced to
  balance the inserted and deleted edges to ensure the obtained
  subgraphs to be reasonable communities. Some benchmark testing
  examples are used to validate the effectiveness of the proposed
  method. It is interesting that the weight here can be determined
  only by the topological features of the network. Meanwhile we make
  some comparison of our model with maximizing modularity $Q$ and
  modularity density $D$ on some of the benchmark networks, although
  sometimes too many or a little less numbers of communities are
  obtained with $Q$ or $D$, a proper number of communities are
  detected with the weighted model. All the computational results
  confirm its capability for community detection for the small or
  middle size networks.