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Search Results: 1 - 10 of 849 matches for " Satoru Miyano "
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2D-Qsar for 450 types of amino acid induction peptides with a novel substructure pair descriptor having wider scope
Tsutomu Osoda, Satoru Miyano
Journal of Cheminformatics , 2011, DOI: 10.1186/1758-2946-3-50
Abstract: The substructure pair descriptor (SPAD) represents peptides as the combination of forty-nine types of key substructures and the sequence of amino acid residues between two substructures. The size of the key substructures is larger and the length of the sequence is longer than traditional descriptors. Similarity searches on C5a inhibitor data set and kinase inhibitor data set showed that order of inhibitors become three times higher by representing peptides with SPAD, respectively. Comparing scope of each descriptor shows that SPAD captures different properties from APH.QSAR/QSPR for peptides is helpful for designing various types of drugs such as kinase inhibitor and antigen. SPAD is a novel and powerful descriptor for various types of peptides. Accuracy of QSAR/QSPR becomes higher by describing peptides with SPAD.Research on the classification of small molecules using computers was popular in the 1990s [1-5], with similarity analysis of compounds being a major objective. At the time, there were mainly two methods for similarity analysis: the fingerprint description approach [4,6] and the inductive logic programming approach [7-9]. In the fingerprint description approach, a molecule is described as a sequence of bits, each of which corresponds to the existence of a chemical substructure. Atom-pair descriptor [4] or substructure type fingerprints are popular descriptors.Research on the classification of peptides became popular in the year 2000 [10-12]. The hidden Markov model (HMM) approach [12] and physical data description of peptide approach [11] were the major approaches. The main subject of these papers is the natural twenty amino acids, such as isoleucine, valine, and so on. For example, the subject of immunity concerns peptides whose components are one of 20 natural amino acids. In traditional research for the classification of peptides, an amino acid residue was described as an alphabet or a set of physical or chemical values [11].However, in practical virtua
An efficient biological pathway layout algorithm combining grid-layout and spring embedder for complicated cellular location information
Kaname Kojima, Masao Nagasaki, Satoru Miyano
BMC Bioinformatics , 2010, DOI: 10.1186/1471-2105-11-335
Abstract: We proposed a new grid-layout algorithm based on the spring embedder algorithm that can handle location information and provide layouts with harmonized appearance. In grid-layout algorithms, the mapping of nodes to grid points that minimizes a cost function is searched. By imposing positional constraints on grid points, location information including complex shapes can be easily considered. Our layout algorithm includes the spring embedder cost as a component of the cost function. We further extend the layout algorithm to enable dynamic update of the positions and sizes of compartments at each step.The new spring embedder-based grid-layout algorithm and a spring embedder algorithm are applied to three biological pathways; endothelial cell model, Fas-induced apoptosis model, and C. elegans cell fate simulation model. From the positional constraints, all the results of our algorithm satisfy location information, and hence, more comprehensible layouts are obtained as compared to the spring embedder algorithm. From the comparison of the number of crossings, the results of the grid-layout-based algorithm tend to contain more crossings than those of the spring embedder algorithm due to the positional constraints. For a fair comparison, we also apply our proposed method without positional constraints. This comparison shows that these results contain less crossings than those of the spring embedder algorithm. We also compared layouts of the proposed algorithm with and without compartment update and verified that latter can reach better local optima.For biological pathways such as signal transduction pathways, gene regulatory networks, and metabolic pathways, one of the crucial techniques for understanding their characteristics is to use graph visualization. Both publicly [1] and commercially available pathway databases [2] display retrieved pathways in the form of graphs to enable users to understand them easily. Usually, in these databases, a large number of pathways are r
Exploring temporal transcription regulation structure of Aspergillus fumigatus in heat shock by state space model
Jin Hwan Do, Rui Yamaguchi, Satoru Miyano
BMC Genomics , 2009, DOI: 10.1186/1471-2164-10-306
Abstract: The estimated transcription regulation structure of A. fumigatus shows that the heat shock proteins are strongly negatively associated with central metabolic pathway genes such as the tricarboxylic acid cycle (TCA cycle) and carbohydrate metabolism. It was 60 min and 120 min, respectively, after the growth temperature changes from 30°C (corresponding to environments of tropical soil) to 37°C and 48°C (corresponding to temperatures in the human body and compost, respectively) that some of genes in TCA cycle were started to be upregulated. In these points, most of heat shock proteins showed lowest expression level after heat shocks. Among the heat shock proteins, the HSP30 (AFU6G06470), a single integral plasma membrane heat shock protein, presented most active role in transcription regulation structure in both heat shock conditions of 37°C and 48°C. The metabolic genes associated with multiple genes in the gene regulation network showed a tendency to have opposite expression patterns of heat shock proteins. The role of those metabolic genes was second regulator in the coherent feed-forward loop type of regulation structure having heat shock protein as its first regulator. This type of regulation structure might be very advantageous for the thermal adaptation of A. fumigatus under heat shock because a small amount of heat shock proteins can rapidly magnify their regulation effect on target genes. However, the coherent feed-forward loop type of regulation of heat shock proteins with metabolic genes became less frequent with increasing temperature. This might be the reason for dramatic increase in the expression of heat shock proteins and the number of heat shock response genes at heat shock of 48°C.We systemically analysed the thermal adaption mechanism of A. fumigatus by state space model with times series microarray data in terms of transcription regulation structure. We suggest for the first time that heat shock proteins might efficiently regulate metabolic genes us
Network-Based Predictions and Simulations by Biological State Space Models: Search for Drug Mode of Action
Rui Yamaguchi,Seiya Imoto,Satoru Miyano,
Rui Yamaguchi
,Seiya Imoto,Satoru Miyano

计算机科学技术学报 , 2010,
Abstract: Since time-course microarray data are short but contain a large number of genes, most of statistical models should be extended so that they can handle such statistically irregular situations. We introduce biological state space models that are established as suitable computational models for constructing gene networks from microarray gene expression data. This chapter elucidates theory and methodology of our biological state space models together with some representative analyses including discovery of drug...
Gene Set-Based Module Discovery Decodes cis-Regulatory Codes Governing Diverse Gene Expression across Human Multiple Tissues
Atsushi Niida,Seiya Imoto,Rui Yamaguchi,Masao Nagasaki,Satoru Miyano
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0010910
Abstract: Decoding transcriptional programs governing transcriptomic diversity across human multiple tissues is a major challenge in bioinformatics. To address this problem, a number of computational methods have focused on cis-regulatory codes driving overexpression or underexpression in a single tissue as compared to others. On the other hand, we recently proposed a different approach to mine cis-regulatory codes: starting from gene sets sharing common cis-regulatory motifs, the method screens for expression modules based on expression coherence. However, both approaches seem to be insufficient to capture transcriptional programs that control gene expression in a subset of all samples. Especially, this limitation would be serious when analyzing multiple tissue data. To overcome this limitation, we developed a new module discovery method termed BEEM (Biclusering-based Extraction of Expression Modules) in order to discover expression modules that are functional in a subset of tissues. We showed that, when applied to expression profiles of human multiple tissues, BEEM finds expression modules missed by two existing approaches that are based on the coherent expression and the single tissue-specific differential expression. From the BEEM results, we obtained new insights into transcriptional programs controlling transcriptomic diversity across various types of tissues. This study introduces BEEM as a powerful tool for decoding regulatory programs from a compendium of gene expression profiles.
BFL: a node and edge betweenness based fast layout algorithm for large scale networks
Tatsunori B Hashimoto, Masao Nagasaki, Kaname Kojima, Satoru Miyano
BMC Bioinformatics , 2009, DOI: 10.1186/1471-2105-10-19
Abstract: To overcome these problems, we propose the use of a biologically important graph metric, betweenness, a measure of network flow. This metric is highly correlated with many biological phenomena such as lethality and clusters. We devise a new fast parallel algorithm calculating betweenness to minimize the preprocessing cost. Using this metric, we also invent a node and edge betweenness based fast layout algorithm (BFL). BFL places the high-betweenness nodes to optimal positions and allows the low-betweenness nodes to reach suboptimal positions. Furthermore, BFL reduces the runtime by combining a sequential insertion algorim with betweenness. For a graph with n nodes, this approach reduces the expected runtime of the algorithm to O(n2) when considering edge crossings, and to O(n log n) when considering only density and edge lengths.Our BFL algorithm is compared against fast graph layout algorithms and approaches requiring intensive optimizations. For gene networks, we show that our algorithm is faster than all layout algorithms tested while providing readability on par with intensive optimization algorithms. We achieve a 1.4 second runtime for a graph with 4000 nodes and 12000 edges on a standard desktop computer.Advances in biotechnology have made it possible to collect vast amounts of genetic data. Although extensive research has been done on numerical and statistical methods to infer the relationship among genes, which we call gene networks, methods for analyzing such data visualizing large gene networks has received less attention.There exists significant former literature on general graph layout algorithms such as orthogonal drawing, planar embedding, force-directed layout [1]. Similarly, metabolic networks with relatively small numbers of nodes (<100) have received significant attention, with notable algorithms being proposed by Karp [2], and [3]. However, these algorithms are designed with fundamentally different goals than those for gene networks. Well known fa
An efficient grid layout algorithm for biological networks utilizing various biological attributes
Kaname Kojima, Masao Nagasaki, Euna Jeong, Mitsuru Kato, Satoru Miyano
BMC Bioinformatics , 2007, DOI: 10.1186/1471-2105-8-76
Abstract: We propose a new grid layout algorithm. To address problem (i), we devised a new force-directed algorithm whose output is suitable as the initial layout. For (ii), we considered that an appropriate alignment of nodes having the same biological attribute is one of the most important factors of the comprehension, and we defined a new score function that gives an advantage to such configurations. For solving problem (iii), we developed a search strategy that considers swapping nodes as well as moving a node, while keeping the order of the time complexity. Though a na?ve implementation increases by one order, the time complexity, we solved this difficulty by devising a method that caches differences between scores of a layout and its possible updates.Layouts of the new grid layout algorithm are compared with that of the previous algorithm and human layout in an endothelial cell model, three times as large as the apoptosis model. The total cost of the result from the new grid layout algorithm is similar to that of the human layout. In addition, its convergence time is drastically reduced (40% reduction).Modeling and simulations of large scale biological pathways are some of the most important tasks in Bioinformatics. Many applications, e.g., Cell Illustrator [1,2], Cytoscape [3], Pajek [4], PATIKA [5,6], and CADLIVE [7,8] have been developed in this area. Related to these topics, the visualization of biopathways is considered to play a key role in understanding biological systems. However, manual drawing of large-scale biopathways is a time consuming work, hence suitable biopathway layout algorithms and their applications are strongly demanded.Biopathways are categorized into three types, i.e., metabolic pathways, signal transduction pathways, and gene-regulatory networks. For metabolic pathways, several algorithms have been already proposed [9-13], and some of them succeeded in capturing the flow of the reactions well. In contrast, few layout algorithms that provide a c
Time-dependent structural transformation analysis to high-level Petri net model with active state transition diagram
Chen Li, Masao Nagasaki, Ayumu Saito, Satoru Miyano
BMC Systems Biology , 2010, DOI: 10.1186/1752-0509-4-39
Abstract: Investigating the dynamic features of current computational models promises a deeper understanding of complex cellular processes. This leads us to develop a method that utilizes structural properties of the model over all simulation time steps. Further, user-friendly overviews of dynamic behaviors can be considered to provide a great help in understanding the variations of system mechanisms.We propose a novel method for constructing and analyzing a so-called active state transition diagram (ASTD) by using time-course simulation data of a high-level Petri net. Our method includes two new algorithms. The first algorithm extracts a series of subnets (called temporal subnets) reflecting biological components contributing to the dynamics, while retaining positive mathematical qualities. The second one creates an ASTD composed of unique temporal subnets. ASTD provides users with concise information allowing them to grasp and trace how a key regulatory subnet and/or a network changes with time. The applicability of our method is demonstrated by the analysis of the underlying model for circadian rhythms in Drosophila.Building ASTD is a useful means to convert a hybrid model dealing with discrete, continuous and more complicated events to finite time-dependent states. Based on ASTD, various analytical approaches can be applied to obtain new insights into not only systematic mechanisms but also dynamics.A great deal of biological datasets have been measured in a lot of laboratories around the world in recent years. Petri nets have been applied successfully in modeling, simulating and analyzing biological networks [1,2] (i.e., metabolic [3,4], signal transduction [5,6] and gene regulatory networks [7,8]). In the meanwhile, a number of public and commercial databases have developed tools to automatically convert biological pathway information into various formats of models, e.g., the tool TRANSPATH2CSML [9] automatically converts data stored in TRANSPATH [10] to a simulation-ba
Systematic reconstruction of TRANSPATH data into Cell System Markup Language
Masao Nagasaki, Ayumu Saito, Chen Li, Euna Jeong, Satoru Miyano
BMC Systems Biology , 2008, DOI: 10.1186/1752-0509-2-53
Abstract: We selected the TRANSPATH database, a manually curated high-quality pathway database, which provides a plentiful source of cellular events in humans, mice, and rats, collected from over 31,500 publications. In this work, we have developed 16 modeling rules based on hybrid functional Petri net with extension (HFPNe), which is suitable for graphical representing and simulating biological processes. In the modeling rules, each Petri net element is incorporated with Cell System Ontology to enable semantic interoperability of models. As a formal ontology for biological pathway modeling with dynamics, CSO also defines biological terminology and corresponding icons. By combining HFPNe with the CSO features, it is possible to make TRANSPATH data to simulation-based and semantically valid models. The results are encoded into a biological pathway format, Cell System Markup Language (CSML), which eases the exchange and integration of biological data and models.By using the 16 modeling rules, 97% of the reactions in TRANSPATH are converted into simulation-based models represented in CSML. This reconstruction demonstrates that it is possible to use our rules to generate quantitative models from static pathway descriptions.Biological pathways are reaction-networks of the biological processes within a cell, which can be classified into three categories: gene regulatory networks, metabolic pathways and signaling pathways. Due to the nature of concurrency of biological pathways, Petri nets have been used to model and analyze biological pathways in many studies [1-9].Nagasaki et al. [10] have proposed an object-oriented Petri nets called hybrid functional Petri net with extension (HFPNe) for modeling complex biological systems. HFPNe-based modeling allows users to easily understand biological networks with a graphical representation and reveals the expressiveness when representing the dynamic behaviors of sophisticated biological pathways. To improve the maintainability and reusabili
Simulation-based model checking approach to cell fate specification during Caenorhabditis elegans vulval development by hybrid functional Petri net with extension
Chen Li, Masao Nagasaki, Kazuko Ueno, Satoru Miyano
BMC Systems Biology , 2009, DOI: 10.1186/1752-0509-3-42
Abstract: A novel method of modeling and simulating biological systems with the use of model checking approach is proposed based on hybrid functional Petri net with extension (HFPNe) as the framework dealing with both discrete and continuous events. Firstly, we construct a quantitative VPC fate model with 1761 components by using HFPNe. Secondly, we employ two major biological fate determination rules – Rule I and Rule II – to VPC fate model. We then conduct 10,000 simulations for each of 48 sets of different genotypes, investigate variations of cell fate patterns under each genotype, and validate the two rules by comparing three simulation targets consisting of fate patterns obtained from in silico and in vivo experiments. In particular, an evaluation was successfully done by using our VPC fate model to investigate one target derived from biological experiments involving hybrid lineage observations. However, the understandings of hybrid lineages are hard to make on a discrete model because the hybrid lineage occurs when the system comes close to certain thresholds as discussed by Sternberg and Horvitz in 1986. Our simulation results suggest that: Rule I that cannot be applied with qualitative based model checking, is more reasonable than Rule II owing to the high coverage of predicted fate patterns (except for the genotype of lin-15ko; lin-12ko double mutants). More insights are also suggested.The quantitative simulation-based model checking approach is a useful means to provide us valuable biological insights and better understandings of biological systems and observation data that may be hard to capture with the qualitative one.Model checking is a successful method for automatic verification of software and reactive systems [1], which is usually applied to ensure consistency and correctness of designed models. Practiced verification methods in most cases are still simple simulation and testing. While simple simulation and testing provide a part of the possible results of a
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