Hiroaki Kitano

Publications (39)282.99 Total impact

• Article: Adding Protein Context to the Human Protein-Protein Interaction Network to Reveal Meaningful Interactions

  Martin H Schaefer, Tiago J S Lopes, Nancy Mah, Jason E Shoemaker, Yukiko Matsuoka, Jean-Fred Fontaine, Caroline Louis-Jeune, Amie J Eisfeld, Gabriele Neumann, Carol Perez-Iratxeta, Yoshihiro Kawaoka, Hiroaki Kitano, Miguel A Andrade-Navarro
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  ABSTRACT: Author Summary Protein-protein-interactions (PPIs) participate in virtually all biological processes. However, the PPI map is not static but the pairs of proteins that interact depends on the type of cell, the subcellular localization and modifications of the participating proteins, among many other factors. Therefore, it is important to understand the specific conditions under which a PPI happens. Unfortunately, experimental methods often do not provide this information or, even worse, measure PPIs under artificial conditions not found in biological systems. We developed a method to infer this missing information from properties of the interacting proteins, such as in which cell types the proteins are found, which functions they fulfill and whether they are known to play a role in disease. We show that PPIs for which we can infer conditions under which they happen have a higher experimental reliability. Also, our inference agrees well with known pathways and disease proteins. Since diseases usually affect specific cell types, we study PPI networks of influenza proteins in lung tissues and of Alzheimer's disease proteins in neural tissues. In both cases, we can highlight interesting interactions potentially playing a role in disease progression.
  PLoS Comput Biol. 01/2013; 9(1):e1002860.
• Article: Adding protein context to the human protein-protein interaction network to reveal meaningful interactions.

  Martin H Schaefer, Tiago J S Lopes, Nancy Mah, Jason E Shoemaker, Yukiko Matsuoka, Jean-Fred Fontaine, Caroline Louis-Jeune, Amie J Eisfeld, Gabriele Neumann, Carol Perez-Iratxeta, Yoshihiro Kawaoka, Hiroaki Kitano, Miguel A Andrade-Navarro
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  ABSTRACT: Interactions of proteins regulate signaling, catalysis, gene expression and many other cellular functions. Therefore, characterizing the entire human interactome is a key effort in current proteomics research. This challenge is complicated by the dynamic nature of protein-protein interactions (PPIs), which are conditional on the cellular context: both interacting proteins must be expressed in the same cell and localized in the same organelle to meet. Additionally, interactions underlie a delicate control of signaling pathways, e.g. by post-translational modifications of the protein partners - hence, many diseases are caused by the perturbation of these mechanisms. Despite the high degree of cell-state specificity of PPIs, many interactions are measured under artificial conditions (e.g. yeast cells are transfected with human genes in yeast two-hybrid assays) or even if detected in a physiological context, this information is missing from the common PPI databases. To overcome these problems, we developed a method that assigns context information to PPIs inferred from various attributes of the interacting proteins: gene expression, functional and disease annotations, and inferred pathways. We demonstrate that context consistency correlates with the experimental reliability of PPIs, which allows us to generate high-confidence tissue- and function-specific subnetworks. We illustrate how these context-filtered networks are enriched in bona fide pathways and disease proteins to prove the ability of context-filters to highlight meaningful interactions with respect to various biological questions. We use this approach to study the lung-specific pathways used by the influenza virus, pointing to IRAK1, BHLHE40 and TOLLIP as potential regulators of influenza virus pathogenicity, and to study the signalling pathways that play a role in Alzheimer's disease, identifying a pathway involving the altered phosphorylation of the Tau protein. Finally, we provide the annotated human PPI network via a web frontend that allows the construction of context-specific networks in several ways.
  PLoS Computational Biology 01/2013; 9(1):e1002860. · 5.22 Impact Factor
• Article: Identification of dosage-sensitive genes in Saccharomyces cerevisiae using the genetic tug-of-war method.

  Koji Makanae, Reiko Kintaka, Takashi Makino, Hiroaki Kitano, Hisao Moriya
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  ABSTRACT: Gene overexpression beyond a permissible limit causes defects in cellular functions. However, the permissible limits of most genes are unclear. Previously, we developed a genetic method designated genetic tug-of-war (gTOW) to measure the copy number limit of overexpression of a target gene. In the current study, we applied gTOW to the analysis of all protein-coding genes in the budding yeast Saccharomyces cerevisiae. We showed that the yeast cellular system was robust against an increase in the copy number by up to 100 copies in >80% of the genes. After frameshift and segmentation analyses, we isolated 115 dosage-sensitive genes (DSGs) with copy number limits of 10 or less. DSGs contained a significant number of genes involved in cytoskeletal organization and intracellular transport. DSGs tended to be highly expressed and to encode protein complex members. We demonstrated that the protein burden caused the dosage sensitivity of highly expressed genes using a gTOW experiment in which the open reading frame was replaced with GFP. Dosage sensitivities of some DSGs were rescued by the simultaneous increase in the copy numbers of partner genes, indicating that stoichiometric imbalances among complexes cause dosage sensitivity. The results obtained in this study will provide basic knowledge about the physiology of chromosomal abnormalities and the evolution of chromosomal composition.
  Genome Research 12/2012; · 13.61 Impact Factor
• Article: CTen: a web-based platform for identifying enriched cell types from heterogeneous microarray data.

  Jason E Shoemaker, Tiago Js Lopes, Samik Ghosh, Yukiko Matsuoka, Yoshihiro Kawaoka, Hiroaki Kitano
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  ABSTRACT: Interpreting in vivo sampled microarray data is often complicated by changes in the cell population demographics. To put gene expression into its proper biological context, it is necessary to distinguish differential gene transcription from artificial gene expression induced by changes in the cellular demographics. CTen (cell type enrichment) is a web-based analytical tool which uses our highly expressed, cell specific (HECS) gene database to identify enriched cell types in heterogeneous microarray data. The web interface is designed for differential expression and gene clustering studies, and the enrichment results are presented as heatmaps or downloadable text files. In this work, we use an independent, cell-specific gene expression data set to assess CTen's performance in accurately identifying the appropriate cell type and provide insight into the suggested level of enrichment to optimally minimize the number of false discoveries. We show that CTen, when applied to microarray data developed from infected lung tissue, can correctly identify the cell signatures of key lymphocytes in a highly heterogeneous environment and compare its performance to another popular bioinformatics tool. Furthermore, we discuss the strong implications cell type enrichment has in the design of effective microarray workflow strategies and show that, by combining CTen with gene expression clustering, we may be able to determine the relative changes in the number of key cell types.CTen is available at http://www.influenza-x.org/~jshoemaker/cten/
  BMC Genomics 09/2012; 13:460. · 4.07 Impact Factor
• Source

  Available from: Anatoly Sorokin

  Article: Software support for SBGN maps: SBGN-ML and LibSBGN.

  Martijn P van Iersel, Alice C Villéger, Tobias Czauderna, Sarah E Boyd, Frank T Bergmann, Augustin Luna, Emek Demir, Anatoly Sorokin, Ugur Dogrusoz, Yukiko Matsuoka, Akira Funahashi, Mirit I Aladjem, Huaiyu Mi, Stuart L Moodie, Hiroaki Kitano, Nicolas Le Novère, Falk Schreiber
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  ABSTRACT: MOTIVATION: LibSBGN is a software library for reading, writing and manipulating Systems Biology Graphical Notation (SBGN) maps stored using the recently developed SBGN-ML file format. The library (available in C++ and Java) makes it easy for developers to add SBGN support to their tools, whereas the file format facilitates the exchange of maps between compatible software applications. The library also supports validation of maps, which simplifies the task of ensuring compliance with the detailed SBGN specifications. With this effort we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner. AVAILABILITY AND IMPLEMENTATION: Milestone 2 was released in December 2011. Source code, example files and binaries are freely available under the terms of either the LGPL v2.1+ or Apache v2.0 open source licenses from http://libsbgn.sourceforge.net. CONTACT: sbgn-libsbgn@lists.sourceforge.net.
  Bioinformatics 05/2012; 28(15):2016-21. · 5.47 Impact Factor
• Article: BioPAX support in CellDesigner.

  Huaiyu Mi, Anushya Muruganujan, Emek Demir, Yukiko Matsuoka, Akira Funahashi, Hiroaki Kitano, Paul D Thomas
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  ABSTRACT: MOTIVATION: BioPAX is a standard language for representing and exchanging models of biological processes at the molecular and cellular levels. It is widely used by different pathway databases and genomics data analysis software. Currently, the primary source of BioPAX data is direct exports from the curated pathway databases. It is still uncommon for wet-lab biologists to share and exchange pathway knowledge using BioPAX. Instead, pathways are usually represented as informal diagrams in the literature. In order to encourage formal representation of pathways, we describe a software package that allows users to create pathway diagrams using CellDesigner, a user-friendly graphical pathway-editing tool and save the pathway data in BioPAX Level 3 format. AVAILABILITY: The plug-in is freely available and can be downloaded at ftp://ftp.pantherdb.org/CellDesigner/plugins/BioPAX/ CONTACT: huaiyumi@usc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
  Bioinformatics 12/2011; 27(24):3437-8. · 5.47 Impact Factor
• Article: Software for systems biology: from tools to integrated platforms.

  Samik Ghosh, Yukiko Matsuoka, Yoshiyuki Asai, Kun-Yi Hsin, Hiroaki Kitano
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  ABSTRACT: Understanding complex biological systems requires extensive support from software tools. Such tools are needed at each step of a systems biology computational workflow, which typically consists of data handling, network inference, deep curation, dynamical simulation and model analysis. In addition, there are now efforts to develop integrated software platforms, so that tools that are used at different stages of the workflow and by different researchers can easily be used together. This Review describes the types of software tools that are required at different stages of systems biology research and the current options that are available for systems biology researchers. We also discuss the challenges and prospects for modelling the effects of genetic changes on physiology and the concept of an integrated platform.
  Nature Reviews Genetics 11/2011; 12(12):821-32. · 38.08 Impact Factor
• Source

  Available from: Miguel Andrade

  Article: Tissue-specific subnetworks and characteristics of publicly available human protein interaction databases.

  Tiago J S Lopes, Martin Schaefer, Jason Shoemaker, Yukiko Matsuoka, Jean-Fred Fontaine, Gabriele Neumann, Miguel A Andrade-Navarro, Yoshihiro Kawaoka, Hiroaki Kitano
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  ABSTRACT: Protein-protein interaction (PPI) databases are widely used tools to study cellular pathways and networks; however, there are several databases available that still do not account for cell type-specific differences. Here, we evaluated the characteristics of six interaction databases, incorporated tissue-specific gene expression information and finally, investigated if the most popular proteins of scientific literature are involved in good quality interactions. We found that the evaluated databases are comparable in terms of node connectivity (i.e. proteins with few interaction partners also have few interaction partners in other databases), but may differ in the identity of interaction partners. We also observed that the incorporation of tissue-specific expression information significantly altered the interaction landscape and finally, we demonstrated that many of the most intensively studied proteins are engaged in interactions associated with low confidence scores. In summary, interaction databases are valuable research tools but may lead to different predictions on interactions or pathways. The accuracy of predictions can be improved by incorporating datasets on organ- and cell type-specific gene expression, and by obtaining additional interaction evidence for the most 'popular' proteins. kitano@sbi.jp Supplementary data are available at Bioinformatics online.
  Bioinformatics 09/2011; 27(17):2414-21. · 5.47 Impact Factor
• Article: Social engineering for virtual 'big science' in systems biology.

  Hiroaki Kitano, Samik Ghosh, Yukiko Matsuoka
  Nature Chemical Biology 06/2011; 7(6):323-6. · 14.69 Impact Factor
• Source

  Available from: PubMed Central

  Article: A comprehensive map of the mTOR signaling network.

  Etienne Caron, Samik Ghosh, Yukiko Matsuoka, Dariel Ashton-Beaucage, Marc Therrien, Sébastien Lemieux, Claude Perreault, Philippe P Roux, Hiroaki Kitano
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  ABSTRACT: The mammalian target of rapamycin (mTOR) is a central regulator of cell growth and proliferation. mTOR signaling is frequently dysregulated in oncogenic cells, and thus an attractive target for anticancer therapy. Using CellDesigner, a modeling support software for graphical notation, we present herein a comprehensive map of the mTOR signaling network, which includes 964 species connected by 777 reactions. The map complies with both the systems biology markup language (SBML) and graphical notation (SBGN) for computational analysis and graphical representation, respectively. As captured in the mTOR map, we review and discuss our current understanding of the mTOR signaling network and highlight the impact of mTOR feedback and crosstalk regulations on drug-based cancer therapy. This map is available on the Payao platform, a Web 2.0 based community-wide interactive process for creating more accurate and information-rich databases. Thus, this comprehensive map of the mTOR network will serve as a tool to facilitate systems-level study of up-to-date mTOR network components and signaling events toward the discovery of novel regulatory processes and therapeutic strategies for cancer.
  Molecular Systems Biology 12/2010; 6:453. · 8.63 Impact Factor
• Article: Connecting the dots: role of standardization and technology sharing in biological simulation.

  Samik Ghosh, Yukiko Matsuoka, Hiroaki Kitano
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  ABSTRACT: The role of biological modeling and simulation in enhancing productivity across the drug discovery pipeline has been increasingly appreciated over the past decade by the pharmaceutical industry. However, adoption of in silico modeling and simulation techniques has been sparse due to skepticism in the associated pay-offs and knowledge gap in research. While biological simulations have been successfully applied in specific projects, a standardized, community-wide platform is imperative for making the final leap of faith across the domain. This review outlines the issues and challenges involved in fostering a private-public collaborative effort for the development of standard modeling and biosimulation platforms and concludes with insights into possible mechanisms for integrating an in silico pipeline into the drug discovery and development process.
  Drug discovery today 10/2010; 15(23-24):1024-31. · 6.63 Impact Factor
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  Available from: PubMed Central

  Article: A comprehensive molecular interaction map of the budding yeast cell cycle.

  Kazunari Kaizu, Samik Ghosh, Yukiko Matsuoka, Hisao Moriya, Yuki Shimizu-Yoshida, Hiroaki Kitano
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  ABSTRACT: With the accumulation of data on complex molecular machineries coordinating cell-cycle dynamics, coupled with its central function in disease patho-physiologies, it is becoming increasingly important to collate the disparate knowledge sources into a comprehensive molecular network amenable to systems-level analyses. In this work, we present a comprehensive map of the budding yeast cell-cycle, curating reactions from ∼600 original papers. Toward leveraging the map as a framework to explore the underlying network architecture, we abstract the molecular components into three planes--signaling, cell-cycle core and structural planes. The planar view together with topological analyses facilitates network-centric identification of functions and control mechanisms. Further, we perform a comparative motif analysis to identify around 194 motifs including feed-forward, mutual inhibitory and feedback mechanisms contributing to cell-cycle robustness. We envisage the open access, comprehensive cell-cycle map to open roads toward community-based deeper understanding of cell-cycle dynamics.
  Molecular Systems Biology 09/2010; 6:415. · 8.63 Impact Factor
• Source

  Available from: Jun'ichi Tsujii

  Article: PathText: a text mining integrator for biological pathway visualizations.

  Brian Kemper, Takuya Matsuzaki, Yukiko Matsuoka, Yoshimasa Tsuruoka, Hiroaki Kitano, Sophia Ananiadou, Jun'ichi Tsujii
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  ABSTRACT: Motivation: Metabolic and signaling pathways are an increasingly important part of organizing knowledge in systems biology. They serve to integrate collective interpretations of facts scattered throughout literature. Biologists construct a pathway by reading a large number of articles and interpreting them as a consistent network, but most of the models constructed currently lack direct links to those articles. Biologists who want to check the original articles have to spend substantial amounts of time to collect relevant articles and identify the sections relevant to the pathway. Furthermore, with the scientific literature expanding by several thousand papers per week, keeping a model relevant requires a continuous curation effort. In this article, we present a system designed to integrate a pathway visualizer, text mining systems and annotation tools into a seamless environment. This will enable biologists to freely move between parts of a pathway and relevant sections of articles, as well as identify relevant papers from large text bases. The system, PathText, is developed by Systems Biology Institute, Okinawa Institute of Science and Technology, National Centre for Text Mining (University of Manchester) and the University of Tokyo, and is being used by groups of biologists from these locations.
  Bioinformatics 06/2010; 26(12):i374-81. · 5.47 Impact Factor
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  Available from: PubMed Central

  Article: Fragilities caused by dosage imbalance in regulation of the budding yeast cell cycle.

  Kazunari Kaizu, Hisao Moriya, Hiroaki Kitano
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  ABSTRACT: Cells can maintain their functions despite fluctuations in intracellular parameters, such as protein activities and gene expression levels. This commonly observed biological property of cells is called robustness. On the other hand, these parameters have different limitations, each reflecting the property of the subsystem containing the parameter. The budding yeast cell cycle is quite fragile upon overexpression of CDC14, but is robust upon overexpression of ESP1. The gene products of both CDC14 and ESP1 are regulated by 1ratio1 binding with their inhibitors (Net1 and Pds1), and a mathematical model predicts the extreme fragility of the cell cycle upon overexpression of CDC14 and ESP1 caused by dosage imbalance between these genes. However, it has not been experimentally shown that dosage imbalance causes fragility of the cell cycle. In this study, we measured the quantitative genetic interactions of these genes by performing combinatorial "genetic tug-of-war" experiments. We first showed experimental evidence that dosage imbalance between CDC14 and NET1 causes fragility. We also showed that fragility arising from dosage imbalance between ESP1 and PDS1 is masked by CDH1 and CLB2. The masking function of CLB2 was stabilization of Pds1 by its phosphorylation. We finally modified Chen's model according to our findings. We thus propose that dosage imbalance causes fragility in biological systems.
  PLoS Genetics 04/2010; 6(4):e1000919. · 8.69 Impact Factor
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  Available from: PubMed Central

  Article: Payao: a community platform for SBML pathway model curation.

  Yukiko Matsuoka, Samik Ghosh, Norihiro Kikuchi, Hiroaki Kitano
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  ABSTRACT: SUMMARY: Payao is a community-based, collaborative web service platform for gene-regulatory and biochemical pathway model curation. The system combines Web 2.0 technologies and online model visualization functions to enable a collaborative community to annotate and curate biological models. Payao reads the models in Systems Biology Markup Language format, displays them with CellDesigner, a process diagram editor, which complies with the Systems Biology Graphical Notation, and provides an interface for model enrichment (adding tags and comments to the models) for the access-controlled community members. Availability and implementation: Freely available for model curation service at http://www.payaologue.org. Web site implemented in Seaser Framework 2.0 with S2Flex2, MySQL 5.0 and Tomcat 5.5, with all major browsers supported. CONTACT: kitano@sbi.jp
  Bioinformatics 04/2010; 26(10):1381-3. · 5.47 Impact Factor
• Article: PathText: a text mining integrator for biological pathway visualizations.

  Brian Kemper, Takuya Matsuzaki, Yukiko Matsuoka, Yoshimasa Tsuruoka, Hiroaki Kitano, Sophia Ananiadou, Jun-ichi Tsujii
  Bioinformatics [ISMB]. 01/2010; 26:374-381.
• Source

  Available from: PubMed Central

  Article: Structure of protein interaction networks and their implications on drug design.

  Takeshi Hase, Hiroshi Tanaka, Yasuhiro Suzuki, So Nakagawa, Hiroaki Kitano
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  ABSTRACT: Protein-protein interaction networks (PINs) are rich sources of information that enable the network properties of biological systems to be understood. A study of the topological and statistical properties of budding yeast and human PINs revealed that they are scale-rich and configured as highly optimized tolerance (HOT) networks that are similar to the router-level topology of the Internet. This is different from claims that such networks are scale-free and configured through simple preferential-attachment processes. Further analysis revealed that there are extensive interconnections among middle-degree nodes that form the backbone of the networks. Degree distributions of essential genes, synthetic lethal genes, synthetic sick genes, and human drug-target genes indicate that there are advantageous drug targets among nodes with middle- to low-degree nodes. Such network properties provide the rationale for combinatorial drugs that target less prominent nodes to increase synergetic efficacy and create fewer side effects.
  PLoS Computational Biology 10/2009; 5(10):e1000550. · 5.22 Impact Factor
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  Available from: Steven Watterson

  Article: Erratum: the systems biology graphical notation.

  Nicolas Le Novère, Michael Hucka, Huaiyu Mi, Stuart Moodie, Falk Schreiber, Anatoly Sorokin, Emek Demir, Katja Wegner, Mirit I Aladjem, Sarala M Wimalaratne, [......], Esther Schmidt, Steven Watterson, Guanming Wu, Igor Goryanin, Douglas B Kell, Chris Sander, Herbert Sauro, Jacky L Snoep, Kurt Kohn, Hiroaki Kitano
  Nature Biotechnology 10/2009; 27(9):864. · 29.50 Impact Factor
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  Available from: Anatoly Sorokin

  Article: The Systems Biology Graphical Notation.

  Nicolas Le Novère, Michael Hucka, Huaiyu Mi, Stuart Moodie, Falk Schreiber, Anatoly Sorokin, Emek Demir, Katja Wegner, Mirit I Aladjem, Sarala M Wimalaratne, [......], Esther Schmidt, Steven Watterson, Guanming Wu, Igor Goryanin, Douglas B Kell, Chris Sander, Herbert Sauro, Jacky L Snoep, Kurt Kohn, Hiroaki Kitano
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  ABSTRACT: Circuit diagrams and Unified Modeling Language diagrams are just two examples of standard visual languages that help accelerate work by promoting regularity, removing ambiguity and enabling software tool support for communication of complex information. Ironically, despite having one of the highest ratios of graphical to textual information, biology still lacks standard graphical notations. The recent deluge of biological knowledge makes addressing this deficit a pressing concern. Toward this goal, we present the Systems Biology Graphical Notation (SBGN), a visual language developed by a community of biochemists, modelers and computer scientists. SBGN consists of three complementary languages: process diagram, entity relationship diagram and activity flow diagram. Together they enable scientists to represent networks of biochemical interactions in a standard, unambiguous way. We believe that SBGN will foster efficient and accurate representation, visualization, storage, exchange and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling.
  Nature Biotechnology 09/2009; 27(8):735-41. · 29.50 Impact Factor
• Article: Consistent design schematics for biological systems: standardization of representation in biological engineering

  Yukiko Matsuoka, Samik Ghosh, Hiroaki Kitano
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  ABSTRACT: 10.1098/rsif.2009.0046.focus The paradigm driving research in synthetic biology entails the engineering of de novo biological constructs with well-characterized input–output behaviours and interfaces. The construction of biological circuits requires iterative phases of design, simulation and assembly, leading to the fabrication of a biological device. In order to represent engineered models in a consistent visual format and further simulating them , standardization of representation and model formalism is imperative. In this article, we review different efforts for standardization, particularly standards for graphical visualization and simulation/annotation schemata adopted in systems biology. We identify the importance of integrating the different standardization efforts and provide insights into potential avenues for developing a common framework for model visualization, simulation and sharing across various tools. We envision that such a synergistic approach would lead to the development of globa
  Journal of The Royal Society Interface. 08/2009; 6:S393-S404.