Ayumu Saito

Cell Illustrator 4.0: a computational platform for systems biology.

Cell Illustrator is a software platform for Systems Biology that uses the concept of Petri net for modeling and simulating biopathways. It is intended for biological scientists working at bench. The latest version of Cell Illustrator 4.0 uses Java Web Start technology and is enhanced with new capabilities, including: automatic graph grid layout algorithms using ontology information; tools using Cell System Markup Language (CSML) 3.0 and Cell System Ontology 3.0; parameter search module; high-performance simulation module; CSML database management system; conversion from CSML model to programming languages (FORTRAN, C, C++, Java, Python and Perl); import from SBML, CellML, and BioPAX; and, export to SVG and HTML. Cell Illustrator employs an extension of hybrid Petri net in an object-oriented style so that biopathway models can include objects such as DNA sequence, molecular density, 3D localization information, transcription with frame-shift, translation with codon table, as well as biochemical reactions.

Epidermal Growth Factor Receptor Tyrosine Kinase Defines Critical Prognostic Genes of Stage I Lung Adenocarcinoma

Purpose To identify stage I lung adenocarcinoma patients with a poor prognosis who will benefit from adjuvant therapy. Patients and Methods Whole gene expression profiles were obtained at 19 time points over a 48-hour time course from human primary lung epithelial cells that were stimulated with epidermal growth factor (EGF) in the presence or absence of a clinically used EGF receptor tyrosine kinase (RTK)-specific inhibitor, gefitinib. The data were subjected to a mathematical simulation using the State Space Model (SSM). “Gefitinib-sensitive” genes, the expressional dynamics of which were altered by addition of gefitinib, were identified. A risk scoring model was constructed to classify high- or low-risk patients based on expression signatures of 139 gefitinib-sensitive genes in lung cancer using a training data set of 253 lung adenocarcinomas of North American cohort. The predictive ability of the risk scoring model was examined in independent cohorts of surgical specimens of lung cancer. Results The risk scoring model enabled the identification of high-risk stage IA and IB cases in another North American cohort for overall survival (OS) with a hazard ratio (HR) of 7.16 (P = 0.029) and 3.26 (P = 0.0072), respectively. It also enabled the identification of high-risk stage I cases without bronchioalveolar carcinoma (BAC) histology in a Japanese cohort for OS and recurrence-free survival (RFS) with HRs of 8.79 (P = 0.001) and 3.72 (P = 0.0049), respectively. Conclusion The set of 139 gefitinib-sensitive genes includes many genes known to be involved in biological aspects of cancer phenotypes, but not known to be involved in EGF signaling. The present result strongly re-emphasizes that EGF signaling status in cancer cells underlies an aggressive phenotype of cancer cells, which is useful for the selection of early-stage lung adenocarcinoma patients with a poor prognosis. Trial Registration The Gene Expression Omnibus (GEO) GSE31210

Molecular features of triple negative breast cancer cells by genome-wide gene expression profiling analysis

Triple negative breast cancer (TNBC) has a poor outcome due to the lack of beneficial therapeutic targets. To clarify the molecular mechanisms involved in the carcinogenesis of TNBC and to identify target molecules for novel anticancer drugs, we analyzed the gene expression profiles of 30 TNBCs as well as 13 normal epithelial ductal cells that were purified by laser-microbeam microdissection. We identified 301 and 321 transcripts that were significantly upregulated and downregulated in TNBC, respectively. In particular, gene expression profile analyses of normal human vital organs allowed us to identify 104 cancer-specific genes, including those involved in breast carcinogenesis such as NEK2, PBK and MELK. Moreover, gene annotation enrichment analysis revealed prominent gene subsets involved in the cell cycle, especially mitosis. Therefore, we focused on cell cycle regulators, asp (abnormal spindle) homolog, microcephaly-associated (Drosophila) (ASPM) and centromere protein K (CENPK) as novel therapeutic targets for TNBC. Small-interfering RNA-mediated knockdown of their expression significantly attenuated TNBC cell viability due to G1 and G2/M cell cycle arrest. Our data will provide a better understanding of the carcinogenesis of TNBC and could contribute to the development of molecular targets as a treatment for TNBC patients.

Comprehensive phosphoproteome analysis unravels the core signaling network that initiates the earliest synapse pathology in preclinical Alzheimer's disease brain

Using a high-end mass spectrometry, we screened phosphoproteins and phosphopeptides in four types of Alzheimers disease (AD) mouse models and human AD postmortem brains. We identified commonly changed phosphoproteins in multiple models and also determined phosphoproteins related to initiation of amyloid beta (Aβ) deposition in the mouse brain. After confirming these proteins were also changed in and human AD brains, we put the proteins on experimentally verified protein-protein interaction databases. Surprisingly, most of the core phosphoproteins were directly connected, and they formed a functional network linked to synaptic spine formation. The change of the core network started at a preclinical stage even before histological Aβ deposition. Systems biology analyses suggested that phosphorylation of myristoylated alanine-rich C-kinase substrate (MARCKS) by overactivated kinases including protein kinases C and calmodulin-dependent kinases initiates synapse pathology. Two-photon microscopic observation revealed recovery of abnormal spine formation in the AD model mice by targeting a core protein MARCKS or by inhibiting candidate kinases, supporting our hypothesis formulated based on phosphoproteome analysis.

AYUMS: an algorithm for completely automatic quantitation based on LC-MS/MS proteome data and its application to the analysis of signal transduction.

Comprehensive description of the behavior of cellular components in a quantitative manner is essential for systematic understanding of biological events. Recent LC-MS/MS (tandem mass spectrometry coupled with liquid chromatography) technology, in combination with the SILAC (Stable Isotope Labeling by Amino acids in Cell culture) method, has enabled us to make relative quantitation at the proteome level. The recent report by Blagoev et al. (Nat. Biotechnol., 22, 1139–1145, 2004) indicated that this method was also applicable for the time-course analysis of cellular signaling events. Relative quatitation can easily be performed by calculating the ratio of peak intensities corresponding to differentially labeled peptides in the MS spectrum. As currently available software requires some GUI applications and is time-consuming, it is not suitable for processing large-scale proteome data. To resolve this difficulty, we developed an algorithm that automatically detects the peaks in each spectrum. Using this algorithm, we developed a software tool named AYUMS that automatically identifies the peaks corresponding to differentially labeled peptides, compares these peaks, calculates each of the peak ratios in mixed samples, and integrates them into one data sheet. This software has enabled us to dramatically save time for generation of the final report. AYUMS is a useful software tool for comprehensive quantitation of the proteome data generated by LC-MS/MS analysis. This software was developed using Java and runs on Linux, Windows, and Mac OS X. Please contact ayums@ims.u-tokyo.ac.jp if you are interested in the application. The project web page is http://www.csml.org/ayums/ .BackgroundComprehensive description of the behavior of cellular components in a quantitative manner is essential for systematic understanding of biological events. Recent LC-MS/MS (tandem mass spectrometry coupled with liquid chromatography) technology, in combination with the SILAC (Stable Isotope Labeling by Amino acids in Cell culture) method, has enabled us to make relative quantitation at the proteome level. The recent report by Blagoev et al. (Nat. Biotechnol., 22, 1139–1145, 2004) indicated that this method was also applicable for the time-course analysis of cellular signaling events. Relative quatitation can easily be performed by calculating the ratio of peak intensities corresponding to differentially labeled peptides in the MS spectrum. As currently available software requires some GUI applications and is time-consuming, it is not suitable for processing large-scale proteome data.ResultsTo resolve this difficulty, we developed an algorithm that automatically detects the peaks in each spectrum. Using this algorithm, we developed a software tool named AYUMS that automatically identifies the peaks corresponding to differentially labeled peptides, compares these peaks, calculates each of the peak ratios in mixed samples, and integrates them into one data sheet. This software has enabled us to dramatically save time for generation of the final report.ConclusionAYUMS is a useful software tool for comprehensive quantitation of the proteome data generated by LC-MS/MS analysis. This software was developed using Java and runs on Linux, Windows, and Mac OS X. Please contact ayums@ims.u-tokyo.ac.jp if you are interested in the application. The project web page is http://www.csml.org/ayums/.

Systematic reconstruction of TRANSPATH data into Cell System Markup Language

BackgroundMany biological repositories store information based on experimental study of the biological processes within a cell, such as protein-protein interactions, metabolic pathways, signal transduction pathways, or regulations of transcription factors and miRNA. Unfortunately, it is difficult to directly use such information when generating simulation-based models. Thus, modeling rules for encoding biological knowledge into system-dynamics-oriented standardized formats would be very useful for fully understanding cellular dynamics at the system level.ResultsWe 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.ConclusionBy 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.

DA 1.0

SUMMARY Data assimilation (DA) is a computational approach that estimates unknown parameters in a pathway model using time-course information. Particle filtering, the underlying method used, is a well-established statistical method that approximates the joint posterior distributions of parameters by using sequentially generated Monte Carlo samples. In this article, we report the release of Java-based software (DA 1.0) with an intuitive and user-friendly interface to allow users to carry out parameters estimation using DA. AVAILABILITY AND IMPLEMENTATION DA 1.0 was developed using Java and thus would be executable on any platform installed with JDK 6.0 (not JRE 6.0) or later. DA 1.0 is freely available for academic users and can be launched or downloaded from http://da.csml.org.

Computational Platform for Systems Biology

Chapters 4 and 5 covered the systematic method to model and simulate pathways. In this chapter, we first introduce a method for visualizing and analyzing large-scale gene networks, and then discuss further functionalities required for the research and development in Systems Biology.

PRD: A protein–RNA interaction database

Although protein–RNA interactions (PRIs) are involved in various important cellular processes, compiled data on PRIs are still limited. This contrasts with protein–protein interactions, which have been intensively recorded in public databases and subjected to network level analysis. Here, we introduce PRD, an online database of PRIs, dispersed across several sources, including scientific literature. Currently, over 10,000 interactions have been stored in PRD using PSI-MI 2.5, which is a standard model for describing detailed molecular interactions, with an emphasis on gene level data. Users can browse all recorded interactions and execute flexible keyword searches against the database via a web interface. Our database is not only a reference of PRIs, but will also be a valuable resource for studying characteristics of PRI networks. Availability PRD can be freely accessed at http://pri.hgc.jp/

Foundations of Systems Biology

Navigating safely through a wealth of genome, protein and metabolite information, as well as a host of information processing tools, without getting lost is crucial for successful research in and teaching of - molecular biology. This concise, easy-to-follow textbook/guide serves as a valuable introduction to contemporary cell biology for readers and offers insight into the key research directions in the field. It begins with an overview of existing tools for finding, designing and investigating metabolic, genetic, signalling and other network databases. This practical guide then introduces Cell Illustrator, a software tool for biological pathway modelling and simulation, developed by the authors. In-depth discussion reveals how this tool can be used for creating, analysing and simulating biological models, thereby explicating and testing current understanding of basic biological processes. Readers do not require prior knowledge of differential equations or programming. Features: Provides many helpful learning aids, such as detailed examples throughout, and exercises and solutions Designed and structured to be part of a semester-long course Discusses the computational functionalities required for Systems Biology Addresses practical issues surrounding software tools Introduces the current big bio-databases such as TRANSPATH by Biobase, and explains why and how they can be used to develop and support systems biology research Includes a CDrom containing Cell Illustrator that allows readers to create highly complex pathways and simulations Explains important pathway databases and software tools, together with their related concepts Guides the reader to model pathways in a step-by-step and clear manner Contains a Foreword written by Professor Andreas Dress, Director CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences Written for undergraduates, this reader-friendly introduction to the field of Systems Biology offers insight and teaches sound expertise in the subject. It will also prove valuable to graduate students and professionals wishing to develop and support their systems-biology research.