KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース)

Modern systems biology is already contributing to a radical transformation of molecular life sciences and biomedicine, and it is expected to have a real impact in the clinical setting in the next years. In this review, the emergence of systems biology is contextualized with a historic overview, and its present state is depicted. The present and expected future contribution of systems biology to the development of molecular medicine is underscored. Concerning the present situation, this review includes a reflection on the "inflation" of biological data and the urgent need for tools and procedures to make hidden information emerge. Descriptions of the impact of networks and models and the available resources and tools for applying them in systems biology approaches to molecular medicine are provided as well. The actual current impact of systems biology in molecular medicine is illustrated, reviewing two cases, namely, those of systems pharmacology and cancer systems biology. Finally, some of the expected contributions of systems biology to the immediate future of molecular medicine are commented.

Systems biology for molecular life sciences and its impact in biomedicine.

BACKGROUND: Meningioma is one of the most common central nervous system tumors derived from meningothelial (arachnoid cap) cells. This paper identiied the network-based Protein-Protein Interactions (PPI) for meningioma compared to healthy controls. METHODS: Gene expression data, including 384 gene or protein names, were extracted from a number of previous investigations. Out of these 384 proteins, 176 were found to be exclusively expressed in meningiomas and 208 proteins were down-regulated. The networks of related differentially expressed genes were explored using cytoscape and the PPI analysis methods such as MCODE and ClueGO. RESULTS: Analysis introduced a number of hub proteins and 27 clusters (protein complex) with distinctive seed genes. Identiied ClueGO Pathways based on subnetworks mined by MCODE was composed of positive regulation in RBC homeostasis, dysregulation of transport from ER to Golgi, disruption regulation of cell cycle and antigen processing and presentation of exogenous peptide antigen and neutraliza- tion of exogenous dsRNA. Combination of over-expression of TCEA1, UBE2E1, XRCC5, IFIT1, IFIT-3, MCM2, and MCM7 and under- expression of CDC25A, SEC31A, and CDK6 can serve as a diagnostic biomarker panel for meningiomas. CONCLUSION: These proposed network-based biomarkers for the meningioma patterns may be helpful in diagnosis, prognosis and treat- ment processes, although biomarker validation is necessary.

Meningioma protein-protein interaction network.

Background: Biology is moving fast toward the virtuous circle of other disciplines: from data to quantitative modeling and back to data. Models are usually developed by mathematicians, physicists, and computer scientists to translate qualitative or semi-quantitative biological knowledge into a quantitative approach. To eliminate semantic confusion between biology and other disciplines, it is necessary to have a list of the most important and frequently used concepts coherently defined. Results: We propose a novel paradigm for generating new concepts for an ontology, starting from model rather than developing a database. We apply that approach to generate concepts for cell and molecule interaction starting from an agent based model. This effort provides a solid infrastructure that is useful to overcome the semantic ambiguities that arise between biologists and mathematicians, physicists, and computer scientists, when they interact in a multidisciplinary field. Conclusions: This effort represents the first attempt at linking molecule ontology with cell ontology, in IMGTONTOLOGY, the well established ontology in immunogenetics and immunoinformatics, and a paradigm for life science biology. With the increasing use of models in biology and medicine, the need to link different levels, from molecules to cells to tissues and organs, is increasingly important.

/pdf/a-novel-paradigm-for-cell-and-molecule-interaction-ontology-12d5q3xmn8.pdf

A novel paradigm for cell and molecule interaction ontology: from the CMM model to IMGT-ONTOLOGY

Pathogenic organisms, causes of various infectious diseases, possess a rich repository of antigenic proteins that engender an immune response in a host. These types of diseases are usually treated with the use of pharmaceuticals; unfortunately, many of these also have a potential to induce fatal side effects, especially allergic responses in the diseased host. In addition, many pathogens evolve (by selective survival) single or multi-drug resistance (MDR). Therefore, a means to prevent the host from becoming susceptible to the pathogen from the onset, rather than trying to devise pharmacologic protocols to treat an ongoing infection, are increasingly seen as desirable to reduce the incidence of infectious diseases altogether. To this end, cost-effective development and use of “safe” vaccines is key. This paper provides an overview on the new and expanding area of computational vaccinology and a brief background on pathogen antigenicity, identification of pathogen-specific antigens, and screening of candidate antigens using various tools and databases developed in the recent past.

/pdf/overview-of-computational-vaccinology-vaccine-development-14nasgdjtn.pdf

Overview of computational vaccinology: vaccine development through information technology.

Summary: We present Systems Biology Metabolic Modeling Assistant (SBMM Assistant), a tool built using an ontology-based mediator, and designed to facilitate metabolic modeling through the integration of data from repositories that contain valuable metabolic information. This software can be used for the visualization, design and management of metabolic networks; selection, integration and storage of metabolic information; and as an assistant for kinetic modeling.

Availability: SBMM Assistant for academic use is freely available at http://www.sbmm.uma.es.

Contact: [email protected]

/pdf/systems-biology-metabolic-modeling-assistant-2kyo3zxr25.pdf

Systems biology metabolic modeling assistant

Protein-protein interactions can be considered the basic skeleton for living organism self-organization and homeostasis. Impressive quantities of experimental data are being obtained and computational tools are essential to integrate and to organize this information. This paper presents Protopia, a biological tool that offers a way of searching for proteins and their interactions in different Protein Interaction Web Databases, as a part of a multidisciplinary initiative of our institution for the integration of biological data http://asp.uma.es
 . The tool accesses the different Databases (at present, the free version of Transfac, DIP, Hprd, Int-Act and iHop), and results are expressed with biological protein names or databases codes and can be depicted as a vector or a matrix. They can be represented and handled interactively as an organic graph. Comparison among databases is carried out using the Uniprot codes annotated for each protein. The tool locates and integrates the current information stored in the aforementioned databases, and redundancies among them are detected. Results are compatible with the most important network analysers, so that they can be compared and analysed by other world-wide known tools and platforms. The visualization possibilities help to attain this goal and they are especially interesting for handling multiple-step or complex networks.

/pdf/protopia-a-protein-protein-interaction-tool-rv5xpwe3c3.pdf

Protopia: a protein-protein interaction tool.

Background
The analysis of information in the biological domain is usually focused on the analysis of data from single on-line data sources. Unfortunately, studying a biological process requires having access to disperse, heterogeneous, autonomous data sources. In this context, an analysis of the information is not possible without the integration of such data.

/pdf/ka-sb-from-data-integration-to-large-scale-reasoning-2gv41eeevi.pdf

KA-SB: from data integration to large scale reasoning.

The emergence of the Semantic Web has produced several changes in the way in which applications are developed. Besides, the Semantic Web idea has changed itself since its initial appearance. Thus, the most significance change in the most recent proposal of the layer of protocols for the Semantic Web is the use of RDF for data interchange. In this sense, this paper presents a proposal for integrating data sources capable of providing their data as RDF documents. Thus, the use of an ontology-based mediator is able to integrate RDF data taking advantage of a domain ontology.

/pdf/a-semantic-mediation-architecture-for-rdf-data-integration-3acrx86jom.pdf

A Semantic Mediation Architecture for RDF Data Integration.

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Papers

Systems biology metabolic modeling assistant

Protopia: a protein-protein interaction tool.

KA-SB: from data integration to large scale reasoning.

KA-SB: from data integration to large scale reasoning.

A Semantic Mediation Architecture for RDF Data Integration.