Browsing by Author "Mazein, A."

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    A guide for developing comprehensive systems biology maps of disease mechanisms: planning, construction and maintenance
    (Frontiers Media S.A., 2023-06-22) Mazein, A.; Acencio, M. L.; Balaur, I.; Rougny, A.; Welter, D.; Niarakis, A.; Ramirez Ardila, D.; Doğrusöz, Uğur; Gawron, P.; Satagopam, V.; Gu, W.; Kremer, A.; Schneider, R.; Ostaszewski, M.
    As a conceptual model of disease mechanisms, a disease map integrates available knowledge and is applied for data interpretation, predictions and hypothesis generation. It is possible to model disease mechanisms on different levels of granularity and adjust the approach to the goals of a particular project. This rich environment together with requirements for high-quality network reconstruction makes it challenging for new curators and groups to be quickly introduced to the development methods. In this review, we offer a step-by-step guide for developing a disease map within its mainstream pipeline that involves using the CellDesigner tool for creating and editing diagrams and the MINERVA Platform for online visualisation and exploration. We also describe how the Neo4j graph database environment can be used for managing and querying efficiently such a resource. For assessing the interoperability and reproducibility we apply FAIR principles.
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    cd2sbgnml: Bidirectional conversion between CellDesigner and SBGN formats
    (Oxford University Press, 2020-01) Balaur, I.; Roy, L.; Mazein, A.; Karaca, S. Gökberk; Doğrusöz, Uğur; Barillot, E.; Zinovyev, A.
    Motivation: CellDesigner is a well-established biological map editor used in many large-scale scientific efforts. However, the interoperability between the Systems Biology Graphical Notation (SBGN) Markup Language (SBGNML) and the CellDesigner’s proprietary Systems Biology Markup Language (SBML) extension formats remains a challenge due to the proprietary extensions used in CellDesigner files. Results: We introduce a library named cd2sbgnml and an associated web service for bidirectional conversion between CellDesigner’s proprietary SBML extension and SBGN-ML formats. We discuss the functionality of the cd2sbgnml converter, which was successfully used for the translation of comprehensive large-scale diagrams such as the RECON Human Metabolic network and the complete Atlas of Cancer Signalling Network, from the CellDesigner file format into SBGN-ML. Availability and implementation: The cd2sbgnml conversion library and the web service were developed in Java, and distributed under the GNU Lesser General Public License v3.0. The sources along with a set of examples are available on GitHub (https://github.com/sbgn/cd2sbgnml and https://github.com/sbgn/cd2sbgnml-webservice, respectively). Supplementary information: Supplementary data are available at Bioinformatics online.
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    Community-driven roadmap for integrated disease maps
    (Oxford University Press, 2018) Ostaszewski, M.; Gebel, S.; Kuperstein, I.; Mazein, A.; Zinovyev, A.; Doğrusöz, Uğur; Hasenauer, J.; Fleming, R. M. T.; Novere, N. L.; Gawron, P.; Ligon, T.; Niarakis, A.; Nickerson, D.; Weindl, D.; Balling, R.; Barillot, E.; Auffray, C.; Schneider, R.
    The Disease Maps Project builds on a network of scientific and clinical groups that exchange best practices, share information and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation or serve as a scaffold for the generation of predictive mathematical models. This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps; sharing parts of maps that encode common human disease mechanisms; providing technical solutions for complexity management of maps; and Web tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.
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    Newt: a comprehensive web-based tool for viewing, constructing and analyzing biological maps
    (Oxford University Press, 2021-05-15) Balcı, Hasan; Siper, Metin Can; Saleh, Nasim; Safarli, İlkin; Roy, L.; Kılıçarslan, Merve; Özaydın, Rümeysa; Mazein, A.; Auffray, C.; Babur, Ö.; Demir, E.; Doğrusöz, Uğur
    Motivation Visualization of cellular processes and pathways is a key recurring requirement for effective biological data analysis. There is a considerable need for sophisticated web-based pathway viewers and editors operating with widely accepted standard formats, using the latest visualization techniques and libraries. Results We developed a web-based tool named Newt for viewing, constructing and analyzing biological maps in standard formats such as SBGN, SBML and SIF. Availability and implementation Newt’s source code is publicly available on GitHub and freely distributed under the GNU LGPL. Ample documentation on Newt can be found on http://newteditor.org and on YouTube.
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    Systems biology graphical notation markup language (SBGNML) version 0.3
    (De Gruyter, 2020) Luna, A.; Bergmann, F. T.; Czauderna, T.; Doğrusöz, Uğur; Rougny, A.; Dräger, A.; Touré, V.; Mazein, A.; Mazein, M. L.
    This document defines Version 0.3 Markup Language (ML) support for the Systems Biology Graphical Notation (SBGN), a set of three complementary visual languages developed for biochemists, modelers, and computer scientists. SBGN aims at representing networks of biochemical interactions in a standard, unambiguous way to 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. SBGN is defined neutrally to programming languages and software encoding; however, it is oriented primarily towards allowing models to be encoded using XML, the eXtensible Markup Language. The notable changes from the previous version include the addition of attributes for better specify metadata about maps, as well as support for multiple maps, sub-maps, colors, and annotations. These changes enable a more efficient exchange of data to other commonly used systems biology formats (e. g., BioPAX and SBML) and between tools supporting SBGN (e. g., CellDesigner, Newt, Krayon, SBGN-ED, STON, cd2sbgnml, and MINERVA). More details on SBGN and related software are available at http://sbgn.org. 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.
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    Systems medicine disease maps: community-driven comprehensive representation of disease mechanisms
    (Nature Publishing Group, 2018) Mazein, A.; Ostaszewski, M.; Kuperstein, I.; Watterson, S.; Le Novère, N.; Lefaudeux, D.; De Meulder, B.; Pellet, J.; Balaur, I.; Saqi, M.; Nogueira, M. M.; He, F.; Parton, A.; Lemonnier, N.; Gawron, P.; Gebel, S.; Hainaut, P.; Ollert, M.; Doğrusöz, Uğur; Barillot, E.; Zinovyev, A.; Schneider, R.; Balling, R.; Auffray, C.
    The development of computational approaches in systems biology has reached a state of maturity that allows their transition to systems medicine. Despite this progress, intuitive visualisation and context-dependent knowledge representation still present a major bottleneck. In this paper, we describe the Disease Maps Project, an effort towards a community-driven computationally readable comprehensive representation of disease mechanisms. We outline the key principles and the framework required for the success of this initiative, including use of best practices, standards and protocols. We apply a modular approach to ensure efficient sharing and reuse of resources for projects dedicated to specific diseases. Community-wide use of disease maps will accelerate the conduct of biomedical research and lead to new disease ontologies defined from mechanism-based disease endotypes rather than phenotypes.