MOTIVATION: Molecular maps are graphical representations of the molecular mechanisms underlying biological systems. They are a valuable tool for curating, exchanging, and understanding biological knowledge, and may serve as a backbone for data analysis and modelling. Molecular maps are supported by a rich software ecosystem. However, there are currently no tools that support advanced programmatic analysis and processing of maps, in particular the extraction of the biological concepts they represent or their comparison. RESULTS: We introduce momapy, a generic Python library to work with molecular maps programmatically. At its core, momapy allows users to extract and separate the data model of a map from its graphical representation, and perform a variety of base operations on them, including their manipulation and comparison. momapy currently supports the SBGN and CellDesigner formats, two of the main standards to represent molecular maps graphically, and can be easily extended to support additional formats and functionalities. AVAILABILITY: momapy is implemented in Python (RRID:SCR_008394) under a GPLv3 license. The code can be downloaded freely from https://github.com/adrienrougny/momapy and is archived on Zenodo (https://doi.org/10.5281/zenodo.19088611). Complete documentation and a user manual are available at https://adrienrougny.github.io/momapy.

Graph databases are becoming increasingly popular across scientific disciplines, being highly suitable for storing and connecting complex heterogeneous data. In systems biology, they are used as a backend solution for biological data repositories, ontologies, networks, pathways, and knowledge graph databases. In this review, we analyse all publications using or mentioning graph databases retrieved from PubMed and PubMed Central full-text search, focusing on the top 16 available graph databases, Publications are categorized according to their domain and application, focusing on pathway and network biology and relevant ontologies and tools. We detail different approaches and highlight the advantages of outstanding resources, such as UniProtKB, Disease Ontology, and Reactome, which provide graph-based solutions. We discuss ongoing efforts of the systems biology community to standardize and harmonize knowledge graph creation and the maintenance of integrated resources. Outlining prospects, including the use of graph databases as a way of communication between biological data repositories, we conclude that efficient design, querying, and maintenance of graph databases will be key for knowledge generation in systems biology and other research fields with heterogeneous data.

SUMMARY: The systems biology graphical notation (SBGN) has become the de facto standard for the graphical representation of molecular maps. Having rapid and easy access to the content of large collections of maps is necessary to perform semantic or graph-based analysis of these resources. To this end, we propose StonPy, a new tool to store and query SBGN maps in a Neo4j graph database. StonPy notably includes a data model that takes into account all three SBGN languages and a completion module to automatically build valid SBGN maps from query results. StonPy is built as a library that can be integrated into other software and offers a command-line interface that allows users to easily perform all operations. AVAILABILITY AND IMPLEMENTATION: StonPy is implemented in Python 3 under a GPLv3 license. Its code and complete documentation are freely available from https://github.com/adrienrougny/stonpy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Detailed maps of the molecular basis of the disease are powerful tools for interpreting data and building predictive models. Modularity and composability are considered necessary network features for large-scale collaborative efforts to build comprehensive molecular descriptions of disease mechanisms. An effective way to create and manage large systems is to compose multiple subsystems. Composable network components could effectively harness the contributions of many individuals and enable teams to seamlessly assemble many individual components into comprehensive maps. We examine manually built versions of the RAS-RAF-MEK-ERK cascade from the Atlas of Cancer Signalling Network, PANTHER and Reactome databases and review them in terms of their reusability and composability for assembling new disease models. We identify design principles for managing complex systems that could make it easier for investigators to share and reuse network components. We demonstrate the main challenges including incompatible levels of detail and ambiguous representation of complexes and highlight the need to address these challenges.

A comprehensible representation of a molecular network is key to communicating and understanding scientific results in systems biology. The Systems Biology Graphical Notation (SBGN) has emerged as the main standard to represent such networks graphically. It has been implemented by different software tools, and is now largely used to communicate maps in scientific publications. However, learning the standard, and using it to build large maps, can be tedious. Moreover, SBGN maps are not grounded on a formal semantic layer and therefore do not enable formal analysis. Here, we introduce a new set of patterns representing recurring concepts encountered in molecular networks, called SBGN bricks. The bricks are structured in a new ontology, the Bricks Ontology (BKO), to define clear semantics for each of the biological concepts they represent. We show the usefulness of the bricks and BKO for both the template-based construction and the semantic annotation of molecular networks. The SBGN bricks and BKO can be freely explored and downloaded at sbgnbricks.org.

BACKGROUND: The temporal coordination of biological processes by the circadian clock is an important mechanism, and its disruption has negative health outcomes, including cancer. Experimental and theoretical evidence suggests that the oscillators driving the circadian clock and the cell cycle are coupled through phase locking. RESULTS: We present a detailed and documented map of known mechanisms related to the regulation of the circadian clock, and its coupling with an existing cell cycle map which includes main interactions of the mammalian cell cycle. The coherence of the merged map has been validated with a qualitative dynamics analysis. We verified that the coupled circadian clock and cell cycle maps reproduce the observed sequence of phase markers. Moreover, we predicted mutations that contribute to regulating checkpoints of the two oscillators. CONCLUSIONS: Our approach underlined the potential key role of the core clock protein NR1D1 in regulating cell cycle progression. We predicted that its activity influences negatively the progression of the cell cycle from phase G2 to M. This is consistent with the earlier experimental finding that pharmacological activation of NR1D1 inhibits tumour cell proliferation and shows that our approach can identify biologically relevant species in the context of large and complex networks.

SUMMARY: The systems biology graphical notation (SBGN) has emerged as the main standard to represent biological maps graphically. It comprises three complementary languages: Process Description, for detailed biomolecular processes; Activity Flow, for influences of biological activities and Entity Relationship, for independent relations shared among biological entities. On the other hand, TikZ is one of the most commonly used package to 'program' graphics within TEX/LATEX. Here, we present sbgntikz, a TikZ library that allows drawing and customizing SBGN maps directly into TEX/LATEX documents, using the TikZ language. sbgntikz supports all glyphs of the three SBGN languages, and offers options that facilitate the drawing of complex glyph assembly within TikZ. Furthermore, sbgntikz is provided together with a converter that allows transforming any SBGN map stored under the SBGN Markup Language into a TikZ picture, or rendering it directly into a PDF file. AVAILABILITY AND IMPLEMENTATION: sbgntikz, the SBGN-ML to sbgntikz converter, as well as a complete documentation can be freely downloaded from https://github.com/Adrienrougny/sbgntikz/. The library and the converter are compatible with all recent operating systems, including Windows, MacOS, and all common Linux distributions. SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.