Transcriptional complex assembly represented in SBGN PD

This poster shows how transcriptional complex assembly can be represented in SBGN Process Description language. Example: LPS-induced TNF-alpha enhancer complex formation

Metabolic Network Representation in SBGN PD: EC and Identity Gate

This presentations describes proposed changes in SBGN PD that would allow this language to be better adopted for representing metabolic network

Visual representation of cellular networks

Development of advanced techniques for biological network visualisation is crucial for successful progress in the areas of systems-level biology and data-intensive bioinformatics. However, current techniques for biological network visualisation fall short of expectations for representing extensive biological networks. In order to provide really useful network visualisation tools, new approaches have to be proposed and applied alongside with those most powerful features of current visualisation systems. The resulting representation techniques have to be tested by applying to large-scale examples that would include metabolic, signaling and gene expression events. User survey should also be carried out to further prove the advantages of the new techniques. The present thesis describes an attempt to achieve the above objectives, by performing the following steps: 1) existing approaches in the area of network representation were analyzed and their shortcomings and advantages were defined; 2) new notation has been developed, in which, the defined best features of the existing systems were integrated with newly introduced potent features such as compact visualization, ‘functional gate’ and ‘identity gate’, 4) new framework was developed that allows managing large-scale networks that are represented on different levels of details and different levels of constrains, while keeping each diagram semantically unambiguous, 5) extensive examples, including genome-scaled human metabolic network and TNF-alpha receptor signalling network, were used to prove that the designed notation and the framework can be applied efficiently, and, finally, 6) a notation survey has been carried out to validate the advantages of the newly developed notation over the existing ones

Upper respiratory tract mucosal immunity for SARS-CoV-2 vaccines

SARS-CoV-2 vaccination significantly reduces morbidity and mortality, but has less impact on viral transmission rates, thus aiding viral evolution; and the longevity of vaccine-induced immunity rapidly declines. Immune responses in respiratory tract mucosal tissues are crucial for early control of infection, and can generate long-term antigen-specific protection with prompt recall responses. However, currently approved SARS-CoV-2 vaccines are not amenable to adequate respiratory mucosal delivery, particularly in the upper airways, which could account for the high vaccine breakthrough infection rates and limited duration of vaccine-mediated protection. In view of these drawbacks, we outline a strategy that has the potential to enhance both the efficacy and durability of existing SARS-CoV-2 vaccines, by inducing robust memory responses in the upper respiratory tract mucosa

Severe COVID-19 versus multisystem inflammatory syndrome:comparing two critical outcomes of SARS-CoV-2 infection

peer reviewedSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is associated with diverse host response immunodynamics and variable inflammatory manifestations. Several immune-modulating risk factors can contribute to a more severe coronavirus disease 2019 (COVID-19) course with increased morbidity and mortality. The comparatively rare post-infectious multisystem inflammatory syndrome (MIS) can develop in formerly healthy individuals, with accelerated progression to life-threatening illness. A common trajectory of immune dysregulation forms a continuum of the COVID-19 spectrum and MIS; however, severity of COVID-19 or the development of MIS is dependent on distinct aetiological factors that produce variable host inflammatory responses to infection with different spatiotemporal manifestations, a comprehensive understanding of which is necessary to set better targeted therapeutic and preventative strategies for both

The Edinburgh human metabolic network reconstruction and its functional analysis

A better understanding of human metabolism and its relationship with diseases is an important task in human systems biology studies. In this paper, we present a high-quality human metabolic network manually reconstructed by integrating genome annotation information from different databases and metabolic reaction information from literature. The network contains nearly 3000 metabolic reactions, which were reorganized into about 70 human-specific metabolic pathways according to their functional relationships. By analysis of the functional connectivity of the metabolites in the network, the bow-tie structure, which was found previously by structure analysis, is reconfirmed. Furthermore, the distribution of the disease related genes in the network suggests that the IN (substrates) subset of the bow-tie structure has more flexibility than other parts

GraphML-SBGN bidirectional converter for metabolic networks.

peer reviewedSystems biology researchers need feasible solutions for editing and visualisation of large biological diagrams. Here, we present the ySBGN bidirectional converter that translates metabolic pathways, developed in the general-purpose yEd Graph Editor (using the GraphML format) into the Systems Biology Graphical Notation Markup Language (SBGN-ML) standard format and vice versa. We illustrate the functionality of this converter by applying it to the translation of the ReconMap resource (available in the SBGN-ML format) to the yEd-specific GraphML and back. The ySBGN tool makes possible to draw extensive metabolic diagrams in a powerful general-purpose graph editor while providing results in the standard SBGN format

Reusability and composability in process description maps: RAS-RAF-MEK-ERK signalling.

peer reviewedDetailed 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

cd2sbgnml: bidirectional conversion between CellDesigner and SBGN formats.

peer reviewed[en] 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 (SBGN-ML) 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