Layout of anatomical structures and blood vessels based on the foundational model of anatomy.

We present a method for the layout of anatomical structures and blood vessels based on information from the Foundational Model of Anatomy (FMA). Our approach integrates a novel vascular layout into the hierarchical treemap representation of anatomy as used in ApiNATOMY. Our method aims to improve the comprehension of complex anatomical and vascular data by providing readable visual representations. The effectiveness of our method is demonstrated through a prototype developed in VANTED, showing potential for application in research, education, and clinical settings.

Exploring animal behaviour multilayer networks in immersive environments - a conceptual framework.

Animal behaviour is often modelled as networks, where, for example, the nodes are individuals of a group and the edges represent behaviour within this group. Different types of behaviours or behavioural categories are then modelled as different yet connected networks which form a multilayer network. Recent developments show the potential and benefit of multilayer networks for animal behaviour research as well as the potential benefit of stereoscopic 3D immersive environments for the interactive visualisation, exploration and analysis of animal behaviour multilayer networks. However, so far animal behaviour research is mainly supported by libraries or software on 2D desktops. Here, we explore the domain-specific requirements for (stereoscopic) 3D environments. Based on those requirements, we provide a proof of concept to visualise, explore and analyse animal behaviour multilayer networks in immersive environments.

PathwayNexus: a tool for interactive metabolic data analysis.

MOTIVATION: High-throughput omics methods increasingly result in large datasets including metabolomics data, which are often difficult to analyse. RESULTS: To help researchers to handle and analyse those datasets by mapping and investigating metabolomics data of multiple sampling conditions (e.g. different time points or treatments) in the context of pathways, PathwayNexus has been developed, which presents the mapping results in a matrix format, allowing users to easily observe the relations between the compounds and the pathways. It also offers functionalities like ranking, sorting, clustering, pathway views, and further analytical tools. Its primary objective is to condense large sets of pathways into smaller, more relevant subsets that align with the specific interests of the user. AVAILABILITY AND IMPLEMENTATION: The methodology presented here is implemented in PathwayNexus, an open-source add-on for Vanted available at www.cls.uni-konstanz.de/software/pathway-nexus. CONTACT: falk.schreiber@unikonstanz.de. SUPPLEMENTARY INFORMATION: Website: www.cls.uni-konstanz.de/software/pathway-nexus.

De-Emphasise, Aggregate, and Hide: A Study of Interactive Visual Transformations for Group Structures in Network Visualisations.

Analysts often have to work with and make sense of large complex networks. One possible solution is to make visualisations interactive, providing users with a way to control visual clutter. Although several interactive methods have been proposed, there may be situations where some of them are too specific to be directly applicable. We have therefore identified several underlying low-level visual transformations, steered by group structures in the networks, and investigated their individual effects on user performance. This may both facilitate the development of further methods and support the generation of new hypotheses. We conducted an exploratory online experiment with 300 participants, involving five tasks, one control condition, and five group-based visual transformations: de-emphasising groups by opacity, position or size, aggregating groups, and hiding groups. The results for the three tasks that were specifically referring to groups show a high usage of the visual transformations by participants and several positive effects of the latter on accuracy, completion time, and mental effort spent. On the other hand, the two tasks that were not directly referring to groups show a lower usage of the visual transformations and the results regarding effects are rather mixed. Supplemental materials are available on DaRUS at https://doi.org/10.18419/darus-3706.

Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data.

Computational methods such as molecular docking or molecular dynamics (MD) simulations have been developed to simulate and explore the interactions between biomolecules. However, the interactions obtained using these methods are difficult to analyse and evaluate. Interaction fingerprints (IFPs) have been proposed to derive interactions from static 3D coordinates and transform them into 1D bit vectors. More recently, the concept has been applied to derive IFPs from MD simulations, which adds a layer of complexity by adding the temporal motion and dynamics of a system. As a result, many IFPs are obtained from one MD simulation, resulting in a large number of individual IFPs that are difficult to analyse compared to IFPs derived from static 3D structures. Scientific contribution: We introduce a new method to systematically aggregate IFPs derived from MD simulation data. In addition, we propose visualisations to effectively analyse and compare IFPs derived from MD simulation data to account for the temporal evolution of interactions and to compare IFPs across different MD simulations. This has been implemented as a freely available Python library and can therefore be easily adopted by other researchers and to different MD simulation datasets.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.