MINERVA-a platform for visualization and curation of molecular interaction networks

Cohort-specific boolean models highlight different regulatory modules during Parkinson's disease progression

Parkinson's disease (PD) involves complex molecular interactions and diverse comorbidities. To better understand its molecular mechanisms, we employed systems medicine approaches using the PD map, a detailed repository of PD-related interactions and applied Probabilistic Boolean Networks (PBNs) to capture the stochastic nature of molecular dynamics. By integrating cohort-level and real-world patient data, we modeled PD's subtype-specific pathway deregulations, providing a refined representation of its molecular landscape. Our study identifies key regulatory biomolecules and pathways that vary across PD subtypes, offering insights into the disease's progression and patient stratification. These findings have significant implications for the development of targeted therapeutic interventions.

Comprehensive molecular interaction map of TGFβ induced epithelial to mesenchymal transition in breast cancer

Breast cancer is one of the prevailing cancers globally, with a high mortality rate. Metastatic breast cancer (MBC) is an advanced stage of cancer, characterised by a highly nonlinear, heterogeneous process involving numerous singling pathways and regulatory interactions. Epithelial-mesenchymal transition (EMT) emerges as a key mechanism exploited by cancer cells. Transforming Growth Factor-β (TGFβ)-dependent signalling is attributed to promote EMT in advanced stages of breast cancer. A comprehensive regulatory map of TGFβ induced EMT was developed through an extensive literature survey. The network assembled comprises of 312 distinct species (proteins, genes, RNAs, complexes), and 426 reactions (state transitions, nuclear translocations, complex associations, and dissociations). The map was developed by following Systems Biology Graphical Notation (SBGN) using Cell Designer and made publicly available using MINERVA ( http://35.174.227.105:8080/minerva/?id=Metastatic_Breast_Cancer_1 ). While the complete molecular mechanism of MBC is still not known, the map captures the elaborate signalling interplay of TGFβ induced EMT-promoting MBC. Subsequently, the disease map assembled was translated into a Boolean model utilising CaSQ and analysed using Cell Collective. Simulations of these have captured the known experimental outcomes of TGFβ induced EMT in MBC. Hub regulators of the assembled map were identified, and their transcriptome-based analysis confirmed their role in cancer metastasis. Elaborate analysis of this map may help in gaining additional insights into the development and progression of metastatic breast cancer.

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.

A mathematical model of Familial Mediterranean Fever predicts mechanisms controlling inflammation

BACKGROUND

Familial Mediterranean Fever (FMF) is a monogenic disease caused by gain-of-function mutations in the MEditerranean FeVer (MEFV) gene. The molecular dysregulations induced by these mutations and the associated causal mechanisms are complex and intricate.

OBJECTIVE

We sought to provide a computational model capturing the mechanistic details of biological pathways involved in FMF physiopathology and enabling the study of the patient's immune cell dynamics.

METHODS

We carried out a literature survey to identify experimental studies published from January 2000 to December 2020, and integrated its results into a molecular map and a mathematical model. Then, we studied the network of molecular interactions and the dynamic of monocytes to identify key players for inflammation phenotype in FMF patients.

RESULTS

We built a molecular map of FMF integrating in a structured manner the current knowledge regarding pathophysiological processes participating in the triggering and perpetuation of the disease flares. The mathematical model derived from the map reproduced patient's monocyte behavior, in particular its proinflammatory role via the Pyrin inflammasome activation. Network analysis and in silico experiments identified NF-κB and JAK1/TYK2 as critical to modulate IL-1β- and IL-18-mediated inflammation.

CONCLUSION

The in silico model of FMF monocyte proved its ability to reproduce in vitro observations. Considering the difficulties related to experimental settings and financial investments to test combinations of stimuli/perturbation in vitro, this model could be used to test complex hypotheses in silico, thus narrowing down the number of in vitro and ex vivo experiments to perform.

The SYSCID map: a graphical and computational resource of molecular mechanisms across rheumatoid arthritis, systemic lupus erythematosus and inflammatory bowel disease

Chronic inflammatory diseases (CIDs), including inflammatory bowel disease (IBD), rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) are thought to emerge from an impaired complex network of inter- and intracellular biochemical interactions among several proteins and small chemical compounds under strong influence of genetic and environmental factors. CIDs are characterised by shared and disease-specific processes, which is reflected by partially overlapping genetic risk maps and pathogenic cells (e.g., T cells). Their pathogenesis involves a plethora of intracellular pathways. The translation of the research findings on CIDs molecular mechanisms into effective treatments is challenging and may explain the low remission rates despite modern targeted therapies. Modelling CID-related causal interactions as networks allows us to tackle the complexity at a systems level and improve our understanding of the interplay of key pathways. Here we report the construction, description, and initial applications of the SYSCID map (https://syscid.elixir-luxembourg.org/), a mechanistic causal interaction network covering the molecular crosstalk between IBD, RA and SLE. We demonstrate that the map serves as an interactive, graphical review of IBD, RA and SLE molecular mechanisms, and helps to understand the complexity of omics data. Examples of such application are illustrated using transcriptome data from time-series gene expression profiles following anti-TNF treatment and data from genome-wide associations studies that enable us to suggest potential effects to altered pathways and propose possible mechanistic biomarkers of treatment response.

SBcoyote: An extensible Python-based reaction editor and viewer

SBcoyote is an open-source cross-platform biochemical reaction viewer and editor released under the liberal MIT license. It is written in Python and uses wxPython to implement the GUI and the drawing canvas. It supports the visualization and editing of compartments, species, and reactions. It includes many options to stylize each of these components. For instance, species can be in different colors and shapes. Other core features include the ability to create alias nodes, alignment of groups of nodes, network zooming, as well as an interactive bird-eye view of the network to allow easy navigation on large networks. A unique feature of the tool is the extensive Python plugin API, where third-party developers can include new functionality. To assist third-party plugin developers, we provide a variety of sample plugins, including, random network generation, a simple auto layout tool, export to Antimony, export SBML, import SBML, etc. Of particular interest are the export and import SBML plugins since these support the SBML level 3 layout and render standard, which is exchangeable with other software packages. Plugins are stored in a GitHub repository, and an included plugin manager can retrieve and install new plugins from the repository on demand. Plugins have version metadata associated with them to make it install plugin updates. Availability: https://github.com/sys-bio/SBcoyote.

Hybrid computational modeling highlights reverse warburg effect in breast cancer-associated fibroblasts

Cancer-associated fibroblasts (CAFs) are amongst the key players of the tumor microenvironment (TME) and are involved in cancer initiation, progression, and resistance to therapy. They exhibit aggressive phenotypes affecting extracellular matrix remodeling, angiogenesis, immune system modulation, tumor growth, and proliferation. CAFs phenotypic changes appear to be associated with metabolic alterations, notably a reverse Warburg effect that may drive fibroblasts transformation. However, its precise molecular mechanisms and regulatory drivers are still under investigation. Deciphering the reverse Warburg effect in breast CAFs may contribute to a better understanding of the interplay between TME and tumor cells, leading to new treatment strategies. In this regard, dynamic modeling approaches able to span multiple biological layers are essential to capture the emergent properties of various biological entities when complex and intertwined pathways are involved. This work presents the first hybrid large-scale computational model for breast CAFs covering major cellular signaling, gene regulation, and metabolic processes. It was generated by combining a cell- and disease-specific asynchronous Boolean model with a generic core metabolic network leveraging both data-driven and manual curation approaches. This model reproduces the experimentally observed reverse Warburg effect in breast CAFs and further identifies Hypoxia-Inducible Factor 1 (HIF-1) as its key molecular driver. Targeting HIF-1 as part of a TME-centered therapeutic strategy may prove beneficial in the treatment of breast cancer by addressing the reverse Warburg effect. Such findings in CAFs, in light of our previously published results in rheumatoid arthritis synovial fibroblasts, point to a common HIF-1-driven metabolic reprogramming of fibroblasts in breast cancer and rheumatoid arthritis.

Visualization of automatically combined disease maps and pathway diagrams for rare diseases

Introduction: Investigation of molecular mechanisms of human disorders, especially rare diseases, require exploration of various knowledge repositories for building precise hypotheses and complex data interpretation. Recently, increasingly more resources offer diagrammatic representation of such mechanisms, including disease-dedicated schematics in pathway databases and disease maps. However, collection of knowledge across them is challenging, especially for research projects with limited manpower. Methods: In this article we present an automated workflow for construction of maps of molecular mechanisms for rare diseases. The workflow requires a standardized definition of a disease using Orphanet or HPO identifiers to collect relevant genes and variants, and to assemble a functional, visual repository of related mechanisms, including data overlays. The diagrams composing the final map are unified to a common systems biology format from CellDesigner SBML, GPML and SBML+layout+render. The constructed resource contains disease-relevant genes and variants as data overlays for immediate visual exploration, including embedded genetic variant browser and protein structure viewer. Results: We demonstrate the functionality of our workflow on two examples of rare diseases: Kawasaki disease and retinitis pigmentosa. Two maps are constructed based on their corresponding identifiers. Moreover, for the retinitis pigmentosa use-case, we include a list of differentially expressed genes to demonstrate how to tailor the workflow using omics datasets. Discussion: In summary, our work allows for an ad-hoc construction of molecular diagrams combined from different sources, preserving their layout and graphical style, but integrating them into a single resource. This allows to reduce time consuming tasks of prototyping of a molecular disease map, enabling visual exploration, hypothesis building, data visualization and further refinement. The code of the workflow is open and accessible at https://gitlab.lcsb.uni.lu/minerva/automap/.

A guide for developing comprehensive systems biology maps of disease mechanisms: planning, construction and maintenance

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.

Applications of Boolean modeling to study the dynamics of a complex disease and therapeutics responses

Computational modeling has emerged as a critical tool in investigating the complex molecular processes involved in biological systems and diseases. In this study, we apply Boolean modeling to uncover the molecular mechanisms underlying Parkinson's disease (PD), one of the most prevalent neurodegenerative disorders. Our approach is based on the PD-map, a comprehensive molecular interaction diagram that captures the key mechanisms involved in the initiation and progression of PD. Using Boolean modeling, we aim to gain a deeper understanding of the disease dynamics, identify potential drug targets, and simulate the response to treatments. Our analysis demonstrates the effectiveness of this approach in uncovering the intricacies of PD. Our results confirm existing knowledge about the disease and provide valuable insights into the underlying mechanisms, ultimately suggesting potential targets for therapeutic intervention. Moreover, our approach allows us to parametrize the models based on omics data for further disease stratification. Our study highlights the value of computational modeling in advancing our understanding of complex biological systems and diseases, emphasizing the importance of continued research in this field. Furthermore, our findings have potential implications for the development of novel therapies for PD, which is a pressing public health concern. Overall, this study represents a significant step forward in the application of computational modeling to the investigation of neurodegenerative diseases, and underscores the power of interdisciplinary approaches in tackling challenging biomedical problems.

Exploration and comparison of molecular mechanisms across diseases using MINERVA Net

Protein function is often interpreted using molecular interaction diagrams, encoding roles a given protein plays in various molecular mechanisms. Information about disease-related mechanisms can be inferred from disease maps, knowledge repositories containing manually constructed systems biology diagrams. Disease maps hosted on the Molecular Interaction Network VisuAlization (MINERVA) Platform are individually accessible through a REST API interface of each instance, making it challenging to systematically explore their contents. To address this challenge, we introduce the MINERVA Net web service, a repository of open-access disease maps allowing users to publicly share minimal information about their maps. The MINERVA Net repository provides REST API endpoints of particular disease maps, which then can be individually queried for content. In this article, we describe the concept of MINERVA Net and illustrate its use by comparing proteins and their interactions in three different disease maps.

Scott W, Sheriff RSM, Suarez Diez M, Van Steen K, Westerhoff HV, Wittig U, Wolstencroft K, Zupanic A, Evelo CT, Hancock JM. Systems Biology in ELIXIR: modelling in the spotlight

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

Scott W, Sheriff RSM, Suarez Diez M, Van Steen K, Westerhoff HV, Wittig U, Wolstencroft K, Zupanic A, Evelo CT, Hancock JM. Systems Biology in ELIXIR: modelling in the spotlight

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

In silico investigation of molecular networks linking gastrointestinal diseases, malnutrition, and sarcopenia

Malnutrition (MN) is a common primary or secondary complication in gastrointestinal diseases. The patient’s nutritional status also influences muscle mass and function, which can be impaired up to the degree of sarcopenia. The molecular interactions in diseases leading to sarcopenia are complex and multifaceted, affecting muscle physiology, the intestine (nutrition), and the liver at different levels. Although extensive knowledge of individual molecular factors is available, their regulatory interplay is not yet fully understood. A comprehensive overall picture of pathological mechanisms and resulting phenotypes is lacking. In silico approaches that convert existing knowledge into computationally readable formats can help unravel mechanisms, underlying such complex molecular processes. From public literature, we manually compiled experimental evidence for molecular interactions involved in the development of sarcopenia into a knowledge base, referred to as the Sarcopenia Map. We integrated two diseases, namely liver cirrhosis (LC), and intestinal dysfunction, by considering their effects on nutrition and blood secretome. We demonstrate the performance of our model by successfully simulating the impact of changing dietary frequency, glycogen storage capacity, and disease severity on the carbohydrate and muscle systems. We present the Sarcopenia Map as a publicly available, open-source, and interactive online resource, that links gastrointestinal diseases, MN, and sarcopenia. The map provides tools that allow users to explore the information on the map and perform in silico simulations.

Boolean modelling as a logic-based dynamic approach in systems medicine

Molecular mechanisms of health and disease are often represented as systems biology diagrams, and the coverage of such representation constantly increases. These static diagrams can be transformed into dynamic models, allowing for in silico simulations and predictions. Boolean modelling is an approach based on an abstract representation of the system. It emphasises the qualitative modelling of biological systems in which each biomolecule can take two possible values: zero for absent or inactive, one for present or active. Because of this approximation, Boolean modelling is applicable to large diagrams, allowing to capture their dynamic properties. We review Boolean models of disease mechanisms and compare a range of methods and tools used for analysis processes. We explain the methodology of Boolean analysis focusing on its application in disease modelling. Finally, we discuss its practical application in analysing signal transduction and gene regulatory pathways in health and disease.

Network- and enrichment-based inference of phenotypes and targets from large-scale disease maps

Complex diseases are inherently multifaceted, and the associated data are often heterogeneous, making linking interactions across genes, metabolites, RNA, proteins, cellular functions, and clinically relevant phenotypes a high-priority challenge. Disease maps have emerged as knowledge bases that capture molecular interactions, disease-related processes, and disease phenotypes with standardized representations in large-scale molecular interaction maps. Various tools are available for disease map analysis, but an intuitive solution to perform in silico experiments on the maps in a wide range of contexts and analyze high-dimensional data is currently missing. To this end, we introduce a two-dimensional enrichment analysis (2DEA) approach to infer downstream and upstream elements through the statistical association of network topology parameters and fold changes from molecular perturbations. We implemented our approach in a plugin suite for the MINERVA platform, providing an environment where experimental data can be mapped onto a disease map and predict potential regulatory interactions through an intuitive graphical user interface. We show several workflows using this approach and analyze two RNA-seq datasets in the Atlas of Inflammation Resolution (AIR) to identify enriched downstream processes and upstream transcription factors. Our work improves the usability of disease maps and increases their functionality by facilitating multi-omics data integration and exploration.

Neurodegeneration and neuroinflammation are linked, but independent of alpha‐synuclein inclusions, in a seeding/spreading mouse model of Parkinson's disease

A key pathological process in Parkinson's disease (PD) is the transneuronal spreading of α‐synuclein. Alpha‐synuclein (α‐syn) is a presynaptic protein that, in PD, forms pathological inclusions. Other hallmarks of PD include neurodegeneration and microgliosis in susceptible brain regions. Whether it is primarily transneuronal spreading of α‐syn particles, inclusion formation, or other mechanisms, such as inflammation, that cause neurodegeneration in PD is unclear. We used a model of spreading of α‐syn induced by striatal injection of α‐syn preformed fibrils into the mouse striatum to address this question. We performed quantitative analysis for α‐syn inclusions, neurodegeneration, and microgliosis in different brain regions, and generated gene expression profiles of the ventral midbrain, at two different timepoints after disease induction. We observed significant neurodegeneration and microgliosis in brain regions not only with, but also without α‐syn inclusions. We also observed prominent microgliosis in injured brain regions that did not correlate with neurodegeneration nor with inclusion load. Using longitudinal gene expression profiling, we observed early gene expression changes, linked to neuroinflammation, that preceded neurodegeneration, indicating an active role of microglia in this process. Altered gene pathways overlapped with those typical of PD. Our observations indicate that α‐syn inclusion formation is not the major driver in the early phases of PD‐like neurodegeneration, but that microglia, activated by diffusible, oligomeric α‐syn, may play a key role in this process. Our findings uncover new features of α‐syn induced pathologies, in particular microgliosis, and point to the necessity for a broader view of the process of α‐syn spreading.

CyFi-MAP: an interactive pathway-based resource for cystic fibrosis

Cystic fibrosis (CF) is a life-threatening autosomal recessive disease caused by more than 2100 mutations in the CF transmembrane conductance regulator (CFTR) gene, generating variability in disease severity among individuals with CF sharing the same CFTR genotype. Systems biology can assist in the collection and visualization of CF data to extract additional biological significance and find novel therapeutic targets. Here, we present the CyFi-MAP-a disease map repository of CFTR molecular mechanisms and pathways involved in CF. Specifically, we represented the wild-type (wt-CFTR) and the F508del associated processes (F508del-CFTR) in separate submaps, with pathways related to protein biosynthesis, endoplasmic reticulum retention, export, activation/inactivation of channel function, and recycling/degradation after endocytosis. CyFi-MAP is an open-access resource with specific, curated and continuously updated information on CFTR-related pathways available online at https://cysticfibrosismap.github.io/ . This tool was developed as a reference CF pathway data repository to be continuously updated and used worldwide in CF research.

Mapping Compound Databases to Disease Maps-A MINERVA Plugin for CandActBase

The MINERVA platform is currently the most widely used platform for visualizing and providing access to disease maps. Disease maps are systems biological maps of molecular interactions relevant in a certain disease context, where they can be used to support drug discovery. For this purpose, we extended MINERVA's own drug and chemical search using the MINERVA plugin starter kit. We developed a plugin to provide a linkage between disease maps in MINERVA and application-specific databases of candidate therapeutics. The plugin has three main functionalities; one shows all the targets of all the compounds in the database, the second is a compound-based search to highlight targets of specific compounds, and the third can be used to find compounds that affect a certain target. As a use case, we applied the plugin to link a disease map and compound database we previously established in the context of cystic fibrosis and, herein, point out possible issues and difficulties. The plugin is publicly available on GitLab; the use-case application to cystic fibrosis, connecting disease maps and the compound database CandActCFTR, is available online.

Simulating Metabolic Flexibility in Low Energy Expenditure Conditions Using Genome-Scale Metabolic Models

Metabolic flexibility is the ability of an organism to adapt its energy source based on nutrient availability and energy requirements. In humans, this ability has been linked to cardio-metabolic health and healthy aging. Genome-scale metabolic models have been employed to simulate metabolic flexibility by computing the Respiratory Quotient (RQ), which is defined as the ratio of carbon dioxide produced to oxygen consumed, and varies between values of 0.7 for pure fat metabolism and 1.0 for pure carbohydrate metabolism. While the nutritional determinants of metabolic flexibility are known, the role of low energy expenditure and sedentary behavior in the development of metabolic inflexibility is less studied. In this study, we present a new description of metabolic flexibility in genome-scale metabolic models which accounts for energy expenditure, and we study the interactions between physical activity and nutrition in a set of patient-derived models of skeletal muscle metabolism in older adults. The simulations show that fuel choice is sensitive to ATP consumption rate in all models tested. The ability to adapt fuel utilization to energy demands is an intrinsic property of the metabolic network.

Humans and machines in biomedical knowledge curation: hypertrophic cardiomyopathy molecular mechanisms' representation

Background

Biomedical knowledge is dispersed in scientific literature and is growing constantly. Curation is the extraction of knowledge from unstructured data into a computable form and could be done manually or automatically. Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease, with genotype–phenotype associations still incompletely understood. We compared human- and machine-curated HCM molecular mechanisms’ models and examined the performance of different machine approaches for that task.

Results

We created six models representing HCM molecular mechanisms using different approaches and made them publicly available, analyzed them as networks, and tried to explain the models’ differences by the analysis of factors that affect the quality of machine-curated models (query constraints and reading systems’ performance). A result of this work is also the Interactive HCM map, the only publicly available knowledge resource dedicated to HCM. Sizes and topological parameters of the networks differed notably, and a low consensus was found in terms of centrality measures between networks. Consensus about the most important nodes was achieved only with respect to one element (calcium). Models with a reduced level of noise were generated and cooperatively working elements were detected. REACH and TRIPS reading systems showed much higher accuracy than Sparser, but at the cost of extraction performance. TRIPS proved to be the best single reading system for text segments about HCM, in terms of the compromise between accuracy and extraction performance.

Conclusions

Different approaches in curation can produce models of the same disease with diverse characteristics, and they give rise to utterly different conclusions in subsequent analysis. The final purpose of the model should direct the choice of curation techniques. Manual curation represents the gold standard for information extraction in biomedical research and is most suitable when only high-quality elements for models are required. Automated curation provides more substance, but high level of noise is expected. Different curation strategies can reduce the level of human input needed. Biomedical knowledge would benefit overwhelmingly, especially as to its rapid growth, if computers were to be able to assist in analysis on a larger scale.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13040-021-00279-2.

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling

Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. Logical regulatory networks can be used to predict biological and cellular behaviors by system perturbations and in silico simulations. Today, broad sets of causal interactions are available in a variety of biological knowledge resources. However, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. It can therefore be challenging to efficiently explore various resources of causal interaction and maintain an overview of recorded contextual information that ensures valid use of the data. This review lists the different types of public resources with causal interactions, the different views on biological processes that they represent, the various data formats they use for data representation and storage, and the data exchange and conversion procedures that are available to extract and download these interactions. This may further raise awareness among the targeted audience, i.e. logical modelers and other scientists interested in molecular causal interactions, but also database managers and curators, about the abundance and variety of causal molecular interaction data, and the variety of tools and approaches to convert them into one interoperable resource.

Machine Learning to Support the Presentation of Complex Pathway Graphs

Visualization of biological mechanisms by means of pathway graphs is necessary to better understand the often complex underlying system. Manual layout of such pathways or maps of knowledge is a difficult and time consuming process. Node duplication is a technique that makes layouts with improved readability possible by reducing edge crossings and shortening edge lengths in drawn diagrams. In this article, we propose an approach using Machine Learning (ML) to facilitate parts of this task by training a Support Vector Machine (SVM) with actions taken during manual biocuration. Our training input is a series of incremental snapshots of a diagram describing mechanisms of a disease, progressively curated by a human expert employing node duplication in the process. As a test of the trained SVM models, they are applied to a single large instance and 25 medium-sized instances of hand-curated biological pathways. Finally, in a user validation study, we compare the model predictions to the outcome of a node duplication questionnaire answered by users of biological pathways with varying experience. We successfully predicted nodes for duplication and emulated human choices, demonstrating that our approach can effectively learn human-like node duplication preferences to support curation of pathway diagrams in various contexts.

Converting disease maps into heavyweight ontologies: general methodology and application to Alzheimer's disease

Omics technologies offer great promises for improving our understanding of diseases. The integration and interpretation of such data pose major challenges, calling for adequate knowledge models. Disease maps provide curated knowledge about disorders' pathophysiology at the molecular level adapted to omics measurements. However, the expressiveness of disease maps could be increased to help in avoiding ambiguities and misinterpretations and to reinforce their interoperability with other knowledge resources. Ontology is an adequate framework to overcome this limitation, through their axiomatic definitions and logical reasoning properties. We introduce the Disease Map Ontology (DMO), an ontological upper model based on systems biology terms. We then propose to apply DMO to Alzheimer's disease (AD). Specifically, we use it to drive the conversion of AlzPathway, a disease map devoted to AD, into a formal ontology: Alzheimer DMO. We demonstrate that it allows one to deal with issues related to redundancy, naming, consistency, process classification and pathway relationships. Furthermore, we show that it can store and manage multi-omics data. Finally, we expand the model using elements from other resources, such as clinical features contained in the AD Ontology, resulting in an enriched model called ADMO-plus. The current versions of DMO, ADMO and ADMO-plus are freely available at http://bioportal.bioontology.org/ontologies/ADMO.

SAMMI: a semi-automated tool for the visualization of metabolic networks

SUMMARY

Here we present a browser-based Semi-Automated Metabolic Map Illustrator (SAMMI) for the visualization of metabolic networks. While automated features allow for easy network partitioning, navigation, and node positioning, SAMMI also offers a wide array of manual map editing features. This combination allows for fast, context-specific visualization of metabolic networks as well as the development of standardized, large-scale, visually appealing maps. The implementation of SAMMI with popular constraint-based modeling toolboxes also allows for effortless visualization of simulation results of genome-scale metabolic models.

AVAILABILITY AND IMPLEMENTATION

SAMMI has been implemented as a standalone web-based tool and as plug-ins for the COBRA and COBRApy toolboxes. SAMMI and its COBRA plugins are available under the GPL 3.0 license and are available along with documentation, tutorials, and source code at www.SammiTool.com.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Atlas of Inflammation Resolution (AIR)

Acute inflammation is a protective reaction by the immune system in response to invading pathogens or tissue damage. Ideally, the response should be localized, self-limited, and returning to homeostasis. If not resolved, acute inflammation can result in organ pathologies leading to chronic inflammatory phenotypes. Acute inflammation and inflammation resolution are complex coordinated processes, involving a number of cell types, interacting in space and time. The biomolecular complexity and the fact that several biomedical fields are involved, make a multi- and interdisciplinary approach necessary. The Atlas of Inflammation Resolution (AIR) is a web-based resource capturing an essential part of the state-of-the-art in acute inflammation and inflammation resolution research. The AIR provides an interface for users to search thousands of interactions, arranged in inter-connected multi-layers of process diagrams, covering a wide range of clinically relevant phenotypes. By mapping experimental data onto the AIR, it can be used to elucidate drug action as well as molecular mechanisms underlying different disease phenotypes. For the visualization and exploration of information, the AIR uses the Minerva platform, which is a well-established tool for the presentation of disease maps. The molecular details of the AIR are encoded using international standards. The AIR was created as a freely accessible resource, supporting research and education in the fields of acute inflammation and inflammation resolution. The AIR connects research communities, facilitates clinical decision making, and supports research scientists in the formulation and validation of hypotheses. The AIR is accessible through https://air.bio.informatik.uni-rostock.de.

WITHDRAWN: The Atlas of Inflammation Resolution (AIR)

The Publisher regrets that this article is an accidental duplication of an article that has already been published, https://doi.org/10.1016/j.mam.2020.100894. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.

LAITOR4HPC: A text mining pipeline based on HPC for building interaction networks

Background

The amount of published full-text articles has increased dramatically. Text mining tools configure an essential approach to building biological networks, updating databases and providing annotation for new pathways. PESCADOR is an online web server based on LAITOR and NLProt text mining tools, which retrieves protein-protein co-occurrences in a tabular-based format, adding a network schema. Here we present an HPC-oriented version of PESCADOR’s native text mining tool, renamed to LAITOR4HPC, aiming to access an unlimited abstract amount in a short time to enrich available networks, build new ones and possibly highlight whether fields of research have been exhaustively studied.

Results

By taking advantage of parallel computing HPC infrastructure, the full collection of MEDLINE abstracts available until June 2017 was analyzed in a shorter period (6 days) when compared to the original online implementation (with an estimated 2 years to run the same data). Additionally, three case studies were presented to illustrate LAITOR4HPC usage possibilities. The first case study targeted soybean and was used to retrieve an overview of published co-occurrences in a single organism, retrieving 15,788 proteins in 7894 co-occurrences. In the second case study, a target gene family was searched in many organisms, by analyzing 15 species under biotic stress. Most co-occurrences regarded Arabidopsis thaliana and Zea mays. The third case study concerned the construction and enrichment of an available pathway. Choosing A. thaliana for further analysis, the defensin pathway was enriched, showing additional signaling and regulation molecules, and how they respond to each other in the modulation of this complex plant defense response.

Conclusions

LAITOR4HPC can be used for an efficient text mining based construction of biological networks derived from big data sources, such as MEDLINE abstracts. Time consumption and data input limitations will depend on the available resources at the HPC facility. LAITOR4HPC enables enough flexibility for different approaches and data amounts targeted to an organism, a subject, or a specific pathway. Additionally, it can deliver comprehensive results where interactions are classified into four types, according to their reliability.

Closing the gap between formats for storing layout information in systems biology

The understanding of complex biological networks often relies on both a dedicated layout and a topology. Currently, there are three major competing layout-aware systems biology formats, but there are no software tools or software libraries supporting all of them. This complicates the management of molecular network layouts and hinders their reuse and extension. In this paper, we present a high-level overview of the layout formats in systems biology, focusing on their commonalities and differences, review their support in existing software tools, libraries and repositories and finally introduce a new conversion module within the MINERVA platform. The module is available via a REST API and offers, besides the ability to convert between layout-aware systems biology formats, the possibility to export layouts into several graphical formats. The module enables conversion of very large networks with thousands of elements, such as disease maps or metabolic reconstructions, rendering it widely applicable in systems biology.

MINERVA API and plugins: opening molecular network analysis and visualization to the community

Summary

The complexity of molecular networks makes them difficult to navigate and interpret, creating a need for specialized software. MINERVA is a web platform for visualization, exploration and management of molecular networks. Here, we introduce an extension to MINERVA architecture that greatly facilitates the access and use of the stored molecular network data. It allows to incorporate such data in analytical pipelines via a programmatic access interface, and to extend the platform’s visual exploration and analytics functionality via plugin architecture. This is possible for any molecular network hosted by the MINERVA platform encoded in well-recognized systems biology formats. To showcase the possibilities of the plugin architecture, we have developed several plugins extending the MINERVA core functionalities. In the article, we demonstrate the plugins for interactive tree traversal of molecular networks, for enrichment analysis and for mapping and visualization of known disease variants or known adverse drug reactions to molecules in the network.

Availability and implementation

Plugins developed and maintained by the MINERVA team are available under the AGPL v3 license at https://git-r3lab.uni.lu/minerva/plugins/. The MINERVA API and plugin documentation is available at https://minerva-web.lcsb.uni.lu.

The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease

A multitude of factors contribute to complex diseases and can be measured with ‘omics’ methods. Databases facilitate data interpretation for underlying mechanisms. Here, we describe the Virtual Metabolic Human (VMH, www.vmh.life) database encapsulating current knowledge of human metabolism within five interlinked resources ‘Human metabolism’, ‘Gut microbiome’, ‘Disease’, ‘Nutrition’, and ‘ReconMaps’. The VMH captures 5180 unique metabolites, 17 730 unique reactions, 3695 human genes, 255 Mendelian diseases, 818 microbes, 632 685 microbial genes and 8790 food items. The VMH’s unique features are (i) the hosting of the metabolic reconstructions of human and gut microbes amenable for metabolic modeling; (ii) seven human metabolic maps for data visualization; (iii) a nutrition designer; (iv) a user-friendly webpage and application-programming interface to access its content; (v) user feedback option for community engagement and (vi) the connection of its entities to 57 other web resources. The VMH represents a novel, interdisciplinary database for data interpretation and hypothesis generation to the biomedical community.

Setting the basis of best practices and standards for curation and annotation of logical models in biology-highlights of the [BC]2 2019 CoLoMoTo/SysMod Workshop

The fast accumulation of biological data calls for their integration, analysis and exploitation through more systematic approaches. The generation of novel, relevant hypotheses from this enormous quantity of data remains challenging. Logical models have long been used to answer a variety of questions regarding the dynamical behaviours of regulatory networks. As the number of published logical models increases, there is a pressing need for systematic model annotation, referencing and curation in community-supported and standardised formats. This article summarises the key topics and future directions of a meeting entitled 'Annotation and curation of computational models in biology', organised as part of the 2019 [BC]2 conference. The purpose of the meeting was to develop and drive forward a plan towards the standardised annotation of logical models, review and connect various ongoing projects of experts from different communities involved in the modelling and annotation of molecular biological entities, interactions, pathways and models. This article defines a roadmap towards the annotation and curation of logical models, including milestones for best practices and minimum standard requirements.

Comprehensive Map of the Regulated Cell Death Signaling Network: A Powerful Analytical Tool for Studying Diseases

The processes leading to, or avoiding cell death are widely studied, because of their frequent perturbation in various diseases. Cell death occurs in three highly interconnected steps: Initiation, signaling and execution. We used a systems biology approach to gather information about all known modes of regulated cell death (RCD). Based on the experimental data retrieved from literature by manual curation, we graphically depicted the biological processes involved in RCD in the form of a seamless comprehensive signaling network map. The molecular mechanisms of each RCD mode are represented in detail. The RCD network map is divided into 26 functional modules that can be visualized contextually in the whole seamless network, as well as in individual diagrams. The resource is freely available and accessible via several web platforms for map navigation, data integration, and analysis. The RCD network map was employed for interpreting the functional differences in cell death regulation between Alzheimer's disease and non-small cell lung cancer based on gene expression data that allowed emphasizing the molecular mechanisms underlying the inverse comorbidity between the two pathologies. In addition, the map was used for the analysis of genomic and transcriptomic data from ovarian cancer patients that provided RCD map-based signatures of four distinct tumor subtypes and highlighted the difference in regulations of cell death molecular mechanisms.

RA-map: building a state-of-the-art interactive knowledge base for rheumatoid arthritis

Rheumatoid arthritis (RA) is a progressive, inflammatory autoimmune disease of unknown aetiology. The complex mechanism of aetiopathogenesis, progress and chronicity of the disease involves genetic, epigenetic and environmental factors. To understand the molecular mechanisms underlying disease phenotypes, one has to place implicated factors in their functional context. However, integration and organization of such data in a systematic manner remains a challenging task. Molecular maps are widely used in biology to provide a useful and intuitive way of depicting a variety of biological processes and disease mechanisms. Recent large-scale collaborative efforts such as the Disease Maps Project demonstrate the utility of such maps as versatile tools to organize and formalize disease-specific knowledge in a comprehensive way, both human and machine-readable. We present a systematic effort to construct a fully annotated, expert validated, state-of-the-art knowledge base for RA in the form of a molecular map. The RA map illustrates molecular and signalling pathways implicated in the disease. Signal transduction is depicted from receptors to the nucleus using the Systems Biology Graphical Notation (SBGN) standard representation. High-quality manual curation, use of only human-specific studies and focus on small-scale experiments aim to limit false positives in the map. The state-of-the-art molecular map for RA, using information from 353 peer-reviewed scientific publications, comprises 506 species, 446 reactions and 8 phenotypes. The species in the map are classified to 303 proteins, 61 complexes, 106 genes, 106 RNA entities, 2 ions and 7 simple molecules. The RA map is available online at ramap.elixir-luxembourg.org as an open-access knowledge base allowing for easy navigation and search of molecular pathways implicated in the disease. Furthermore, the RA map can serve as a template for omics data visualization.

MolArt: a molecular structure annotation and visualization tool

Summary

MolArt fills the gap between sequence and structure visualization by providing a light-weight, interactive environment enabling exploration of sequence annotations in the context of available experimental or predicted protein structures. Provided a UniProt ID, MolArt downloads and displays sequence annotations, sequence-structure mapping and relevant structures. The sequence and structure views are interlinked, enabling sequence annotations being color overlaid over the mapped structures, thus providing an enhanced understanding and interpretation of the available molecular data.

Availability and implementation

MolArt is released under the Apache 2 license and is available at https://github.com/davidhoksza/MolArt. The project web page https://davidhoksza.github.io/MolArt/ features examples and applications of the tool.

Connecting environmental exposure and neurodegeneration using cheminformatics and high resolution mass spectrometry: potential and challenges

Connecting chemical exposures over a lifetime to complex chronic diseases with multifactorial causes such as neurodegenerative diseases is an immense challenge requiring a long-term, interdisciplinary approach. Rapid developments in analytical and data technologies, such as non-target high resolution mass spectrometry (NT-HR-MS), have opened up new possibilities to accomplish this, inconceivable 20 years ago. While NT-HR-MS is being applied to increasingly complex research questions, there are still many unidentified chemicals and uncertainties in linking exposures to human health outcomes and environmental impacts. In this perspective, we explore the possibilities and challenges involved in using cheminformatics and NT-HR-MS to answer complex questions that cross many scientific disciplines, taking the identification of potential (small molecule) neurotoxicants in environmental or biological matrices as a case study. We explore capturing literature knowledge and patient exposure information in a form amenable to high-throughput data mining, and the related cheminformatic challenges. We then briefly cover which sample matrices are available, which method(s) could potentially be used to detect these chemicals in various matrices and what remains beyond the reach of NT-HR-MS. We touch on the potential for biological validation systems to contribute to mechanistic understanding of observations and explore which sampling and data archiving strategies may be required to form an accurate, sustained picture of small molecule signatures on extensive cohorts of patients with chronic neurodegenerative disorders. Finally, we reflect on how NT-HR-MS can support unravelling the contribution of the environment to complex diseases.

Independent Component Analysis for Unraveling the Complexity of Cancer Omics Datasets

Independent component analysis (ICA) is a matrix factorization approach where the signals captured by each individual matrix factors are optimized to become as mutually independent as possible. Initially suggested for solving source blind separation problems in various fields, ICA was shown to be successful in analyzing functional magnetic resonance imaging (fMRI) and other types of biomedical data. In the last twenty years, ICA became a part of the standard machine learning toolbox, together with other matrix factorization methods such as principal component analysis (PCA) and non-negative matrix factorization (NMF). Here, we review a number of recent works where ICA was shown to be a useful tool for unraveling the complexity of cancer biology from the analysis of different types of omics data, mainly collected for tumoral samples. Such works highlight the use of ICA in dimensionality reduction, deconvolution, data pre-processing, meta-analysis, and others applied to different data types (transcriptome, methylome, proteome, single-cell data). We particularly focus on the technical aspects of ICA application in omics studies such as using different protocols, determining the optimal number of components, assessing and improving reproducibility of the ICA results, and comparison with other popular matrix factorization techniques. We discuss the emerging ICA applications to the integrative analysis of multi-level omics datasets and introduce a conceptual view on ICA as a tool for defining functional subsystems of a complex biological system and their interactions under various conditions. Our review is accompanied by a Jupyter notebook which illustrates the discussed concepts and provides a practical tool for applying ICA to the analysis of cancer omics datasets.

Metabopolis: scalable network layout for biological pathway diagrams in urban map style

Background

Biological pathways represent chains of molecular interactions in biological systems that jointly form complex dynamic networks. The network structure changes from the significance of biological experiments and layout algorithms often sacrifice low-level details to maintain high-level information, which complicates the entire image to large biochemical systems such as human metabolic pathways.

Results

Our work is inspired by concepts from urban planning since we create a visual hierarchy of biological pathways, which is analogous to city blocks and grid-like road networks in an urban area. We automatize the manual drawing process of biologists by first partitioning the map domain into multiple sub-blocks, and then building the corresponding pathways by routing edges schematically, to maintain the global and local context simultaneously. Our system incorporates constrained floor-planning and network-flow algorithms to optimize the layout of sub-blocks and to distribute the edge density along the map domain. We have developed the approach in close collaboration with domain experts and present their feedback on the pathway diagrams based on selected use cases.

Conclusions

We present a new approach for computing biological pathway maps that untangles visual clutter by decomposing large networks into semantic sub-networks and bundling long edges to create space for presenting relationships systematically.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2779-4) contains supplementary material, which is available to authorized users.

Community-driven roadmap for integrated disease maps

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.

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.

Reactome diagram viewer: data structures and strategies to boost performance

Motivation

Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. For web-based pathway visualization, Reactome uses a custom pathway diagram viewer that has been evolved over the past years. Here, we present comprehensive enhancements in usability and performance based on extensive usability testing sessions and technology developments, aiming to optimize the viewer towards the needs of the community.

Results

The pathway diagram viewer version 3 achieves consistently better performance, loading and rendering of 97% of the diagrams in Reactome in less than 1 s. Combining the multi-layer html5 canvas strategy with a space partitioning data structure minimizes CPU workload, enabling the introduction of new features that further enhance user experience. Through the use of highly optimized data structures and algorithms, Reactome has boosted the performance and usability of the new pathway diagram viewer, providing a robust, scalable and easy-to-integrate solution to pathway visualization. As graph-based visualization of complex data is a frequent challenge in bioinformatics, many of the individual strategies presented here are applicable to a wide range of web-based bioinformatics resources.

Availability and implementation

Reactome is available online at: https://reactome.org. The diagram viewer is part of the Reactome pathway browser (https://reactome.org/PathwayBrowser/) and also available as a stand-alone widget at: https://reactome.org/dev/diagram/. The source code is freely available at: https://github.com/reactome-pwp/diagram.

Supplementary information

Supplementary data are available at Bioinformatics online.

Signalling maps in cancer research: construction and data analysis

Generation and usage of high-quality molecular signalling network maps can be augmented by standardizing notations, establishing curation workflows and application of computational biology methods to exploit the knowledge contained in the maps. In this manuscript, we summarize the major aims and challenges of assembling information in the form of comprehensive maps of molecular interactions. Mainly, we share our experience gained while creating the Atlas of Cancer Signalling Network. In the step-by-step procedure, we describe the map construction process and suggest solutions for map complexity management by introducing a hierarchical modular map structure. In addition, we describe the NaviCell platform, a computational technology using Google Maps API to explore comprehensive molecular maps similar to geographical maps and explain the advantages of semantic zooming principles for map navigation. We also provide the outline to prepare signalling network maps for navigation using the NaviCell platform. Finally, several examples of cancer high-throughput data analysis and visualization in the context of comprehensive signalling maps are presented.

Clustering approaches for visual knowledge exploration in molecular interaction networks

BACKGROUND

Biomedical knowledge grows in complexity, and becomes encoded in network-based repositories, which include focused, expert-drawn diagrams, networks of evidence-based associations and established ontologies. Combining these structured information sources is an important computational challenge, as large graphs are difficult to analyze visually.

RESULTS

We investigate knowledge discovery in manually curated and annotated molecular interaction diagrams. To evaluate similarity of content we use: i) Euclidean distance in expert-drawn diagrams, ii) shortest path distance using the underlying network and iii) ontology-based distance. We employ clustering with these metrics used separately and in pairwise combinations. We propose a novel bi-level optimization approach together with an evolutionary algorithm for informative combination of distance metrics. We compare the enrichment of the obtained clusters between the solutions and with expert knowledge. We calculate the number of Gene and Disease Ontology terms discovered by different solutions as a measure of cluster quality. Our results show that combining distance metrics can improve clustering accuracy, based on the comparison with expert-provided clusters. Also, the performance of specific combinations of distance functions depends on the clustering depth (number of clusters). By employing bi-level optimization approach we evaluated relative importance of distance functions and we found that indeed the order by which they are combined affects clustering performance. Next, with the enrichment analysis of clustering results we found that both hierarchical and bi-level clustering schemes discovered more Gene and Disease Ontology terms than expert-provided clusters for the same knowledge repository. Moreover, bi-level clustering found more enriched terms than the best hierarchical clustering solution for three distinct distance metric combinations in three different instances of disease maps.

CONCLUSIONS

In this work we examined the impact of different distance functions on clustering of a visual biomedical knowledge repository. We found that combining distance functions may be beneficial for clustering, and improve exploration of such repositories. We proposed bi-level optimization to evaluate the importance of order by which the distance functions are combined. Both combination and order of these functions affected clustering quality and knowledge recognition in the considered benchmarks. We propose that multiple dimensions can be utilized simultaneously for visual knowledge exploration.

From hype to reality: data science enabling personalized medicine

BACKGROUND

Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of 'big data' and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future.

CONCLUSIONS

There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.

Reactome enhanced pathway visualization

Motivation

Reactome is a free, open-source, open-data, curated and peer-reviewed knowledge base of biomolecular pathways. Pathways are arranged in a hierarchical structure that largely corresponds to the GO biological process hierarchy, allowing the user to navigate from high level concepts like immune system to detailed pathway diagrams showing biomolecular events like membrane transport or phosphorylation. Here, we present new developments in the Reactome visualization system that facilitate navigation through the pathway hierarchy and enable efficient reuse of Reactome visualizations for users’ own research presentations and publications.

Results

For the higher levels of the hierarchy, Reactome now provides scalable, interactive textbook-style diagrams in SVG format, which are also freely downloadable and editable. Repeated diagram elements like ‘mitochondrion’ or ‘receptor’ are available as a library of graphic elements. Detailed lower-level diagrams are now downloadable in editable PPTX format as sets of interconnected objects.

Availability and implementation

http://reactome.org