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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. Community-driven and highly interdisciplinary, the project is collaborative and supports community standards, open access, and the FAIR data principles. The coordination of community work allowed for an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework links key molecules highlighted from broad omics data analysis and computational modeling to dysregulated pathways in a cell-, tissue- or patient-specific manner. We also employ text mining and AI-assisted analysis to identify potential drugs and drug targets and use topological analysis to reveal interesting structural features of the map. The proposed framework is versatile and expandable, offering a significant upgrade in the arsenal used to understand virus-host interactions and other complex pathologies.

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.

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has experienced an extremely fast worldwide spreading, producing an unprecedented pandemic situation. As a result, a strong response from the research community resulted in a vast amount of new COVID-19-related publications, covering different clinical and molecular aspects of the disease. In spite of all these efforts, most of the underlying molecular events that lead SARS-CoV-2 infection to the observed clinical traits of COVID-19 remain unclear, since multiple biological pathways, cell types and organs are involved through a set of complex interactions. In this context, COVID-19 Disease Map (C19DM) initiative[1] was launched in order to provide a systems perspective to the COVID-19 infection, by integrating and formalising mechanistic knowledge, using current systems biology standards (https://covid.pages.uni.lu/). This open-access collection of curated pathways is a valuable resource which allows the conversion into different mathematical models, which in turn, can provide interesting mechanistic insights by using an array of simulation methods. One of these methods, PhysiCell[2] is an agent-based flexible cell simulator that can be used to perform diverse multiscale modelling. In fact, PhysiCell has been adapted to the COVID-19 problem recently by incorporating some signalling models of the interactions of virus and human host cells. In this work we present, PhysiBoSS-COVID, an effort to integrate MaBoSS[3], a stochastic Boolean modelling software, into PhysiCell-COVID[6] to allow the leverage of cell- and pathway-specific Boolean models in this framework. To obtain these COVID-19-specific models, we have taken advantage of CaSQ[4] ability to convert all C19DM maps into SBML-qual files, that can subsequently be transformed to MaBoSS-format Boolean models, ready-to-use with PhysiBoSS-COVID. As a proof of concept, we have incorporated models from several immune cells, as well as human epithelial host cells infected by SARS-CoV-2 to our prototype and hereby present preliminary results. PhysiBoSS-COVID, which is based on our previous work PhysiBoSS[5], provides a framework that enables testing of combined genetic and environmental perturbations, and can offer mechanistic insights of SARS-CoV-2 infection and dissemination among human host cells. Finally, PhysiBoSS-COVID was incorporated as a use case into the European HPC/Exascale Centre of Excellence in Personalized Medicine (PerMedCoE, http://permedcoe.eu/), whose purpose is to adapt multiscale modelling to supercomputing environments and to provide an easy-to-use interface to systems biology end users.

Rheumatoid arthritis (RA) is a multifactorial autoimmune disease that causes chronic inflammation of the joints, with an aetiology still unclear. RA is a complex inflammatory disease that involves an interconnected array of genetic, environmental, and epigenetic factors. Recently, systems biology and network-based approaches have been proposed to study such complex diseases. Combining multiple data types allows us to discover new knowledge as integration can help compensate for missing or unreliable information. Moreover, if multiple sources of evidence point to the same outcome then it is less likely to obtain false positives. Among computational approaches, machine learning stands out as a promising field in bioinformatics, as it allows for integration of multi-omic biomedical datasets. In this work, we present our efforts to integrate different layers of biological data in the form of networks in order to obtain a global view of the disease. To do so, we make use of publicly available transcriptomic datasets (peripheral blood) relative to RA and a variety of bioinformatics analyses such as statistical analysis, differential expression analysis, and machine learning network inference (Nicolle et al., 2015) to infer an RA-specific TF co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using the state of the art RA map based on the functional overlaps between the two networks (Singh et al., 2020). Lastly, a list of RA specific variants available in DisGeNET (Piñero et al., 2020) will be used as an overlap to highlight key genes associated with known disease mutations. We combine three different biological layers (gene expression, signalling cascades, mutations), obtained by bottom-up prior knowledge-based (DisGenNET, RA map) and top-down data-driven (CoRegNet) methods to build an integrative, disease-specific network. The goal behind this endeavor is to unravel mechanisms governing the regulation of key genes identified as mutation carriers in RA and also derive patient-specific models to gain more insights into the disease heterogeneity. &nbsp

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease affecting 1% of the population. Its pathogenesis mainly revolves around synovial hyperplasia, consisting of fibroblast-like synoviocytes (RASFs) accumulation. After acquiring an aggressive phenotype, RASFs reduce contact inhibition, resist to apoptosis, migrate and invade periarticular tissues (e.g. bone and cartilage). Similarly in the Cancer field, the importance of the stromal microenvironment has been recognized for years. It is widely accepted that cancer develops as a result of genetic and epigenetic alterations in clonal cells, but that growth, survival, and metastasis are regulated by stromal cells, also known as Cancer Associated Fibroblasts (CAFs). Thus, proliferating and aggressive fibroblasts not only seem to be a key feature in RA but also in cancer. An increasing number of studies have shown obvious parallels between CAFs and RASFs, not considering them anymore as passive responders but key disease effectors. A critical property lies in CAFs’ and RAFs’ altered glucose metabolism: they both are prone to metabolic reprogramming leading to a glycolytic switch. This common feature could prove to be critical and identifying the molecular links between metabolic reprogramming and fibroblasts activation may represent a serious lead in the development of complementary therapies for these pathologies. To study similarities in RASFs and CAFs glucose metabolism regulation, detailed molecular maps will be used as knowledge bases about the associated phenotypes. Regarding RA, a state-of-the-art molecular map has been published (Singh et al., 2020) illustrating pathways involved and allowing to extract specific fibroblast information. Similarly, a molecular interaction map for CAFs is also available (Kuperstein et al., 2015). After both being greatly enriched with metabolic information (focusing on the glycolytic switch), these maps will represent templates for various stages of data visualization and bioinformatic analysis. Both up-to-date molecular maps will also be converted into cell-specific executable Boolean models of intracellular networks, using the CaSQ tool (Aghamiri et al., 2020). By conducting several in-silico perturbations, fibroblasts’ response to different conditions will be studied, allowing to decipher the input/output relationships at a cellular level. Based on the bioinformatic and dynamic analysis, the identification of a regulatory mechanism (common or not) explaining fibroblasts’ metabolic reprogramming in disease-specific conditions and the phenotypic changes leading to the pathogenic transformation in RASFs and CAFs is expected. This will lead to a better understanding of the mechanisms underlying RA and Cancer pathogenesis at a global systemic level, potentially leading to the identification of new therapeutic targets.

Motivation Molecular interaction maps have emerged as a meaningful way of representing biological mechanisms in a comprehensive and systematic manner. However, their static nature provides limited insights to the emerging behaviour of the described biological system under different conditions. Computational modelling provides the means to study dynamic properties through in silico simulations and perturbations. We aim to bridge the gap between static and dynamic representations of biological systems with CaSQ, a software tool that infers Boolean rules based on the topology and semantics of molecular interaction maps built with CellDesigner. Results We developed CaSQ by defining conversion rules and logical formulas for inferred Boolean models according to the topology and the annotations of the starting molecular interaction maps. We used CaSQ to produce executable files of existing molecular maps that differ in size, complexity and the use of Systems Biology Graphical Notation (SBGN) standards. We also compared, where possible, the manually built logical models corresponding to a molecular map to the ones inferred by CaSQ. The tool is able to process large and complex maps built with CellDesigner (either following SBGN standards or not) and produce Boolean models in a standard output format, Systems Biology Marked Up Language-qualitative (SBML-qual), that can be further analyzed using popular modelling tools. References, annotations and layout of the CellDesigner molecular map are retained in the obtained model, facilitating interoperability and model reusability. Availability and implementation The present tool is available online: https://lifeware.inria.fr/∼soliman/post/casq/ and distributed as a Python package under the GNU GPLv3 license. The code can be accessed here: https://gitlab.inria.fr/soliman/casq. Supplementary information Supplementary data are available at Bioinformatics online.

International audience

International audience

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has experienced an extremely fast worldwide spreading, producing an unprecedented pandemic situation. As a result, a strong response from the research community resulted in a vast amount of new COVID-19-related publications, covering different clinical and molecular aspects of the disease. In spite of all these efforts, most of the underlying molecular events that lead SARS-CoV-2 infection to the observed clinical traits of COVID-19 remain unclear, since multiple biological pathways, cell types and organs are involved through a set of complex interactions. In this context, COVID-19 Disease Map (C19DM) initiative[1] was launched in order to provide a systems perspective to the COVID-19 infection, by integrating and formalising mechanistic knowledge, using current systems biology standards (https://covid.pages.uni.lu/). This open-access collection of curated pathways is a valuable resource which allows the conversion into different mathematical models, which in turn, can provide interesting mechanistic insights by using an array of simulation methods. One of these methods, PhysiCell[2] is an agent-based flexible cell simulator that can be used to perform diverse multiscale modelling. In fact, PhysiCell has been adapted to the COVID-19 problem recently by incorporating some signalling models of the interactions of virus and human host cells. In this work we present, PhysiBoSS-COVID, an effort to integrate MaBoSS[3], a stochastic Boolean modelling software, into PhysiCell-COVID[6] to allow the leverage of cell- and pathway-specific Boolean models in this framework. To obtain these COVID-19-specific models, we have taken advantage of CaSQ[4] ability to convert all C19DM maps into SBML-qual files, that can subsequently be transformed to MaBoSS-format Boolean models, ready-to-use with PhysiBoSS-COVID. As a proof of concept, we have incorporated models from several immune cells, as well as human epithelial host cells infected by SARS-CoV-2 to our prototype and hereby present preliminary results. PhysiBoSS-COVID, which is based on our previous work PhysiBoSS[5], provides a framework that enables testing of combined genetic and environmental perturbations, and can offer mechanistic insights of SARS-CoV-2 infection and dissemination among human host cells. Finally, PhysiBoSS-COVID was incorporated as a use case into the European HPC/Exascale Centre of Excellence in Personalized Medicine (PerMedCoE, http://permedcoe.eu/), whose purpose is to adapt multiscale modelling to supercomputing environments and to provide an easy-to-use interface to systems biology end users.

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has experienced an extremely fast worldwide spreading, producing an unprecedented pandemic situation. As a result, a strong response from the research community resulted in a vast amount of new COVID-19-related publications, covering different clinical and molecular aspects of the disease. In spite of all these efforts, most of the underlying molecular events that lead SARS-CoV-2 infection to the observed clinical traits of COVID-19 remain unclear, since multiple biological pathways, cell types and organs are involved through a set of complex interactions. In this context, COVID-19 Disease Map (C19DM) initiative[1] was launched in order to provide a systems perspective to the COVID-19 infection, by integrating and formalising mechanistic knowledge, using current systems biology standards (https://covid.pages.uni.lu/). This open-access collection of curated pathways is a valuable resource which allows the conversion into different mathematical models, which in turn, can provide interesting mechanistic insights by using an array of simulation methods. One of these methods, PhysiCell[2] is an agent-based flexible cell simulator that can be used to perform diverse multiscale modelling. In fact, PhysiCell has been adapted to the COVID-19 problem recently by incorporating some signalling models of the interactions of virus and human host cells. In this work we present, PhysiBoSS-COVID, an effort to integrate MaBoSS[3], a stochastic Boolean modelling software, into PhysiCell-COVID[6] to allow the leverage of cell- and pathway-specific Boolean models in this framework. To obtain these COVID-19-specific models, we have taken advantage of CaSQ[4] ability to convert all C19DM maps into SBML-qual files, that can subsequently be transformed to MaBoSS-format Boolean models, ready-to-use with PhysiBoSS-COVID. As a proof of concept, we have incorporated models from several immune cells, as well as human epithelial host cells infected by SARS-CoV-2 to our prototype and hereby present preliminary results. PhysiBoSS-COVID, which is based on our previous work PhysiBoSS[5], provides a framework that enables testing of combined genetic and environmental perturbations, and can offer mechanistic insights of SARS-CoV-2 infection and dissemination among human host cells. Finally, PhysiBoSS-COVID was incorporated as a use case into the European HPC/Exascale Centre of Excellence in Personalized Medicine (PerMedCoE, http://permedcoe.eu/), whose purpose is to adapt multiscale modelling to supercomputing environments and to provide an easy-to-use interface to systems biology end users., European Commission grants: INFORE project(H2020-ICT-825070) and PerMedCoE project(H2020-ICT-951773)

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has experienced an extremely fast worldwide spreading, producing an unprecedented pandemic situation. As a result, a strong response from the research community resulted in a vast amount of new COVID-19-related publications, covering different clinical and molecular aspects of the disease. In spite of all these efforts, most of the underlying molecular events that lead SARS-CoV-2 infection to the observed clinical traits of COVID-19 remain unclear, since multiple biological pathways, cell types and organs are involved through a set of complex interactions. In this context, COVID-19 Disease Map (C19DM) initiative[1] was launched in order to provide a systems perspective to the COVID-19 infection, by integrating and formalising mechanistic knowledge, using current systems biology standards (https://covid.pages.uni.lu/). This open-access collection of curated pathways is a valuable resource which allows the conversion into different mathematical models, which in turn, can provide interesting mechanistic insights by using an array of simulation methods. One of these methods, PhysiCell[2] is an agent-based flexible cell simulator that can be used to perform diverse multiscale modelling. In fact, PhysiCell has been adapted to the COVID-19 problem recently by incorporating some signalling models of the interactions of virus and human host cells. In this work we present, PhysiBoSS-COVID, an effort to integrate MaBoSS[3], a stochastic Boolean modelling software, into PhysiCell-COVID[6] to allow the leverage of cell- and pathway-specific Boolean models in this framework. To obtain these COVID-19-specific models, we have taken advantage of CaSQ[4] ability to convert all C19DM maps into SBML-qual files, that can subsequently be transformed to MaBoSS-format Boolean models, ready-to-use with PhysiBoSS-COVID. As a proof of concept, we have incorporated models from several immune cells, as well as human epithelial host cells infected by SARS-CoV-2 to our prototype and hereby present preliminary results. PhysiBoSS-COVID, which is based on our previous work PhysiBoSS[5], provides a framework that enables testing of combined genetic and environmental perturbations, and can offer mechanistic insights of SARS-CoV-2 infection and dissemination among human host cells. Finally, PhysiBoSS-COVID was incorporated as a use case into the European HPC/Exascale Centre of Excellence in Personalized Medicine (PerMedCoE, http://permedcoe.eu/), whose purpose is to adapt multiscale modelling to supercomputing environments and to provide an easy-to-use interface to systems biology end users.

The Disease Maps Project builds upon 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 in order to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.

Funder: Bundesministerium für Bildung und Forschung (BMBF); Id: http://dx.doi.org/10.13039/501100002347, 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.

Funder: Bundesministerium f��r Bildung und Forschung, Funder: Bundesministerium f��r Bildung und Forschung (BMBF), 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 Disease Maps Project builds upon 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 in order to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.

International audience; Rheumatoid arthritis (RA) is a multifactorial, complex autoimmune disease that involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this work, we aim to unravel mechanisms governing the regulation of key transcription factors in RA and derive patient-specific models to gain more insights into the disease heterogeneity and the response to treatment. We first use publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA-specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using a state-of-the-art, RA-specific molecular map. Then, the integrative network is used as a template to analyse patients’ data regarding their response to anti-TNF treatment and identify master regulators and upstream cascades affected by the treatment. Finally, we use the Boolean formalism to simulate in silico subparts of the integrated network and identify combinations and conditions that can switch on or off the identified TFs, mimicking the effects of single and combined perturbations.

International audience