COVID‐19 Disease Map, a computational knowledge repository of virus‐host interaction mechanisms

Abstract

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.

A versatile and interoperable computational framework for the analysis and modeling of COVID-19 disease mechanisms

“…Pre-processing and differential gene expression analysis was performed in R using the DESeq2 package (64). Next, a combined pathway collection of the COVID-19 Disease Map (21 pathways (76)), WikiPathways (597 pathways (11)) and Reactome (1,222 pathways (12)) was created. Pathway enrichment analysis was performed using the clusterProfiler R package (73).…”

“…We expanded this simulator with our tool, PhysiBoSS (85), which incorporates MaBoSS (86), a tool that stochastically simulates Boolean models, into PhysiCell (87), a tool that uses agent-based modeling to simulate cells and their surrounding environment, and their interplay. Two Boolean models were used: first, the epithelial apoptosis model was converted from the map to the model using CaSQ (26) and the C19DMap project (https://fairdomhub.org/models/712) (76). We modified the apoptosis model to capture mechanisms such as BAX activating the apoptosome complex and included output nodes as readouts.…”

Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures

“…Several computational approaches have been applied to combat COVID-19, [ 94 ] ranging from the establishment of a knowledge repository of COVID-19 molecular mechanisms, i.e., COVID-19 disease maps [ 95 , 96 ], to the identification of candidate drugs which may be helpful in COVID-19 treatment and prevention [ 97 ]. In this context, different approaches have also been proposed to analyse the COVID-19 metabolic signatures and propose novel treatments and diagnostic approaches using GEMs.…”

Perspectives of (/memorandum for) systems thinking on COVID‐19 pandemic and pathology

“…Unfortunately, clinical observations of COVID‐19 symptomatology, course of disease, response to medications and so forth, are essential but they are difficult to measure since they are only ‘qualitative data’. An interesting starting point for the first set of required data is the COVID‐19 disease map, 113 which in a repeatedly revised manner organizes what is known about the biology of the disease into functional diagrams. It would be advisable to create corresponding maps of social and environmental aspects and their influences on the progression of the disease.…”