Setting the basis of best practices and standards for curation and annotation of logical models in biology

International audienceThe 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

Reversibility and composition of rewriting in hierarchies

Post-proceedings version.International audienceIn this paper we study how SqPO rewriting of individual objects and hierarchies of objects can be reversed and how the composition of rewrites can be constructed. We introduce the notion of a rule hierarchy , study how such rule hierarchies can be applied to object hierarchies and analyse the conditions under which this application is reversible. We then present a theory for constructing the composition of consecutive hierarchy rewrites. We further illustrate how the reversibility and composition of rewriting can be used to design an audit trail for both individual graphs and graph hierarchies. This provides us a compact way to maintain the history of updates of an object including its multiple versions. The main application of the designed framework is an audit of updates to knowledge represented with hierarchies of graphs. The prototype system for transformations in hierarchies of simple graphs with attributes is implemented as a part of the ReGraph Python library

Reversibility and composition of rewriting in hierarchies

Post-proceedings version.International audienceIn this paper we study how SqPO rewriting of individual objects and hierarchies of objects can be reversed and how the composition of rewrites can be constructed. We introduce the notion of a rule hierarchy , study how such rule hierarchies can be applied to object hierarchies and analyse the conditions under which this application is reversible. We then present a theory for constructing the composition of consecutive hierarchy rewrites. We further illustrate how the reversibility and composition of rewriting can be used to design an audit trail for both individual graphs and graph hierarchies. This provides us a compact way to maintain the history of updates of an object including its multiple versions. The main application of the designed framework is an audit of updates to knowledge represented with hierarchies of graphs. The prototype system for transformations in hierarchies of simple graphs with attributes is implemented as a part of the ReGraph Python library

KAMIStudio: an environment for biocuration of cell signalling knowledge

International audienc

Bio-curation for cellular signalling: the KAMI project

International audienc

A Machine-Generated View of the Role of Blood Glucose Levels in the Severity of COVID-19

SARS-CoV-2 started spreading toward the end of 2019 causing COVID-19, a disease that reached pandemic proportions among the human population within months. The reasons for the spectrum of differences in the severity of the disease across the population, and in particular why the disease affects more severely the aging population and those with specific preconditions are unclear. We developed machine learning models to mine 240,000 scientific articles openly accessible in the CORD-19 database, and constructed knowledge graphs to synthesize the extracted information and navigate the collective knowledge in an attempt to search for a potential common underlying reason for disease severity. The machine-driven framework we developed repeatedly pointed to elevated blood glucose as a key facilitator in the progression of COVID-19. Indeed, when we systematically retraced the steps of the SARS-CoV-2 infection, we found evidence linking elevated glucose to each major step of the life-cycle of the virus, progression of the disease, and presentation of symptoms. Specifically, elevations of glucose provide ideal conditions for the virus to evade and weaken the first level of the immune defense system in the lungs, gain access to deep alveolar cells, bind to the ACE2 receptor and enter the pulmonary cells, accelerate replication of the virus within cells increasing cell death and inducing an pulmonary inflammatory response, which overwhelms an already weakened innate immune system to trigger an avalanche of systemic infections, inflammation and cell damage, a cytokine storm and thrombotic events. We tested the feasibility of the hypothesis by manually reviewing the literature referenced by the machine-generated synthesis, reconstructing atomistically the virus at the surface of the pulmonary airways, and performing quantitative computational modeling of the effects of glucose levels on the infection process. We conclude that elevation in glucose levels can facilitate the progression of the disease through multiple mechanisms and can explain much of the differences in disease severity seen across the population. The study provides diagnostic considerations, new areas of research and potential treatments, and cautions on treatment strategies and critical care conditions that induce elevations in blood glucose levels