Combining Devs and Semantic Technologies for Modeling the SARS-CoV-2 Replication Machinery

Abstract

The search for inhibitors of SARS-CoV-2 viral replication depends on an in-depth knowledge of the different stages of the viral cycle. The macro-molecular level focuses on the interactions between the virus and the infected cell, while the micro-molecular level focuses on the different biochemical reactions leading to the production of new viruses. Here, a hybrid approach for modeling the SARS-CoV-2 viral replication in the micro- and macro-molecular levels is presented. The proposed approach combines ontology engineering and DEVS modeling. Biological knowledge at the micro-level of the viral system is capitalized by ontological models, while the dynamic behavior of SARS-CoV-2 molecular mechanisms are modeled by DEVS models. The proposed DEVS approach uses ontological concepts and SWRL rules to compute the main functions and behaviour of the molecular components involved in the SARS-CoV-2 replication cycle. We illustrate the proposed approach through the simulation of the SARS-CoV-2 proteins production by cellular ribosomes.