Understanding the replication machinery of viruses contributes to suggest and try effective antiviral strategies. Exhaustive knowledge about the proteins structure, their function, or their interaction is one of the preconditions for successfully modeling it. In this context, modeling methods based on a formal representation with a high semantic expressiveness would be relevant to extract proteins and their nucleotide or amino acid sequences as an element from the replication process. Consequently, our approach relies on the use of semantic technologies to design the SARS-CoV-2 replication machinery. This provides the ability to infer new knowledge related to each step of the virus replication. More specifically, we developed an ontology-based approach enriched with reasoning process of a complete replication machinery process for SARS-CoV-2. We present in this paper a partial overview of our ontology OntoRepliCov to describe one step of this process, namely, the continuous translation or protein synthesis, through classes, properties, axioms, and SWRL (Semantic Web Rule Language) rules.
The search for inhibitors of SARS-CoV-2 viral replication depends on the understanding of the events taking place at different molecular levels during the viral infection. The macro-molecular level focuses on the interactions among viral and host proteins, while the micro-molecular level focuses on the different biochemical modifications that occur to one or more amino acids on proteins. A hybrid approach for modeling the SARS-CoV-2 viral replication in the micro-and macro-molecular levels is presented in this paper. The proposed approach combines two domains which complement one another, ontology engineering and discrete event system specification (DEVS) modeling.In this approach, biological knowledge at the micro-level of the viral system is capitalized and inferred by ontological models, while the dynamic behavior of SARS-CoV-2 molecular mechanisms and their different state changes in time are modeled by DEVS models. We illustrate the proposed approach through the modeling and simulation of the ribosome, a key molecule of the host cell that all viruses compete for, including the SARS-CoV-2.
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