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Mechanistic modeling of positive-sense RNA virus infection in mammalian cells

Monday, July 17 at 6:00pm

SMB2023 SMB2023 Follow Monday during the "PS01" time block.
Room assignment: in The Ohio Union.
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Alexander A. DiBiasi

University of Pittsburgh
"Mechanistic modeling of positive-sense RNA virus infection in mammalian cells"
More than one-third of all virus genera consist of positive-sense RNA (+ssRNA) viruses. Some well-known examples of highly pathogenic +ssRNA viruses in humans include Hepatitis C virus and SARS-CoV-2, which pose significant public health threats. In an effort to combat these threats, many have created or expanded upon mechanistic models of the replication of these viruses. Here we present a comprehensive review of mechanistic models describing positive-sense RNA virus replication in mammalian cells. We discuss the wide range of applications from these models and potential future research directions in this field. One common strategy to enhance the understanding of +ssRNA virus replication was to expand upon previous models. Viral RNA allocation, negative-sense RNA, and patient level dynamics are some example expansions. Another approach to advancing existing models is to improve their reproducibility, creating a more streamlined experience when using those models. Some models investigate the interplay between virus and innate immune response, exploring the effects on virus production and comparing signaling pathways. Finally, numerous models incorporate antiviral treatments, ranging from gene therapy strategies to nonstructural protein inhibitors like daclatasvir. An analysis of the reviewed models revealed some potential future directions. For instance, nearly half of the reviewed models were of Hepatitis C virus, leaving opportunities for modeling other +ssRNA viruses. Furthermore, every model features RNA replication, but the steps that become before or after RNA replication are not as prominently represented. In conclusion, positive-sense RNA viral replication models have been applied to a diverse set of pathogens, immune system components, and potential therapies, and hold considerable promise for helping develop future therapies for viral diseases.
Additional authors: Caroline I. Larkin and James R. Faeder, University of PIttsburgh

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Annual Meeting for the Society for Mathematical Biology, 2023.