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Advancing Mechanistic Understanding ...

Larkin, Caroline I.,

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Advancing Mechanistic Understanding of Viral Replication Dynamics With Computational Modeling and Bayesian Inference /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advancing Mechanistic Understanding of Viral Replication Dynamics With Computational Modeling and Bayesian Inference // Caroline I Larkin.
Author:	Larkin, Caroline I.,
Description:	1 electronic resource (188 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
Contained By:	Dissertations Abstracts International86-12B.
Subject:	Systematic biology. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31844956
ISBN:	9798280724297

Advancing Mechanistic Understanding of Viral Replication Dynamics With Computational Modeling and Bayesian Inference /
Larkin, Caroline I.,

Advancing Mechanistic Understanding of Viral Replication Dynamics With Computational Modeling and Bayesian Inference /Caroline I Larkin. - 1 electronic resource (188 pages)

Source: Dissertations Abstracts International, Volume: 86-12, Section: B.

Infectious diseases caused by positive-sense RNA (+ssRNA) viruses continue to pose a major threat to public health. While antiviral therapies and vaccines have advanced significantly over the past century, the rapid evolution of RNA viruses remains an immense challenge, enabling the emergence of new pathogenic variants. One such virus is Eastern equine encephalitis virus (EEEV), which replicates rapidly - producing viral particles as early as three hours post-infection. Although the molecular components driving EEEV replication have been studied, how these mechanisms integrate to determine overall viral dynamics remains poorly understood. This knowledge gap limits progress in therapeutic development.This dissertation investigates the mechanisms and kinetics of EEEV replication by integrating mechanistic modeling with Bayesian inference. We begin with a review of existing +ssRNA virus replication models to inform the development of a novel model of EEEV intracellular dynamics. Calibrated using new time-course data of viral RNA, proteins, and infectious particles, the model reveals that EEEV prioritizes genome allocation to assembly, supporting its rapid production of progeny virions.A key challenge in constructing accurate kinetic models is parameter estimation. To this end, we evaluate the performance of three Bayesian inference methods - Parallel Tempering Markov Chain Monte Carlo, Sequential Monte Carlo, and preconditioned Monte Carlo - on their ability to recover parameters across a suite of benchmark biological models. Our results demonstrate that the choice of inference method significantly affects the quality and reproducibility of parameter estimates. Notably, preconditioned Monte Carlo consistently produces stable and reliable results across diverse scenarios.By bridging experimental data with mechanistic modeling and advanced Bayesian inference, this work deepens our understanding of EEEV replication and provides a robust framework for studying complex intracellular processes. The insights gained here contribute not only to computational virology but also offer a foundation for developing targeted antiviral strategies to combat +ssRNA viral infections.

English

ISBN: 9798280724297Subjects--Topical Terms:

3173492
Systematic biology.
Subjects--Index Terms:

Bayesian inference

Advancing Mechanistic Understanding of Viral Replication Dynamics With Computational Modeling and Bayesian Inference /
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500 $a Includes supplementary digital materials.
500 $a Advisors: Faeder, James R. Committee members: Lee, Robin E. C.; Klimstra, William B.; Schwartz, Russell.
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520 $a Infectious diseases caused by positive-sense RNA (+ssRNA) viruses continue to pose a major threat to public health. While antiviral therapies and vaccines have advanced significantly over the past century, the rapid evolution of RNA viruses remains an immense challenge, enabling the emergence of new pathogenic variants. One such virus is Eastern equine encephalitis virus (EEEV), which replicates rapidly - producing viral particles as early as three hours post-infection. Although the molecular components driving EEEV replication have been studied, how these mechanisms integrate to determine overall viral dynamics remains poorly understood. This knowledge gap limits progress in therapeutic development.This dissertation investigates the mechanisms and kinetics of EEEV replication by integrating mechanistic modeling with Bayesian inference. We begin with a review of existing +ssRNA virus replication models to inform the development of a novel model of EEEV intracellular dynamics. Calibrated using new time-course data of viral RNA, proteins, and infectious particles, the model reveals that EEEV prioritizes genome allocation to assembly, supporting its rapid production of progeny virions.A key challenge in constructing accurate kinetic models is parameter estimation. To this end, we evaluate the performance of three Bayesian inference methods - Parallel Tempering Markov Chain Monte Carlo, Sequential Monte Carlo, and preconditioned Monte Carlo - on their ability to recover parameters across a suite of benchmark biological models. Our results demonstrate that the choice of inference method significantly affects the quality and reproducibility of parameter estimates. Notably, preconditioned Monte Carlo consistently produces stable and reliable results across diverse scenarios.By bridging experimental data with mechanistic modeling and advanced Bayesian inference, this work deepens our understanding of EEEV replication and provides a robust framework for studying complex intracellular processes. The insights gained here contribute not only to computational virology but also offer a foundation for developing targeted antiviral strategies to combat +ssRNA viral infections.
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