The discrepancy in the turnover of cells and virus in different organs or viral reservoirs necessitates the investigation of multiple compartments within a host. Establishing a multi-compartmental structure that describes the complexity of various organs, where viral infection comprehensively proceeds, provides a modeling framework for exploring the effect of spatial heterogeneity on viral dynamics. To successfully suppress within-host viral replication, it is imperative to determine drug administration during therapy, particularly for a combination of antiretroviral drugs. The proposed model provides quantitative insights into pharmacokinetics and the resulting virus population, which substantially relates to environmental heterogeneity. The main results are the following: (1) A model incorporating drug treatment admits threshold dynamics, driving to either viral extinction or uniform persistence, regardless of non-trivial initial infection, in the entire system. (2) Viral infection may be underestimated if a well-mixed (single-compartmental) model is used. (3) Optimal drug administration depends not only on the drug distribution over various compartments but also on the timing, described by phase shifts, of the administration of different drugs in a combined therapy.
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http://dx.doi.org/10.1007/s11538-019-00658-1 | DOI Listing |
Subcell Biochem
December 2024
Department of Macromolecular Structure, Centro Nacional de Biotecnología (CNB-CSIC), Madrid, Spain.
Understanding the dynamic processes involving virus structural components within host cells is crucial for comprehending viral infection, as viruses rely entirely on host cells for replication. Viral infection involves various intracellular stages, including cell entry, genome uncoating, replication, transcription and translation, assembly of new virus particles in a complex morphogenetic process, and the release of new virions from the host cell. These events are dynamic and scarce and can be obscured by other cellular processes, necessitating novel approaches for their in situ characterization.
View Article and Find Full Text PDFBMC Genomics
December 2024
School of Computer Science and Technology, Qingdao University, Ningxia Road, Qingdao, Shandong Province, 266071, China.
Background: Discontinuous transcription allows coronaviruses to efficiently replicate and transmit within host cells, enhancing their adaptability and survival. Assembling viral transcripts is crucial for virology research and the development of antiviral strategies. However, traditional transcript assembly methods primarily designed for variable alternative splicing events in eukaryotes are not suitable for the viral transcript assembly problem.
View Article and Find Full Text PDFJ Math Biol
December 2024
Complex Systems Research Center, Shanxi University, Taiyuan, 030006, Shanxi, China.
In addition to non-pharmaceutical interventions, antiviral drugs and vaccination are considered as the optimal solutions to control and eliminate the COVID-19 pandemic. It is necessary to couple within-host and between-host models to investigate the impact of treatment and vaccination. Hence, we propose an age-structured model, where the infection age is used to link the within-host viral dynamics and the disease dynamics at the population level.
View Article and Find Full Text PDFFront Cell Infect Microbiol
December 2024
Department of Infectious Diseases, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
In the context of chronic hepatitis B virus (HBV) infection, the continuous replication of HBV within host hepatocytes is a characteristic feature. Rather than directly causing hepatocyte destruction, this replication leads to immune dysfunction and establishes a state of T-B immune tolerance. Successful clearance of the HBV virus is dependent on the close collaboration between humoral and cellular immunity.
View Article and Find Full Text PDFMath Biosci Eng
October 2024
Department of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
Uncertainty in parameter estimates from fitting within-host models to empirical data limits the model's ability to uncover mechanisms of infection, disease progression, and to guide pharmaceutical interventions. Understanding the effect of model structure and data availability on model predictions is important for informing model development and experimental design. To address sources of uncertainty in parameter estimation, we used four mathematical models of influenza A infection with increased degrees of biological realism.
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