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Mobile promoters are emerging as a new class of mobile genetic elements, first identified by examining prokaryote genome sequences, and more recently confirmed by experimental observations in bacteria. Recent datasets have identified over 40,000 putative mobile promoters in sequenced prokaryote genomes, however only one-third of these are in regions of the genome directly upstream from coding sequences, that is, in promoter regions. The presence of many promoter sequences in non-promoter regions is unexplained. Here we develop a general mathematical model for the dynamics of mobile promoters, extending previous work to capture the dynamics both within and outside promoter regions. From this general model, we apply rigorous model selection techniques to identify which parameters are statistically justified in describing the available mobile promoter data, and find best-fit values of these parameters. Our results suggest that high rates of horizontal gene transfer maintain the population of mobile promoters in promoter regions, and that once established at these sites, mobile promoters are rarely lost, but are commonly copied to other genomic regions. In contrast, mobile promoter copies in non-promoter regions are more numerous and more volatile, experiencing substantially higher rates of duplication, loss and diversification. [ABSTRACT FROM AUTHOR]

Abstract: Viral load and CD4 T-cell counts in patients infected with the human immunodeficiency virus (HIV) are commonly used to guide clinical decisions regarding drug therapy or to assess therapeutic outcomes in clinical trials. However, random fluctuations in these markers of infection can obscure clinically significant change. We employ a Monte Carlo simulation to investigate contributing factors in the expected variability in CD4 T-cell count and viral load due solely to the stochastic nature of HIV infection. The simulation includes processes that contribute to the variability in HIV infection including CD4 and CD8 T-cell population dynamics as well as T-cell activation and proliferation. The simulation results may reconcile the wide range of variabilities in viral load observed in clinical studies, by quantifying correlations between viral load measurements taken days or weeks apart. The sensitivity of variability in T-cell count and viral load to changes in the lifetimes of CD4 and CD8 T-cells is investigated, as well as the effects of drug therapy. [Copyright &y& Elsevier]

Abstract: We describe a Monte Carlo simulation of the within-host dynamics of human immunodeficiency virus 1 (HIV-1). The simulation proceeds at the level of individual T-cells and virions in a small volume of plasma, thus capturing the inherent stochasticity in viral replication, mutation and T-cell infection. When cell lifetimes are distributed exponentially in the Monte Carlo approach, our simulation results are in perfect agreement with the predictions of the corresponding systems of differential equations from the literature. The Monte Carlo model, however, uniquely allows us to estimate the natural variability in important parameters such as the T-cell count, viral load, and the basic reproductive ratio, in both the presence and absence of drug therapy. The simulation also yields the probability that an infection will not become established after exposure to a viral inoculum of a given size. Finally, we extend the Monte Carlo approach to include distributions of cell lifetimes that are less-dispersed than exponential. [Copyright &y& Elsevier]

Within-host SARS-CoV-2 modelling studies have been published throughout the COVID-19 pandemic. These studies contain highly variable numbers of individuals and capture varying timescales of pathogen dynamics; some studies capture the time of disease onset, the peak viral load and subsequent heterogeneity in clearance dynamics across individuals, while others capture late-time post-peak dynamics. In this study, we curate multiple previously published SARS-CoV-2 viral load data sets, fit these data with a consistent modelling approach, and estimate the variability of in-host parameters including the basic reproduction number, R 0 , as well as the best-fit eclipse phase profile. We find that fitted dynamics can be highly variable across data sets, and highly variable within data sets, particularly when key components of the dynamic trajectories (e.g. peak viral load) are not represented in the data. Further, we investigated the role of the eclipse phase time distribution in fitting SARS-CoV-2 viral load data. By varying the shape parameter of an Erlang distribution, we demonstrate that models with either no eclipse phase, or with an exponentially-distributed eclipse phase, offer significantly worse fits to these data, whereas models with less dispersion around the mean eclipse time (shape parameter two or more) offered the best fits to the available data across all data sets used in this work. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics". [Display omitted] • SARS-CoV-2 early in-host infection data is necessary for proper parameter estimation. • Time of peak viral load and associated parameters for SARS-CoV-2 cannot be accurately estimated with late infection data. • Robust exploration of in-host parameter variability and practical identifiability. • SARS-CoV-2 dynamics are highly variable across and within data sets. • The eclipse dynamics are best described by an Erlang distribution. [ABSTRACT FROM AUTHOR]