22,252 research outputs found
The Stochastic Dance of Early HIV Infection
The stochastic nature of early HIV infection is described in a series of models, each of which captures aspects of the dance of HIV during the early stages of infection. It is to this highly variable target that the immune response must respond. The adaptability of the various components of the immune response is an important aspect of the system\u27s operation, as the nature of the pathogens that the response will be required to respond to and the order in which those responses must be made cannot be known beforehand. As HIV infection has direct influence over cells responsible for the immune response, the dance predicts that the immune response will be also in a variable state of readiness and capability for this task of adaptation. The description of the stochastic dance of HIV here will use the tools of stochastic models, and for the most part, simulation. The justification for this approach is that the early stages and the development of HIV diversity require that the model to be able to describe both individual sample path and patient-to-patient variability. In addition, as early viral dynamics are best described using branching processes, the explosive growth of these models both predicts high variability and rapid response of HIV to changes in system parameters.
In this paper, a basic viral growth model based on a time dependent continuous-time branching process is used to describe the growth of HIV infected cells in the macrophage and lymphocyte populations. Immigration from the reservoir population is added to the basic model to describe the incubation time distribution. This distribution is deduced directly from the modeling assumptions and the model of viral growth. A system of two branching processes, one in the infected macrophage population and one in the infected lymphocyte population is used to provide a description of the relationship between the development of HIV diversity as it relates to tropism (host cell preference). The role of the immune response to HIV and HIV infected cells is used to describe the movement of the infection from a few infected macrophages to a disease of infected CD4+ role= presentation style= box-sizing: border-box; margin: 0px; padding: 0px; display: inline-block; font-style: normal; font-weight: normal; line-height: normal; font-size: 14.4px; text-indent: 0px; text-align: left; text-transform: none; letter-spacing: normal; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; position: relative; CD4+ T lymphocytes
Complex Spatial Dynamics of Oncolytic Viruses In Vitro: Mathematical and Experimental Approaches
Oncolytic viruses replicate selectively in tumor cells and can serve as targeted treatment agents. While promising results have been observed in clinical trials, consistent success of therapy remains elusive. The dynamics of virus spread through tumor cell populations has been studied both experimentally and computationally. However, a basic understanding of the principles underlying virus spread in spatially structured target cell populations has yet to be obtained. This paper studies such dynamics, using a newly constructed recombinant adenovirus type-5 (Ad5) that expresses enhanced jellyfish green fluorescent protein (EGFP), AdEGFPuci, and grows on human 293 embryonic kidney epithelial cells, allowing us to track cell numbers and spatial patterns over time. The cells are arranged in a two-dimensional setting and allow virus spread to occur only to target cells within the local neighborhood. Despite the simplicity of the setup, complex dynamics are observed. Experiments gave rise to three spatial patterns that we call “hollow ring structure”, “filled ring structure”, and “disperse pattern”. An agent-based, stochastic computational model is used to simulate and interpret the experiments. The model can reproduce the experimentally observed patterns, and identifies key parameters that determine which pattern of virus growth arises. The model is further used to study the long-term outcome of the dynamics for the different growth patterns, and to investigate conditions under which the virus population eliminates the target cells. We find that both the filled ring structure and disperse pattern of initial expansion are indicative of treatment failure, where target cells persist in the long run. The hollow ring structure is associated with either target cell extinction or low-level persistence, both of which can be viewed as treatment success. Interestingly, it is found that equilibrium properties of ordinary differential equations describing the dynamics in local neighborhoods in the agent-based model can predict the outcome of the spatial virus-cell dynamics, which has important practical implications. This analysis provides a first step towards understanding spatial oncolytic virus dynamics, upon which more detailed investigations and further complexity can be built
Predicting the outcomes of treatment to eradicate the latent reservoir for HIV-1
Massive research efforts are now underway to develop a cure for HIV
infection, allowing patients to discontinue lifelong combination antiretroviral
therapy (ART). New latency-reversing agents (LRAs) may be able to purge the
persistent reservoir of latent virus in resting memory CD4+ T cells, but the
degree of reservoir reduction needed for cure remains unknown. Here we use a
stochastic model of infection dynamics to estimate the efficacy of LRA needed
to prevent viral rebound after ART interruption. We incorporate clinical data
to estimate population-level parameter distributions and outcomes. Our findings
suggest that approximately 2,000-fold reductions are required to permit a
majority of patients to interrupt ART for one year without rebound and that
rebound may occur suddenly after multiple years. Greater than 10,000-fold
reductions may be required to prevent rebound altogether. Our results predict
large variation in rebound times following LRA therapy, which will complicate
clinical management. This model provides benchmarks for moving LRAs from the
lab to the clinic and can aid in the design and interpretation of clinical
trials. These results also apply to other interventions to reduce the latent
reservoir and can explain the observed return of viremia after months of
apparent cure in recent bone marrow transplant recipients and an
immediately-treated neonate.Comment: 8 pages main text (4 figures). In PNAS Early Edition
http://www.pnas.org/content/early/2014/08/05/1406663111. Ancillary files: SI,
24 pages SI (7 figures). File .htm opens a browser-based application to
calculate rebound times (see SI). Or, the .cdf file can be run with
Mathematica. The most up-to-date version of the code is available at
http://www.danielrosenbloom.com/reboundtimes
Evolutionary Dynamics of Giant Viruses and their Virophages
Giant viruses contain large genomes, encode many proteins atypical for
viruses, replicate in large viral factories, and tend to infect protists. The
giant virus replication factories can in turn be infected by so called
virophages, which are smaller viruses that negatively impact giant virus
replication. An example are Mimiviruses that infect the protist Acanthamoeba
and that are themselves infected by the virophage Sputnik. This paper examines
the evolutionary dynamics of this system, using mathematical models. While the
models suggest that the virophage population will evolve to increasing degrees
of giant virus inhibition, it further suggests that this renders the virophage
population prone to extinction due to dynamic instabilities over wide parameter
ranges. Implications and conditions required to avoid extinction are discussed.
Another interesting result is that virophage presence can fundamentally alter
the evolutionary course of the giant virus. While the giant virus is predicted
to evolve towards increasing its basic reproductive ratio in the absence of the
virophage, the opposite is true its presence. Therefore, virophages can not
only benefit the host population directly by inhibiting the giant viruses, but
also indirectly by causing giant viruses to evolve towards weaker phenotypes.
Experimental tests for this model are suggested
Giant number fluctuations in microbial ecologies
Statistical fluctuations in population sizes of microbes may be quite large
depending on the nature of their underlying stochastic dynamics. For example,
the variance of the population size of a microbe undergoing a pure birth
process with unlimited resources is proportional to the square of its mean. We
refer to such large fluctuations, with the variance growing as square of the
mean, as Giant Number Fluctuations (GNF). Luria and Delbruck showed that
spontaneous mutation processes in microbial populations exhibit GNF. We explore
whether GNF can arise in other microbial ecologies. We study certain simple
ecological models evolving via stochastic processes: (i) bi-directional
mutation, (ii) lysis-lysogeny of bacteria by bacteriophage, and (iii)
horizontal gene transfer (HGT). For the case of bi-directional mutation
process, we show analytically exactly that the GNF relationship holds at large
times. For the ecological model of bacteria undergoing lysis or lysogeny under
viral infection, we show that if the viral population can be experimentally
manipulated to stay quasi-stationary, the process of lysogeny maps essentially
to one-way mutation process and hence the GNF property of the lysogens follows.
Finally, we show that even the process of HGT may map to the mutation process
at large times, and thereby exhibits GNF.Comment: 18 pages, 5 figure
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