9,769 research outputs found
Hybrid spreading mechanisms and T cell activation shape the dynamics of HIV-1 infection
HIV-1 can disseminate between susceptible cells by two mechanisms: cell-free
infection following fluid-phase diffusion of virions and by highly-efficient
direct cell-to-cell transmission at immune cell contacts. The contribution of
this hybrid spreading mechanism, which is also a characteristic of some
important computer worm outbreaks, to HIV-1 progression in vivo remains
unknown. Here we present a new mathematical model that explicitly incorporates
the ability of HIV-1 to use hybrid spreading mechanisms and evaluate the
consequences for HIV-1 pathogenenesis. The model captures the major phases of
the HIV-1 infection course of a cohort of treatment naive patients and also
accurately predicts the results of the Short Pulse Anti-Retroviral Therapy at
Seroconversion (SPARTAC) trial. Using this model we find that hybrid spreading
is critical to seed and establish infection, and that cell-to-cell spread and
increased CD4+ T cell activation are important for HIV-1 progression. Notably,
the model predicts that cell-to-cell spread becomes increasingly effective as
infection progresses and thus may present a considerable treatment barrier.
Deriving predictions of various treatments' influence on HIV-1 progression
highlights the importance of earlier intervention and suggests that treatments
effectively targeting cell-to-cell HIV-1 spread can delay progression to AIDS.
This study suggests that hybrid spreading is a fundamental feature of HIV
infection, and provides the mathematical framework incorporating this feature
with which to evaluate future therapeutic strategies
Mathematical modelling of internal HIV dynamics
We study a mathematical model for the viral dynamics of HIV in an infected individual in the presence of HAART. The paper starts with a literature review and then formulates the basic mathematical model. An expression for R0, the basic reproduction number of the virus under steady state application of HAART, is derived followed by an equilibrium and stability analysis. There is always a disease-free equilibrium (DFE) which is globally asymptotically stable for R0 1 then some simulations will die out whereas others will not. Stochastic simulations suggest that if R0 > 1 those which do not die out approach a stochastic quasi-equilibrium consisting of random uctuations about the non-trivial deterministic equilibrium levels, but the amplitude of these uctuations is so small that practically the system is at the non-trivial equilibrium. A brief discussion concludes the paper
A Simple Cellular Automaton Model for Influenza A Viral Infections
Viral kinetics have been extensively studied in the past through the use of
spatially homogeneous ordinary differential equations describing the time
evolution of the diseased state. However, spatial characteristics such as
localized populations of dead cells might adversely affect the spread of
infection, similar to the manner in which a counter-fire can stop a forest fire
from spreading. In order to investigate the influence of spatial
heterogeneities on viral spread, a simple 2-D cellular automaton (CA) model of
a viral infection has been developed. In this initial phase of the
investigation, the CA model is validated against clinical immunological data
for uncomplicated influenza A infections. Our results will be shown and
discussed.Comment: LaTeX, 12 pages, 18 EPS figures, uses document class ReTeX4, and
packages amsmath and SIunit
Time-delayed model of immune response in plants
In the studies of plant infections, the plant immune response is known to play an essential role. In this paper we derive and analyse a new mathematical model of plant immune response with particular account for post-transcriptional gene silencing (PTGS). Besides biologically accurate representation of the PTGS dynamics, the model explicitly includes two time delays to represent the maturation time of the growing plant tissue and the non-instantaneous nature of the PTGS. Through analytical and numerical analysis of stability of the steady states of the model we identify parameter regions associated with recovery
and resistant phenotypes, as well as possible chronic infections. Dynamics of the system in these regimes is illustrated by numerical simulations of the model
StochKit-FF: Efficient Systems Biology on Multicore Architectures
The stochastic modelling of biological systems is an informative, and in some
cases, very adequate technique, which may however result in being more
expensive than other modelling approaches, such as differential equations. We
present StochKit-FF, a parallel version of StochKit, a reference toolkit for
stochastic simulations. StochKit-FF is based on the FastFlow programming
toolkit for multicores and exploits the novel concept of selective memory. We
experiment StochKit-FF on a model of HIV infection dynamics, with the aim of
extracting information from efficiently run experiments, here in terms of
average and variance and, on a longer term, of more structured data.Comment: 14 pages + cover pag
Enhancement of chemotherapy using oncolytic virotherapy: Mathematical and optimal control analysis
Oncolytic virotherapy (OV) has been emerging as a promising novel cancer
treatment that may be further combined with the existing therapeutic modalities
to enhance their effects. To investigate how OV could enhance chemotherapy, we
propose an ODE based model describing the interactions between tumour cells,
the immune response, and a treatment combination with chemotherapy and
oncolytic viruses. Stability analysis of the model with constant chemotherapy
treatment rates shows that without any form of treatment, a tumour would grow
to its maximum size. It also demonstrates that chemotherapy alone is capable of
clearing tumour cells provided that the drug efficacy is greater than the
intrinsic tumour growth rate. Furthermore, OV alone may not be able to clear
tumour cells from body tissue but would rather enhance chemotherapy if viruses
with high viral potency are used. To assess the combined effect of OV and
chemotherapy we use the forward sensitivity index to perform a sensitivity
analysis, with respect to chemotherapy key parameters, of the virus basic
reproductive number and the tumour endemic equilibrium. The results from this
sensitivity analysis indicate the existence of a critical dose of chemotherapy
above which no further significant reduction in the tumour population can be
observed. Numerical simulations show that a successful combinational therapy of
the chemotherapeutic drugs and viruses depends mostly on the virus burst size,
infection rate, and the amount of drugs supplied. Optimal control analysis was
performed, by means of Pontryagin's principle, to further refine predictions of
the model with constant treatment rates by accounting for the treatment costs
and sides effects.Comment: This is a preprint of a paper whose final and definite form is with
'Mathematical Biosciences and Engineering', ISSN 1551-0018 (print), ISSN
1547-1063 (online), available at
[http://www.aimsciences.org/journal/1551-0018]. Submitted 27-March-2018;
revised 04-July-2018; accepted for publication 10-July-201
Mathematical models for chemotaxis and their applications in self-organisation phenomena
Chemotaxis is a fundamental guidance mechanism of cells and organisms,
responsible for attracting microbes to food, embryonic cells into developing
tissues, immune cells to infection sites, animals towards potential mates, and
mathematicians into biology. The Patlak-Keller-Segel (PKS) system forms part of
the bedrock of mathematical biology, a go-to-choice for modellers and analysts
alike. For the former it is simple yet recapitulates numerous phenomena; the
latter are attracted to these rich dynamics. Here I review the adoption of PKS
systems when explaining self-organisation processes. I consider their
foundation, returning to the initial efforts of Patlak and Keller and Segel,
and briefly describe their patterning properties. Applications of PKS systems
are considered in their diverse areas, including microbiology, development,
immunology, cancer, ecology and crime. In each case a historical perspective is
provided on the evidence for chemotactic behaviour, followed by a review of
modelling efforts; a compendium of the models is included as an Appendix.
Finally, a half-serious/half-tongue-in-cheek model is developed to explain how
cliques form in academia. Assumptions in which scholars alter their research
line according to available problems leads to clustering of academics and the
formation of "hot" research topics.Comment: 35 pages, 8 figures, Submitted to Journal of Theoretical Biolog
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