43,820 research outputs found
SIMMUNE, a tool for simulating and analyzing immune system behavior
We present a new approach to the simulation and analysis of immune system
behavior. The simulations that can be done with our software package called
SIMMUNE are based on immunological data that describe the behavior of immune
system agents (cells, molecules) on a microscopial (i.e. agent-agent
interaction) scale by defining cellular stimulus-response mechanisms. Since the
behavior of the agents in SIMMUNE can be very flexibly configured, its
application is not limited to immune system simulations. We outline the
principles of SIMMUNE's multiscale analysis of emergent structure within the
simulated immune system that allow the identification of immunological contexts
using minimal a priori assumptions about the higher level organization of the
immune system.Comment: 23 pages, 10 figure
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
Comparing System Dynamics and Agent-Based Simulation for Tumour Growth and its Interactions with Effector Cells
There is little research concerning comparisons and combination of System
Dynamics Simulation (SDS) and Agent Based Simulation (ABS). ABS is a paradigm
used in many levels of abstraction, including those levels covered by SDS. We
believe that the establishment of frameworks for the choice between these two
simulation approaches would contribute to the simulation research. Hence, our
work aims for the establishment of directions for the choice between SDS and
ABS approaches for immune system-related problems. Previously, we compared the
use of ABS and SDS for modelling agents' behaviour in an environment with
nomovement or interactions between these agents. We concluded that for these
types of agents it is preferable to use SDS, as it takes up less computational
resources and produces the same results as those obtained by the ABS model. In
order to move this research forward, our next research question is: if we
introduce interactions between these agents will SDS still be the most
appropriate paradigm to be used? To answer this question for immune system
simulation problems, we will use, as case studies, models involving
interactions between tumour cells and immune effector cells. Experiments show
that there are cases where SDS and ABS can not be used interchangeably, and
therefore, their comparison is not straightforward.Comment: 8 pages, 8 figures, 2 tables, International Summer Computer
Simulation Conference 201
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
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
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