24,475 research outputs found
Combination interventions for Hepatitis C and Cirrhosis reduction among people who inject drugs: An agent-based, networked population simulation experiment
Hepatitis C virus (HCV) infection is endemic in people who inject drugs
(PWID), with prevalence estimates above 60 percent for PWID in the United
States. Previous modeling studies suggest that direct acting antiviral (DAA)
treatment can lower overall prevalence in this population, but treatment is
often delayed until the onset of advanced liver disease (fibrosis stage 3 or
later) due to cost. Lower cost interventions featuring syringe access (SA) and
medically assisted treatment (MAT) for addiction are known to be less costly,
but have shown mixed results in lowering HCV rates below current levels. Little
is known about the potential synergistic effects of combining DAA and MAT
treatment, and large-scale tests of combined interventions are rare. While
simulation experiments can reveal likely long-term effects, most prior
simulations have been performed on closed populations of model agents--a
scenario quite different from the open, mobile populations known to most health
agencies. This paper uses data from the Centers for Disease Control's National
HIV Behavioral Surveillance project, IDU round 3, collected in New York City in
2012 by the New York City Department of Health and Mental Hygiene to
parameterize simulations of open populations. Our results show that, in an open
population, SA/MAT by itself has only small effects on HCV prevalence, while
DAA treatment by itself can significantly lower both HCV and HCV-related
advanced liver disease prevalence. More importantly, the simulation experiments
suggest that cost effective synergistic combinations of the two strategies can
dramatically reduce HCV incidence. We conclude that adopting SA/MAT
implementations alongside DAA interventions can play a critical role in
reducing the long-term consequences of ongoing infection
MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME
Computational experiments using spatial stochastic simulations have led to
important new biological insights, but they require specialized tools, a
complex software stack, as well as large and scalable compute and data analysis
resources due to the large computational cost associated with Monte Carlo
computational workflows. The complexity of setting up and managing a
large-scale distributed computation environment to support productive and
reproducible modeling can be prohibitive for practitioners in systems biology.
This results in a barrier to the adoption of spatial stochastic simulation
tools, effectively limiting the type of biological questions addressed by
quantitative modeling. In this paper, we present PyURDME, a new, user-friendly
spatial modeling and simulation package, and MOLNs, a cloud computing appliance
for distributed simulation of stochastic reaction-diffusion models. MOLNs is
based on IPython and provides an interactive programming platform for
development of sharable and reproducible distributed parallel computational
experiments
Developing and applying heterogeneous phylogenetic models with XRate
Modeling sequence evolution on phylogenetic trees is a useful technique in
computational biology. Especially powerful are models which take account of the
heterogeneous nature of sequence evolution according to the "grammar" of the
encoded gene features. However, beyond a modest level of model complexity,
manual coding of models becomes prohibitively labor-intensive. We demonstrate,
via a set of case studies, the new built-in model-prototyping capabilities of
XRate (macros and Scheme extensions). These features allow rapid implementation
of phylogenetic models which would have previously been far more
labor-intensive. XRate's new capabilities for lineage-specific models,
ancestral sequence reconstruction, and improved annotation output are also
discussed. XRate's flexible model-specification capabilities and computational
efficiency make it well-suited to developing and prototyping phylogenetic
grammar models. XRate is available as part of the DART software package:
http://biowiki.org/DART .Comment: 34 pages, 3 figures, glossary of XRate model terminolog
Detailed simulations of cell biology with Smoldyn 2.1.
Most cellular processes depend on intracellular locations and random collisions of individual protein molecules. To model these processes, we developed algorithms to simulate the diffusion, membrane interactions, and reactions of individual molecules, and implemented these in the Smoldyn program. Compared to the popular MCell and ChemCell simulators, we found that Smoldyn was in many cases more accurate, more computationally efficient, and easier to use. Using Smoldyn, we modeled pheromone response system signaling among yeast cells of opposite mating type. This model showed that secreted Bar1 protease might help a cell identify the fittest mating partner by sharpening the pheromone concentration gradient. This model involved about 200,000 protein molecules, about 7000 cubic microns of volume, and about 75 minutes of simulated time; it took about 10 hours to run. Over the next several years, as faster computers become available, Smoldyn will allow researchers to model and explore systems the size of entire bacterial and smaller eukaryotic cells
Programmable models of growth and mutation of cancer-cell populations
In this paper we propose a systematic approach to construct mathematical
models describing populations of cancer-cells at different stages of disease
development. The methodology we propose is based on stochastic Concurrent
Constraint Programming, a flexible stochastic modelling language. The
methodology is tested on (and partially motivated by) the study of prostate
cancer. In particular, we prove how our method is suitable to systematically
reconstruct different mathematical models of prostate cancer growth - together
with interactions with different kinds of hormone therapy - at different levels
of refinement.Comment: In Proceedings CompMod 2011, arXiv:1109.104
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