1,064 research outputs found
Oscillating epidemics in a dynamic network model: stochastic and mean-field analysis
An adaptive network model using SIS epidemic propagation with link-type-dependent link activation and deletion is considered. Bifurcation analysis of the pairwise ODE approximation and the network-based stochastic simulation is carried out, showing that three typical behaviours may occur; namely, oscillations can be observed besides disease-free or endemic steady states. The oscillatory behaviour in the stochastic simulations is studied using Fourier analysis, as well as through analysing the exact master equations of the stochastic model. By going beyond simply comparing simulation results to mean-field models, our approach yields deeper insights into the observed phenomena and help better understand and map out the limitations of mean-field models
The transition between stochastic and deterministic behavior in an excitable gene circuit
We explore the connection between a stochastic simulation model and an
ordinary differential equations (ODEs) model of the dynamics of an excitable
gene circuit that exhibits noise-induced oscillations. Near a bifurcation point
in the ODE model, the stochastic simulation model yields behavior dramatically
different from that predicted by the ODE model. We analyze how that behavior
depends on the gene copy number and find very slow convergence to the large
number limit near the bifurcation point. The implications for understanding the
dynamics of gene circuits and other birth-death dynamical systems with small
numbers of constituents are discussed.Comment: PLoS ONE: Research Article, published 11 Apr 201
The interplay of intrinsic and extrinsic bounded noises in genetic networks
After being considered as a nuisance to be filtered out, it became recently
clear that biochemical noise plays a complex role, often fully functional, for
a genetic network. The influence of intrinsic and extrinsic noises on genetic
networks has intensively been investigated in last ten years, though
contributions on the co-presence of both are sparse. Extrinsic noise is usually
modeled as an unbounded white or colored gaussian stochastic process, even
though realistic stochastic perturbations are clearly bounded. In this paper we
consider Gillespie-like stochastic models of nonlinear networks, i.e. the
intrinsic noise, where the model jump rates are affected by colored bounded
extrinsic noises synthesized by a suitable biochemical state-dependent Langevin
system. These systems are described by a master equation, and a simulation
algorithm to analyze them is derived. This new modeling paradigm should enlarge
the class of systems amenable at modeling.
We investigated the influence of both amplitude and autocorrelation time of a
extrinsic Sine-Wiener noise on: the Michaelis-Menten approximation of
noisy enzymatic reactions, which we show to be applicable also in co-presence
of both intrinsic and extrinsic noise, a model of enzymatic futile cycle
and a genetic toggle switch. In and we show that the
presence of a bounded extrinsic noise induces qualitative modifications in the
probability densities of the involved chemicals, where new modes emerge, thus
suggesting the possibile functional role of bounded noises
BigraphER: rewriting and analysis engine for bigraphs
BigraphER is a suite of open-source tools providing an effi-
cient implementation of rewriting, simulation, and visualisation for bigraphs,
a universal formalism for modelling interacting systems that
evolve in time and space and first introduced by Milner. BigraphER consists
of an OCaml library that provides programming interfaces for the
manipulation of bigraphs, their constituents and reaction rules, and a
command-line tool capable of simulating Bigraphical Reactive Systems
(BRSs) and computing their transition systems. Other features are native
support for both bigraphs and bigraphs with sharing, stochastic reaction
rules, rule priorities, instantiation maps, parameterised controls, predicate
checking, graphical output and integration with the probabilistic
model checker PRISM
Colored extrinsic fluctuations and stochastic gene expression
Stochasticity is both exploited and controlled by cells. Although the intrinsic stochasticity inherent in biochemistry is relatively well understood, cellular variation, or ‘noise', is predominantly generated by interactions of the system of interest with other stochastic systems in the cell or its environment. Such extrinsic fluctuations are nonspecific, affecting many system components, and have a substantial lifetime, comparable to the cell cycle (they are ‘colored'). Here, we extend the standard stochastic simulation algorithm to include extrinsic fluctuations. We show that these fluctuations affect mean protein numbers and intrinsic noise, can speed up typical network response times, and can explain trends in high-throughput measurements of variation. If extrinsic fluctuations in two components of the network are correlated, they may combine constructively (amplifying each other) or destructively (attenuating each other). Consequently, we predict that incoherent feedforward loops attenuate stochasticity, while coherent feedforwards amplify it. Our results demonstrate that both the timescales of extrinsic fluctuations and their nonspecificity substantially affect the function and performance of biochemical networks
Simple, Fast and Accurate Implementation of the Diffusion Approximation Algorithm for Stochastic Ion Channels with Multiple States
The phenomena that emerge from the interaction of the stochastic opening and
closing of ion channels (channel noise) with the non-linear neural dynamics are
essential to our understanding of the operation of the nervous system. The
effects that channel noise can have on neural dynamics are generally studied
using numerical simulations of stochastic models. Algorithms based on discrete
Markov Chains (MC) seem to be the most reliable and trustworthy, but even
optimized algorithms come with a non-negligible computational cost. Diffusion
Approximation (DA) methods use Stochastic Differential Equations (SDE) to
approximate the behavior of a number of MCs, considerably speeding up
simulation times. However, model comparisons have suggested that DA methods did
not lead to the same results as in MC modeling in terms of channel noise
statistics and effects on excitability. Recently, it was shown that the
difference arose because MCs were modeled with coupled activation subunits,
while the DA was modeled using uncoupled activation subunits. Implementations
of DA with coupled subunits, in the context of a specific kinetic scheme,
yielded similar results to MC. However, it remained unclear how to generalize
these implementations to different kinetic schemes, or whether they were faster
than MC algorithms. Additionally, a steady state approximation was used for the
stochastic terms, which, as we show here, can introduce significant
inaccuracies. We derived the SDE explicitly for any given ion channel kinetic
scheme. The resulting generic equations were surprisingly simple and
interpretable - allowing an easy and efficient DA implementation. The algorithm
was tested in a voltage clamp simulation and in two different current clamp
simulations, yielding the same results as MC modeling. Also, the simulation
efficiency of this DA method demonstrated considerable superiority over MC
methods.Comment: 32 text pages, 10 figures, 1 supplementary text + figur
Global Versus Local Computations: Fast Computing with Identifiers
This paper studies what can be computed by using probabilistic local
interactions with agents with a very restricted power in polylogarithmic
parallel time. It is known that if agents are only finite state (corresponding
to the Population Protocol model by Angluin et al.), then only semilinear
predicates over the global input can be computed. In fact, if the population
starts with a unique leader, these predicates can even be computed in a
polylogarithmic parallel time. If identifiers are added (corresponding to the
Community Protocol model by Guerraoui and Ruppert), then more global predicates
over the input multiset can be computed. Local predicates over the input sorted
according to the identifiers can also be computed, as long as the identifiers
are ordered. The time of some of those predicates might require exponential
parallel time. In this paper, we consider what can be computed with Community
Protocol in a polylogarithmic number of parallel interactions. We introduce the
class CPPL corresponding to protocols that use , for some k,
expected interactions to compute their predicates, or equivalently a
polylogarithmic number of parallel expected interactions. We provide some
computable protocols, some boundaries of the class, using the fact that the
population can compute its size. We also prove two impossibility results
providing some arguments showing that local computations are no longer easy:
the population does not have the time to compare a linear number of consecutive
identifiers. The Linearly Local languages, such that the rational language
, are not computable.Comment: Long version of SSS 2016 publication, appendixed version of SIROCCO
201
Bayesian inference of biochemical kinetic parameters using the linear noise approximation
Background
Fluorescent and luminescent gene reporters allow us to dynamically quantify changes in molecular species concentration over time on the single cell level. The mathematical modeling of their interaction through multivariate dynamical models requires the deveopment of effective statistical methods to calibrate such models against available data. Given the prevalence of stochasticity and noise in biochemical systems inference for stochastic models is of special interest. In this paper we present a simple and computationally efficient algorithm for the estimation of biochemical kinetic parameters from gene reporter data.
Results
We use the linear noise approximation to model biochemical reactions through a stochastic dynamic model which essentially approximates a diffusion model by an ordinary differential equation model with an appropriately defined noise process. An explicit formula for the likelihood function can be derived allowing for computationally efficient parameter estimation. The proposed algorithm is embedded in a Bayesian framework and inference is performed using Markov chain Monte Carlo.
Conclusion
The major advantage of the method is that in contrast to the more established diffusion approximation based methods the computationally costly methods of data augmentation are not necessary. Our approach also allows for unobserved variables and measurement error. The application of the method to both simulated and experimental data shows that the proposed methodology provides a useful alternative to diffusion approximation based methods
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