699 research outputs found
A Minimal Model of Signaling Network Elucidates Cell-to-Cell Stochastic Variability in Apoptosis
Signaling networks are designed to sense an environmental stimulus and adapt
to it. We propose and study a minimal model of signaling network that can sense
and respond to external stimuli of varying strength in an adaptive manner. The
structure of this minimal network is derived based on some simple assumptions
on its differential response to external stimuli. We employ stochastic
differential equations and probability distributions obtained from stochastic
simulations to characterize differential signaling response in our minimal
network model. We show that the proposed minimal signaling network displays two
distinct types of response as the strength of the stimulus is decreased. The
signaling network has a deterministic part that undergoes rapid activation by a
strong stimulus in which case cell-to-cell fluctuations can be ignored. As the
strength of the stimulus decreases, the stochastic part of the network begins
dominating the signaling response where slow activation is observed with
characteristic large cell-to-cell stochastic variability. Interestingly, this
proposed stochastic signaling network can capture some of the essential
signaling behaviors of a complex apoptotic cell death signaling network that
has been studied through experiments and large-scale computer simulations. Thus
we claim that the proposed signaling network is an appropriate minimal model of
apoptosis signaling. Elucidating the fundamental design principles of complex
cellular signaling pathways such as apoptosis signaling remains a challenging
task. We demonstrate how our proposed minimal model can help elucidate the
effect of a specific apoptotic inhibitor Bcl-2 on apoptotic signaling in a
cell-type independent manner. We also discuss the implications of our study in
elucidating the adaptive strategy of cell death signaling pathways.Comment: 9 pages, 6 figure
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
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
Syntactic Markovian Bisimulation for Chemical Reaction Networks
In chemical reaction networks (CRNs) with stochastic semantics based on
continuous-time Markov chains (CTMCs), the typically large populations of
species cause combinatorially large state spaces. This makes the analysis very
difficult in practice and represents the major bottleneck for the applicability
of minimization techniques based, for instance, on lumpability. In this paper
we present syntactic Markovian bisimulation (SMB), a notion of bisimulation
developed in the Larsen-Skou style of probabilistic bisimulation, defined over
the structure of a CRN rather than over its underlying CTMC. SMB identifies a
lumpable partition of the CTMC state space a priori, in the sense that it is an
equivalence relation over species implying that two CTMC states are lumpable
when they are invariant with respect to the total population of species within
the same equivalence class. We develop an efficient partition-refinement
algorithm which computes the largest SMB of a CRN in polynomial time in the
number of species and reactions. We also provide an algorithm for obtaining a
quotient network from an SMB that induces the lumped CTMC directly, thus
avoiding the generation of the state space of the original CRN altogether. In
practice, we show that SMB allows significant reductions in a number of models
from the literature. Finally, we study SMB with respect to the deterministic
semantics of CRNs based on ordinary differential equations (ODEs), where each
equation gives the time-course evolution of the concentration of a species. SMB
implies forward CRN bisimulation, a recently developed behavioral notion of
equivalence for the ODE semantics, in an analogous sense: it yields a smaller
ODE system that keeps track of the sums of the solutions for equivalent
species.Comment: Extended version (with proofs), of the corresponding paper published
at KimFest 2017 (http://kimfest.cs.aau.dk/
A Dominated Coupling From The Past algorithm for the stochastic simulation of networks of biochemical reactions
<p>Abstract</p> <p>Background</p> <p>In recent years, stochastic descriptions of biochemical reactions based on the Master Equation (ME) have become widespread. These are especially relevant for models involving gene regulation. Gillespie’s Stochastic Simulation Algorithm (SSA) is the most widely used method for the numerical evaluation of these models. The SSA produces exact samples from the distribution of the ME for finite times. However, if the stationary distribution is of interest, the SSA provides no information about convergence or how long the algorithm needs to be run to sample from the stationary distribution with given accuracy. </p> <p>Results</p> <p>We present a proof and numerical characterization of a Perfect Sampling algorithm for the ME of networks of biochemical reactions prevalent in gene regulation and enzymatic catalysis. Our algorithm combines the SSA with Dominated Coupling From The Past (DCFTP) techniques to provide guaranteed sampling from the stationary distribution. The resulting DCFTP-SSA is applicable to networks of reactions with uni-molecular stoichiometries and sub-linear, (anti-) monotone propensity functions. We showcase its applicability studying steady-state properties of stochastic regulatory networks of relevance in synthetic and systems biology.</p> <p>Conclusion</p> <p>The DCFTP-SSA provides an extension to Gillespie’s SSA with guaranteed sampling from the stationary solution of the ME for a broad class of stochastic biochemical networks.</p
Stochastic simulation and analysis of biomolecular reaction networks
<p>Abstract</p> <p>Background</p> <p>In recent years, several stochastic simulation algorithms have been developed to generate Monte Carlo trajectories that describe the time evolution of the behavior of biomolecular reaction networks. However, the effects of various stochastic simulation and data analysis conditions on the observed dynamics of complex biomolecular reaction networks have not recieved much attention. In order to investigate these issues, we employed a a software package developed in out group, called Biomolecular Network Simulator (BNS), to simulate and analyze the behavior of such systems. The behavior of a hypothetical two gene <it>in vitro </it>transcription-translation reaction network is investigated using the Gillespie exact stochastic algorithm to illustrate some of the factors that influence the analysis and interpretation of these data.</p> <p>Results</p> <p>Specific issues affecting the analysis and interpretation of simulation data are investigated, including: (1) the effect of time interval on data presentation and time-weighted averaging of molecule numbers, (2) effect of time averaging interval on reaction rate analysis, (3) effect of number of simulations on precision of model predictions, and (4) implications of stochastic simulations on optimization procedures.</p> <p>Conclusion</p> <p>The two main factors affecting the analysis of stochastic simulations are: (1) the selection of time intervals to compute or average state variables and (2) the number of simulations generated to evaluate the system behavior.</p
Regulatory control and the costs and benefits of biochemical noise
Experiments in recent years have vividly demonstrated that gene expression
can be highly stochastic. How protein concentration fluctuations affect the
growth rate of a population of cells, is, however, a wide open question. We
present a mathematical model that makes it possible to quantify the effect of
protein concentration fluctuations on the growth rate of a population of
genetically identical cells. The model predicts that the population's growth
rate depends on how the growth rate of a single cell varies with protein
concentration, the variance of the protein concentration fluctuations, and the
correlation time of these fluctuations. The model also predicts that when the
average concentration of a protein is close to the value that maximizes the
growth rate, fluctuations in its concentration always reduce the growth rate.
However, when the average protein concentration deviates sufficiently from the
optimal level, fluctuations can enhance the growth rate of the population, even
when the growth rate of a cell depends linearly on the protein concentration.
The model also shows that the ensemble or population average of a quantity,
such as the average protein expression level or its variance, is in general not
equal to its time average as obtained from tracing a single cell and its
descendants. We apply our model to perform a cost-benefit analysis of gene
regulatory control. Our analysis predicts that the optimal expression level of
a gene regulatory protein is determined by the trade-off between the cost of
synthesizing the regulatory protein and the benefit of minimizing the
fluctuations in the expression of its target gene. We discuss possible
experiments that could test our predictions.Comment: Revised manuscript;35 pages, 4 figures, REVTeX4; to appear in PLoS
Computational Biolog
Programmability of Chemical Reaction Networks
Motivated by the intriguing complexity of biochemical circuitry within individual cells we study Stochastic Chemical Reaction Networks (SCRNs), a formal model that considers a set of chemical reactions acting on a finite number of molecules in a well-stirred solution according to standard chemical kinetics equations. SCRNs have been widely used for describing naturally occurring (bio)chemical systems, and with the advent of synthetic biology they become a promising language for the design of artificial biochemical circuits. Our interest here is the computational power of SCRNs and how they relate to more conventional models of computation. We survey known connections and give new connections between SCRNs and Boolean Logic Circuits, Vector Addition Systems, Petri Nets, Gate Implementability, Primitive Recursive Functions, Register Machines, Fractran, and Turing Machines. A theme to these investigations is the thin line between decidable and undecidable questions about SCRN behavior
Construction of a Mean Square Error Adaptive Euler--Maruyama Method with Applications in Multilevel Monte Carlo
A formal mean square error expansion (MSE) is derived for Euler--Maruyama
numerical solutions of stochastic differential equations (SDE). The error
expansion is used to construct a pathwise a posteriori adaptive time stepping
Euler--Maruyama method for numerical solutions of SDE, and the resulting method
is incorporated into a multilevel Monte Carlo (MLMC) method for weak
approximations of SDE. This gives an efficient MSE adaptive MLMC method for
handling a number of low-regularity approximation problems. In low-regularity
numerical example problems, the developed adaptive MLMC method is shown to
outperform the uniform time stepping MLMC method by orders of magnitude,
producing output whose error with high probability is bounded by TOL>0 at the
near-optimal MLMC cost rate O(TOL^{-2}log(TOL)^4).Comment: 43 pages, 12 figure
We are all one together : peer educators\u27 views about falls prevention education for community-dwelling older adults - a qualitative study
Background: Falls are common in older people. Despite strong evidence for effective falls prevention strategies, there appears to be limited translation of these strategies from research to clinical practice. Use of peers in delivering falls prevention education messages has been proposed to improve uptake of falls prevention strategies and facilitate translation to practice. Volunteer peer educators often deliver educational presentations on falls prevention to community-dwelling older adults. However, research evaluating the effectiveness of peer-led education approaches in falls prevention has been limited and no known study has evaluated such a program from the perspective of peer educators involved in delivering the message. The purpose of this study was to explore peer educators’ perspective about their role in delivering peer-led falls prevention education for community-dwelling older adults.
Methods: A two-stage qualitative inductive constant comparative design was used.In stage one (core component) focus group interviews involving a total of eleven participants were conducted. During stage two (supplementary component) semi-structured interviews with two participants were conducted. Data were analysed thematically by two researchers independently. Key themes were identified and findings were displayed in a conceptual framework.
Results: Peer educators were motivated to deliver educational presentations and importantly, to reach an optimal peer connection with their audience. Key themes identified included both personal and organisational factors that impact on educators’ capacity to facilitate their peers’ engagement with the message. Personal factors that facilitated message delivery and engagement included peer-to-peer connection and perceived credibility, while barriers included a reluctance to accept the message that they were at risk of falling by some members in the audience. Organisational factors, including ongoing training for peer educators and formative feedback following presentations, were perceived as essential because they affect successful message delivery.
Conclusions: Peer educators have the potential to effectively deliver falls prevention education to older adults and influence acceptance of the message as they possess the peer-to-peer connection that facilitates optimal engagement. There is a need to consider incorporating learnings from this research into a formal large scale evaluation of the effectiveness of the peer education approach in reducing falls in older adults
- …