ArticlePloS one2012
Intrinsic noise analyzer: a software package for the exploration of stochastic biochemical kinetics using the system size expansion.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
- Accurate quantification of uncertainty in epidemic parameter estimates and predictions using stochastic compartmental models.Statistical methods in medical research · 2019Article
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- Use of daily Internet search query data improves real-time projections of influenza epidemics.Journal of the Royal Society, Interface · 2018Article
- SELANSI: a toolbox for simulation of stochastic gene regulatory networks.Bioinformatics (Oxford, England) · 2018Article
- A Likelihood Approach for Real-Time Calibration of Stochastic Compartmental Epidemic Models.PLoS computational biology · 2017Article
- The Impact of Topic Characteristics and Threat on Willingness to Engage with Wikipedia Articles: Insights from Laboratory Experiments.Frontiers in psychology · 2017Article
- A Comprehensive View of the β-Arrestinome.Frontiers in endocrinology · 2017Article
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- Stochastic Simulation of Biomolecular Networks in Dynamic Environments.PLoS computational biology · 2016Article
- Analytical approximations for spatial stochastic gene expression in single cells and tissues.Journal of the Royal Society, Interface · 2016Article
- CERENA: ChEmical REaction Network Analyzer--A Toolbox for the Simulation and Analysis of Stochastic Chemical Kinetics.PloS one · 2016Article
- Deterministic inference for stochastic systems using multiple shooting and a linear noise approximation for the transition probabilities.IET systems biology · 2015Article
- Exploiting intrinsic fluctuations to identify model parameters.IET systems biology · 2015Article
- Structure-energy-based predictions and network modelling of RASopathy and cancer missense mutations.Molecular systems biology · 2014Article
- Mathematical models light up plant signaling.The Plant cell · 2014Review
- Modeling heterogeneous responsiveness of intrinsic apoptosis pathway.BMC systems biology · 2013Article
- Systems biology: the role of engineering in the reverse engineering of biological signaling.Cells · 2013Article
- How reliable is the linear noise approximation of gene regulatory networks?BMC genomics · 2013Article
- Effects of bursty protein production on the noisy oscillatory properties of downstream pathways.Scientific reports · 2013Article
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3 authors.
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Abstract
The accepted stochastic descriptions of biochemical dynamics under well-mixed conditions are given by the Chemical Master Equation and the Stochastic Simulation Algorithm, which are equivalent. The latter is a Monte-Carlo method, which, despite enjoying broad availability in a large number of existing software packages, is computationally expensive due to the huge amounts of ensemble averaging required for obtaining accurate statistical information. The former is a set of coupled differential-difference equations for the probability of the system being in any one of the possible mesoscopic states; these equations are typically computationally intractable because of the inherently large state space. Here we introduce the software package intrinsic Noise Analyzer (iNA), which allows for systematic analysis of stochastic biochemical kinetics by means of van Kampen's system size expansion of the Chemical Master Equation. iNA is platform independent and supports the popular SBML format natively. The present implementation is the first to adopt a complementary approach that combines state-of-the-art analysis tools using the computer algebra system Ginac with traditional methods of stochastic simulation. iNA integrates two approximation methods based on the system size expansion, the Linear Noise Approximation and effective mesoscopic rate equations, which to-date have not been available to non-expert users, into an easy-to-use graphical user interface. In particular, the present methods allow for quick approximate analysis of time-dependent mean concentrations, variances, covariances and correlations coefficients, which typically outperforms stochastic simulations. These analytical tools are complemented by automated multi-core stochastic simulations with direct statistical evaluation and visualization. We showcase iNA's performance by using it to explore the stochastic properties of cooperative and non-cooperative enzyme kinetics and a gene network associated with circadian rhythms. The software iNA is freely available as executable binaries for Linux, MacOSX and Microsoft Windows, as well as the full source code under an open source license.
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