Evidence map›Paper›PMID 22723865›Full record

ArticlePloS one2012

Intrinsic noise analyzer: a software package for the exploration of stochastic biochemical kinetics using the system size expansion.

Philipp Thomas, Hannes Matuschek, Ramon Grima

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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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0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

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  7. A Comprehensive View of the β-Arrestinome.Frontiers in endocrinology · 2017
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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Philipp ThomasSchool of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.
Hannes Matuschek
Ramon Grima

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Models, BiologicalModels, ChemicalSoftwareAlgorithmsCircadian ClocksComputer SimulationEnzymesFeedback, PhysiologicalGene Regulatory NetworksInternetIntracellular SpaceKineticsUser-Computer InterfaceEnzymes

Identifiers

PMID22723865
PMCPMC3373587

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.