Evidence map›Paper›PMID 29644520›Full record

ArticleBulletin of mathematical biology2019

The Linear Noise Approximation for Spatially Dependent Biochemical Networks.

Per Lötstedt

Abstract read
In one paragraph

Article in Bulletin of mathematical biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Per LötstedtDivision of Scientific Computing, Department of Information Technology, Uppsala University, SE-75105, Uppsala, Sweden. perl@it.uu.se.ORCID 0000-0003-2143-3078

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An algorithm for computing the linear noise approximation (LNA) of the reaction-diffusion master equation (RDME) is developed and tested. The RDME is often used as a model for biochemical reaction networks. The LNA is derived for a general discretization of the spatial domain of the problem. If M is the number of chemical species in the network and N is the number of nodes in the discretization in space, then the computational work to determine approximations of the mean and the covariances of the probability distributions is proportional to [Formula: see text] in a straightforward implementation. In our LNA algorithm, the work is proportional to [Formula: see text]. Since N usually is larger than M, this is a significant reduction. The accuracy of the approximation in the algorithm is estimated analytically and evaluated in numerical experiments.

Indexed as

AlgorithmsModels, BiologicalModels, ChemicalAnalysis of VarianceBiochemical PhenomenaComputer SimulationDiffusionLinear ModelsMarkov ChainsMathematical ConceptsProbabilityStochastic ProcessesSystems BiologyFast algorithmLinear noise approximationSpatially dependent

Identifiers

PMID29644520
PMCPMC6677697

What OpenQuestion holds

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Registered trials

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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.