Evidence map›Paper›PMID 21186362›Full record

ArticleNature methods2011

Efficient modeling, simulation and coarse-graining of biological complexity with NFsim.

Michael W Sneddon, James R Faeder, Thierry Emonet

Abstract read
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In one paragraph

Article in Nature methods, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 134 papers.

0numbers the graph read from it
0cells of the map it votes in
134citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

134 citing papers in PubMed.

  1. Article
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  8. A spatial model of autophosphorylation of CabioRxiv : the preprint server for biology · 2024
    Article
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  10. bnglViz: online visualization of rule-based models.Bioinformatics (Oxford, England) · 2024
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Verifiable biology.Journal of the Royal Society, Interface · 2023
    Review
  18. Article
  19. Article
  20. Review

74 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Michael W SneddonDepartment of Molecular, Cellular and Developmental Biology, Yale University, New Haven, Connecticut, USA.
James R Faeder
Thierry Emonet

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Managing the overwhelming numbers of molecular states and interactions is a fundamental obstacle to building predictive models of biological systems. Here we introduce the Network-Free Stochastic Simulator (NFsim), a general-purpose modeling platform that overcomes the combinatorial nature of molecular interactions. Unlike standard simulators that represent molecular species as variables in equations, NFsim uses a biologically intuitive representation: objects with binding and modification sites acted on by reaction rules. During simulations, rules operate directly on molecular objects to produce exact stochastic results with performance that scales independently of the reaction network size. Reaction rates can be defined as arbitrary functions of molecular states to provide powerful coarse-graining capabilities, for example to merge Boolean and kinetic representations of biological networks. NFsim enables researchers to simulate many biological systems that were previously inaccessible to general-purpose software, as we illustrate with models of immune system signaling, microbial signaling, cytoskeletal assembly and oscillating gene expression.

Indexed as

Computer SimulationModels, BiologicalSoftware DesignStochastic ProcessesBiochemistryKineticsPhosphorylation

Identifiers

PMID21186362

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.