Evidence map›Paper›PMID 29206229›Full record

ArticlePLoS computational biology2017

pSSAlib: The partial-propensity stochastic chemical network simulator.

Oleksandr Ostrenko, Pietro Incardona, Rajesh Ramaswamy, Lutz Brusch, Ivo F Sbalzarini

Abstract read
In one paragraph

Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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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

4 citing papers in PubMed.

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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

5 authors.

Oleksandr OstrenkoCenter for Information Services and High Performance Computing, TU Dresden, Dresden, Germany.
Pietro IncardonaChair of Scientific Computing for Systems Biology, Faculty of Computer Science, TU Dresden, Dresden, Germany.
Rajesh RamaswamyMax Planck Institute for the Physics of Complex Systems, Dresden, Germany.
Lutz BruschCenter for Information Services and High Performance Computing, TU Dresden, Dresden, Germany.ORCID http://orcid.org/0000-0003-0137-5106
Ivo F SbalzariniCenter for Advancing Electronics Dresden, TU Dresden, Dresden, Germany.ORCID http://orcid.org/0000-0003-4414-4340

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chemical reaction networks are ubiquitous in biology, and their dynamics is fundamentally stochastic. Here, we present the software library pSSAlib, which provides a complete and concise implementation of the most efficient partial-propensity methods for simulating exact stochastic chemical kinetics. pSSAlib can import models encoded in Systems Biology Markup Language, supports time delays in chemical reactions, and stochastic spatiotemporal reaction-diffusion systems. It also provides tools for statistical analysis of simulation results and supports multiple output formats. It has previously been used for studies of biochemical reaction pathways and to benchmark other stochastic simulation methods. Here, we describe pSSAlib in detail and apply it to a new model of the endocytic pathway in eukaryotic cells, leading to the discovery of a stochastic counterpart of the cut-out switch motif underlying early-to-late endosome conversion. pSSAlib is provided as a stand-alone command-line tool and as a developer API. We also provide a plug-in for the SBMLToolbox. The open-source code and pre-packaged installers are freely available from http://mosaic.mpi-cbg.de.

Indexed as

Models, BiologicalSoftwareStochastic ProcessesAlgorithmsBiochemical PhenomenaComputational BiologyComputer SimulationKineticsReproducibility of Results

Identifiers

PMID29206229
PMCPMC5730222

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LicenceCC BY
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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.