Evidence map›Paper›PMID 30335785›Full record

ArticlePLoS computational biology2018

RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor.

Hao Wang, Simonas Marcišauskas, Benjamín J Sánchez, Iván Domenzain, Daniel Hermansson, Rasmus Agren, Jens Nielsen, Eduard J Kerkhoven

Abstract read
In one paragraph

Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 186 papers.

0numbers the graph read from it
0cells of the map it votes in
186citing 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

186 citing papers in PubMed.

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  16. Computational and structural biotechnology journal · 2026
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  18. Evaluating Metabolic Support in Pairwise Microbial Communities Using MetQuest.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  19. Article
  20. Article

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

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

8 authors.

Hao WangDepartment of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.ORCID 0000-0001-7475-0136
Simonas MarcišauskasDepartment of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.ORCID 0000-0003-0770-6383
Benjamín J SánchezDepartment of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.ORCID 0000-0001-6093-4110
Iván DomenzainDepartment of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.ORCID 0000-0002-5322-2040
Daniel HermanssonDepartment of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
Rasmus AgrenDepartment of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Gothenburg, Sweden.
Jens NielsenDepartment of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.ORCID 0000-0002-9955-6003
Eduard J KerkhovenDepartment of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.ORCID 0000-0002-3593-5792

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

RAVEN is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present RAVEN Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of RAVEN 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor. Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S. coelicolor GEM, exemplifies that RAVEN 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor, with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that RAVEN 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-to-date databases. Both RAVEN 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community (https://github.com/SysBioChalmers/RAVEN and https://github.com/SysBioChalmers/Streptomyces_coelicolor-GEM).

Indexed as

SoftwareComputational BiologyComputer SimulationDatabases, GeneticGene EditingMetabolic Networks and PathwaysModels, GeneticStreptomyces coelicolor

Identifiers

PMID30335785
PMCPMC6207324

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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.