RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor
Artikel i vetenskaplig tidskrift, 2018

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).

Författare

Hao Wang

Wallenberg Lab.

CSBI

Simonas Marcisauskas

Chalmers, Biologi och bioteknik, Systembiologi

Benjamin José Sanchez Barja

Chalmers, Biologi och bioteknik, Systembiologi

Iván Domenzain Del Castillo Cerecer

Chalmers, Biologi och bioteknik, Systembiologi

Daniel Hermansson

Chalmers, Biologi och bioteknik, Systembiologi

Rasmus Ågren

CSBI

Jens B Nielsen

Chalmers, Biologi och bioteknik, Systembiologi

Danmarks Tekniske Universitet (DTU)

Eduard Kerkhoven

Chalmers, Biologi och bioteknik, Systembiologi

PLoS Computational Biology

1553-734X (ISSN) 1553-7358 (eISSN)

Vol. 14 10 e1006541-

Ämneskategorier

Bioinformatik (beräkningsbiologi)

Sannolikhetsteori och statistik

Datavetenskap (datalogi)

DOI

10.1371/journal.pcbi.1006541

PubMed

30335785

Mer information

Senast uppdaterat

2019-08-27