In:
Biotechnology and Bioengineering, Wiley, Vol. 120, No. 6 ( 2023-06), p. 1623-1639
Abstract:
Genome‐scale metabolic models (GEMs) have been widely used to guide the computational design of microbial cell factories, and to date, seven GEMs have been reported for Bacillus subtilis , a model gram‐positive microorganism widely used in bioproduction of functional nutraceuticals and food ingredients. However, none of them are widely used because they often lead to erroneous predictions due to their low predictive power and lack of information on regulatory mechanisms. In this work, we constructed a new version of GEM for B. subtilis ( i Bsu1209), which contains 1209 genes, 1595 metabolites, and 1948 reactions. We applied machine learning to fill gaps, which formed a relatively complete metabolic network able to predict with high accuracy (89.3%) the growth of 1209 mutants under 12 different culture conditions. In addition, we developed a visualization and code‐free software, Model Tool, for multiconstraints model reconstruction and analysis. We used this software to construct et i Bsu1209, a multiscale model that integrates enzymatic constraints, thermodynamic constraints, and transcriptional regulatory networks. Furthermore, we used et i Bsu1209 to guide a metabolic engineering strategy (knocking out fabI and yfkN genes) for the overproduction of nutraceutical menaquinone‐7, and the titer increased to 153.94 mg/L, 2.2‐times that of the parental strain. To the best of our knowledge, et i Bsu1209 is the first comprehensive multiscale model for B. subtilis and can serve as a solid basis for rational computational design of B. subtilis cell factories for bioproduction.
Type of Medium:
Online Resource
ISSN:
0006-3592
,
1097-0290
Language:
English
Publisher:
Wiley
Publication Date:
2023
detail.hit.zdb_id:
1480809-2
detail.hit.zdb_id:
280318-5
SSG:
12
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