Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition.

Genome-scale metabolic modeling is an important tool in the study of metabolism by enhancing the collation of knowledge, interpretation of data, and prediction of metabolic capabilities. A frequent assumption in the use of genome-scale models is that the in vivo organism is evolved for optimal growth, where growth is represented by flux through a biomass objective function (BOF). While the specific composition of the BOF is crucial, its formulation is often inherited from similar organisms due to the experimental challenges associated with its proper determination.

Enzyme-Constrained Models and Omics Analysis of Reveal Metabolic Changes that Enhance Heterologous Production.

Many biosynthetic gene clusters (BGCs) require heterologous expression to realize their genetic potential, including silent and metagenomic BGCs. Although the engineered M1152 is a widely used host for heterologous expression of BGCs, a systemic understanding of how its genetic modifications affect the metabolism is lacking and limiting further development. We performed a comparative analysis of M1152 and its ancestor M145, connecting information from proteomics, transcriptomics, and cultivation data into a comprehensive picture of the metabolic differences between these strains.

Predicting Strain Engineering Strategies Using iKS1317: A Genome-Scale Metabolic Model of Streptomyces coelicolor.

Streptomyces coelicolor is a model organism for the Actinobacteria, a phylum known to produce an extensive range of different bioactive compounds that include antibiotics currently used in the clinic. Biosynthetic gene clusters discovered in genomes of other Actinobacteria can be transferred to and expressed in S. coelicolor, making it a factory for heterologous production of secondary metabolites.