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. A cell's macro-molecular composition is not fixed and it responds to changes in environmental conditions. As a consequence, initiatives for the high-fidelity determination of cellular biomass composition have been launched. Thus, there is a need for a mathematical and computational framework capable of using multiple measurements of cellular biomass composition in different environments. Here, we propose two different computational approaches for directly addressing this challenge: Biomass Trade-off Weighting (BTW) and Higher-dimensional-plane InterPolation (HIP). In lieu of experimental data on biomass composition-variation in response to changing nutrient environment, we assess the properties of BTW and HIP using three hypothetical, yet biologically plausible, BOFs for the Escherichia coli genome-scale metabolic model iML1515. We find that the BTW and HIP formulations have a significant impact on model performance and phenotypes. Furthermore, the BTW method generates larger growth rates in all environments when compared to HIP. Using acetate secretion and the respiratory quotient as proxies for phenotypic changes, we find marked differences between the methods as HIP generates BOFs more similar to a reference BOF than BTW. We conclude that the presented methods constitute a conceptual step in developing genome-scale metabolic modelling approaches capable of addressing the inherent dependence of cellular biomass composition on nutrient environments.

Many biosynthetic gene clusters (BGCs) require heterologous expression to realize their genetic potential, including silent and metagenomic BGCs. Although the engineered Streptomyces coelicolor 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. Instrumental to this comparison was the application of an improved consensus genome-scale metabolic model (GEM) of. S. coelicolor Although many metabolic patterns are retained in M1152, we find that this strain suffers from oxidative stress, possibly caused by increased oxidative metabolism. Furthermore, precursor availability is likely not limiting polyketide production, implying that other strategies could be beneficial for further development of S. coelicolor for heterologous production of novel compounds.

Many biosynthetic gene clusters (BGCs) in the genomes of environmental microorganisms require heterologous expression in order to realize their genetic potential, including cryptic and metagenomic BGCs. Streptomyces coelicolor M1152 is a widely used host strain for the heterologous expression of BGCs, as it has been genetically engineered for this purpose via the deletion of four of its native biosynthetic gene clusters (BGCs) and the introduction of a point mutation in the rpoB gene that encodes the beta subunit of RNA polymerase. This latter mutation was shown to have a strong positive impact on antibiotic biosynthesis via processes that remain poorly understood. Therefore, a systemic understanding of the consequences on cellular metabolism of the genomic changes of M1152 could greatly contribute to this understanding. Here we carried out a comparative analysis of M1152 and its ancestor strain M145, connecting observed phenotypic differences to changes in transcript and protein abundance. Measured protein abundance was used to constrain an amended genome-scale model (GEM) and to predict metabolic fluxes. This approach connects observed differences in growth rate and glucose consumption to changes in central carbon metabolism, accompanied by differential expression of important regulons. Our results suggest that precursor availability is not limiting the biosynthesis of secondary metabolites. This implies that alternative strategies could be beneficial for further development of S. coelicolor for heterologous production of novel compounds.

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. Genome-scale metabolic reconstructions have successfully been used in several biotechnology applications to facilitate the over-production of target metabolites. Here, the authors present iKS1317, the most comprehensive and accurate reconstructed genome-scale metabolic model (GEM) for S. coelicolor. The model reconstruction is based on previous models, publicly available databases, and published literature and includes 1317 genes, 2119 reactions, and 1581 metabolites. It correctly predicts wild-type growth in 96.5% of the evaluated growth environments and gene knockout predictions in 78.4% when comparing with observed mutant growth phenotypes, with a total accuracy of 83.3%. However, using a minimal nutrient environment for the gene knockout predictions, iKS1317 has an accuracy of 87.1% in predicting mutant growth phenotypes. Furthermore, we used iKS1317 and existing strain design algorithms to suggest robust gene-knockout strategies to increase the production of acetyl-CoA. Since acetyl-CoA is the most important precursor for polyketide antibiotics, the suggested strategies may be implemented in vivo to improve the function of S. coelicolor as a heterologous expression host.