A bio-based economy has the potential to provide sustainable substitutes for petroleum-based products and new chemical building blocks for advanced materials. We previously engineered Saccharomyces cerevisiae for industrial production of the isoprenoid artemisinic acid for use in antimalarial treatments. Adapting these strains for biosynthesis of other isoprenoids such as β-farnesene (CH), a plant sesquiterpene with versatile industrial applications, is straightforward. However, S. cerevisiae uses a chemically inefficient pathway for isoprenoid biosynthesis, resulting in yield and productivity limitations incompatible with commodity-scale production. Here we use four non-native metabolic reactions to rewire central carbon metabolism in S. cerevisiae, enabling biosynthesis of cytosolic acetyl coenzyme A (acetyl-CoA, the two-carbon isoprenoid precursor) with a reduced ATP requirement, reduced loss of carbon to CO-emitting reactions, and improved pathway redox balance. We show that strains with rewired central metabolism can devote an identical quantity of sugar to farnesene production as control strains, yet produce 25% more farnesene with that sugar while requiring 75% less oxygen. These changes lower feedstock costs and dramatically increase productivity in industrial fermentations which are by necessity oxygen-constrained. Despite altering key regulatory nodes, engineered strains grow robustly under taxing industrial conditions, maintaining stable yield for two weeks in broth that reaches >15% farnesene by volume. This illustrates that rewiring yeast central metabolism is a viable strategy for cost-effective, large-scale production of acetyl-CoA-derived molecules.

Background: Currently, there exists tens of different microbial and eukaryotic metabolic reconstructions (e.g., Escherichia coli, Saccharomyces cerevisiae, Bacillus subtilis) with many more under development. All of these reconstructions are inherently incomplete with some functionalities missing due to the lack of experimental and/or homology information. A key challenge in the automated generation of genome-scale reconstructions is the elucidation of these gaps and the subsequent generation of hypotheses to bridge them.

With a genome size of ∼580 kb and approximately 480 protein coding regions, Mycoplasma genitalium is one of the smallest known self-replicating organisms and, additionally, has extremely fastidious nutrient requirements. The reduced genomic content of M. genitalium has led researchers to suggest that the molecular assembly contained in this organism may be a close approximation to the minimal set of genes required for bacterial growth. Here, we introduce a systematic approach for the construction and curation of a genome-scale in silico metabolic model for M. genitalium. Key challenges included estimation of biomass composition, handling of enzymes with broad specificities, and the lack of a defined medium. Computational tools were subsequently employed to identify and resolve connectivity gaps in the model as well as growth prediction inconsistencies with gene essentiality experimental data. The curated model, M. genitalium iPS189 (262 reactions, 274 metabolites), is 87% accurate in recapitulating in vivo gene essentiality results for M. genitalium. Approaches and tools described herein provide a roadmap for the automated construction of in silico metabolic models of other organisms.

A key consideration in metabolic engineering is the determination of fluxes of the metabolites within the cell. This determination provides an unambiguous description of metabolism before and/or after engineering interventions. Here, we present a computational framework that combines a constraintbased modeling framework with isotopic label tracing on a large-scale. When cells are fed a growth substrate with certain carbon positions labeled with 13 C, the distribution of this label in the intracellular metabolites can be calculated based on the known biochemistry of the participating pathways. Most labeling studies focus on skeletal representations of central metabolism and ignore many flux routes that could contribute to the observed isotopic labeling patterns. In contrast, our approach investigates the importance of carrying out isotopic labeling studies using a more comprehensive reaction network consisting of 350 fluxes and 184 metabolites in Escherichia coli including global metabolite balances on cofactors such as ATP, NADH, and NADPH. The proposed procedure is demonstrated on an E. coli strain engineered to produce amorphadiene, a precursor to the anti-malarial drug artemisinin. The cells were grown in continuous culture on glucose containing 20% [U-13 C]glucose; the measurements are made using GC-MS performed on 13 amino acids extracted from the cells. We identify flux distributions for which the calculated labeling patterns agree well with the measurements alluding to the accuracy of the network reconstruction. Furthermore, we explore the robustness of the flux calculations to variability in the experimental MS measurements, as well as highlight the key experimental measurements necessary for flux determination. Finally, we discuss the effect of reducing the model, as well as shed light onto the customization of the developed computational framework to other systems.

Background: Recent years has witnessed an increasing number of studies on constructing simple synthetic genetic circuits that exhibit desired properties such as oscillatory behavior, inducer specific activation/repression, etc. It has been widely acknowledged that that task of building circuits to meet multiple inducer-specific requirements is a challenging one. This is because of the incomplete description of component interactions compounded by the fact that the number of ways in which one can chose and interconnect components, increases exponentially with the number of components.