Genes placed under the control of the arabinose-inducible araBAD promoter (P BAD ) of Escherichia coli are expressed in an all-or-none fashion, in which the percentage of induced cells in the population, rather than the degree of induction in individual cells, varies with the concentration of arabinose in the culture medium. Previous work showed that all-or-none gene expression from P BAD was due to the arabinose-dependent expression of the gene encoding the low-affinity high-capacity transporter (araE), and that expression of heterologous genes from P BAD in individual cells could be regulated by placing the araE gene under control of an arabinose-independent promoter. Based on these results, two expression systems were developed to allow regulatable control of genes under control of P BAD . In one system, the native araE promoter on the chromosome was replaced by constitutive promoters of different strengths. In the second system, the araE gene under control of the same constitutive promoters was placed on a medium-copy plasmid. Both systems allow regulatable expression of a plasmid-borne P BAD -controlled heterologous gene and a homogeneous population of cells over a wide range of arabinose concentrations. While the degree of induction varied slightly with the strength of the constitutive promoter, expression was affected most by the arabinose concentration.

Despite the extensive use of Saccharomyces cerevisiae as a platform for synthetic biology, strain engineering remains slow and laborious. Here, we employ CRISPR/Cas9 technology to build a cloning-free toolkit that addresses commonly encountered obstacles in metabolic engineering, including chromosomal integration locus and promoter selection, as well as protein localization and solubility. The toolkit includes 23 Cas9-sgRNA plasmids, 37 promoters of various strengths and temporal expression profiles, and 10 protein-localization, degradation and solubility tags. We facilitated the use of these parts via a web-based tool, that automates the generation of DNA fragments for integration. Our system builds upon existing gene editing methods in the thoroughness with which the parts are standardized and characterized, the types and number of parts available and the ease with which our methodology can be used to perform genetic edits in yeast. We demonstrated the applicability of this toolkit by optimizing the expression of a challenging but industrially important enzyme, taxadiene synthase (TXS). This approach enabled us to diagnose an issue with TXS solubility, the resolution of which yielded a 25-fold improvement in taxadiene production.

The combination of synthetic and systems biology is a powerful framework to study fundamental questions in biology and produce chemicals of immediate practical application such as biofuels, polymers, or therapeutics. However, we cannot yet engineer biological systems as easily and precisely as we engineer physical systems. In this review, we describe the path from the choice of target molecule to scaling production up to commercial volumes. We present and explain some of the current challenges and gaps in our knowledge that must be overcome in order to bring our bioengineering capabilities to the level of other engineering disciplines. Challenges start at molecule selection, where a difficult balance between economic potential and biological feasibility must be struck. Pathway design and construction have recently been revolutionized by next-generation sequencing and exponentially improving DNA synthesis capabilities. Although pathway optimization can be significantly aided by enzyme expression characterization through proteomics, choosing optimal relative protein expression levels for maximum production is still the subject of heuristic, non-systematic approaches. Toxic metabolic intermediates and proteins can significantly affect production, and dynamic pathway regulation emerges as a powerful but yet immature tool to prevent it. Host engineering arises as a much needed complement to pathway engineering for high bioproduct yields; and systems biology approaches such as stoichiometric modeling or growth coupling strategies are required. A final, and often underestimated, challenge is the successful scale up of processes to commercial volumes. Sustained efforts in improving reproducibility and predictability are needed for further development of bioengineering.

Biofoundries provide an integrated infrastructure to enable the rapid design, construction, and testing of genetically reprogrammed organisms for biotechnology applications and research. Many biofoundries are being built and a Global Biofoundry Alliance has recently been established to coordinate activities worldwide.

A stoichiometric model of metabolism was developed to describe the balance of metabolic reactions during steady-state growth of Escherichia coli on glucose (or metabolic intermediates) and mineral salts. The model incorporates 153 reversible and 147 irreversible reactions and 289 metabolites from several metabolic data bases for the biosynthesis of the macromolecular precursors, coenzymes, and prosthetic groups necessary for synthesis of all cellular macromolecules. Correlations describing how the cellular composition changes with growth rate were developed from experimental data and were used to calculate the drain of precursors to macromolecules, coenzymes, and prosthetic groups from the metabolic network for the synthesis of those macromolecules at a specific growth rate. Energy requirements for macromolecular polymerization and proofreading, transport of metabolites, and maintenance of transmembrane gradients were included in the model rather than a lumped maintenance energy term. The underdetermined set of equations was solved using the Simplex algorithm, employing realistic objective functions and constraints; the drain of precursors, coenzymes, and prosthetic groups and the energy requirements for the synthesis of macromolecules served as the primary set of constraints. The model accurately predicted experimentally determined metabolic fluxes for aerobic growth on acetate or acetate plus glucose. In addition, the model predicted the genetic and metabolic regulation that must occur for growth under different conditions, such as the opening of the glyoxylate shunt during growth on acetate and the branching of the tricarboxylic acid cycle under anaerobic growth. Sensitivity analyses were performed to determine the flexibility of pathways and the effects of different rates and growth conditions on the distribution of fluxes.

Type I modular polyketide synthases (PKSs) are polymerases that utilize acyl-CoAs as substrates. Each polyketide elongation reaction is catalyzed by a set of protein domains called a module. Each module usually contains an acyltransferase (AT) domain, which determines the specific acyl-CoA incorporated into each condensation reaction. Although a successful exchange of individual AT domains can lead to the biosynthesis of a large variety of novel compounds, hybrid PKS modules often show significantly decreased activities. Using monomodular PKSs as models, we have systematically analyzed the segments of AT domains and associated linkers in AT exchanges in vitro and have identified the boundaries within a module that can be used to exchange AT domains while maintaining protein stability and enzyme activity. Importantly, the optimized domain boundary is highly conserved, which facilitates AT domain replacements in most type I PKS modules. To further demonstrate the utility of the optimized AT domain boundary, we have constructed hybrid PKSs to produce industrially important short-chain ketones. Our in vitro and in vivo analysis demonstrated production of predicted ketones without significant loss of activities of the hybrid enzymes. These results greatly enhance the mechanistic understanding of PKS modules and prove the benefit of using engineered PKSs as a synthetic biology tool for chemical production.

Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.

BackgroundEconomical conversion of lignocellulosic biomass into biofuels and bioproducts is central to the establishment of a robust bioeconomy. This requires a conversion host that is able to both efficiently assimilate the major lignocellulose-derived carbon sources and divert their metabolites toward specific bioproducts.ResultsIn this study, the carotenogenic yeast Rhodosporidium toruloides was examined for its ability to convert lignocellulose into two non-native sesquiterpenes with biofuel (bisabolene) and pharmaceutical (amorphadiene) applications. We found that R. toruloides can efficiently convert a mixture of glucose and xylose from hydrolyzed lignocellulose into these bioproducts, and unlike many conventional production hosts, its growth and productivity were enhanced in lignocellulosic hydrolysates relative to purified substrates. This organism was demonstrated to have superior growth in corn stover hydrolysates prepared by two different pretreatment methods, one using a novel biocompatible ionic liquid (IL) choline α-ketoglutarate, which produced 261 mg/L of bisabolene at bench scale, and the other using an alkaline pretreatment, which produced 680 mg/L of bisabolene in a high-gravity fed-batch bioreactor. Interestingly, R. toruloides was also observed to assimilate p-coumaric acid liberated from acylated grass lignin in the IL hydrolysate, a finding we verified with purified substrates. R. toruloides was also able to consume several additional compounds with aromatic motifs similar to lignin monomers, suggesting that this organism may have the metabolic potential to convert depolymerized lignin streams alongside lignocellulosic sugars.ConclusionsThis study highlights the natural compatibility of R. toruloides with bioprocess conditions relevant to lignocellulosic biorefineries and demonstrates its ability to produce non-native terpenes.Electronic supplementary materialThe online version of this article (doi:10.1186/s13068-017-0927-5) contains supplementary material, which is available to authorized users.