Design and synthesis of synthetic promoters for consistency of gene expression across growth phases and scale in S. cerevisiae.

Metabolic engineering and synthetic biology endeavors benefit from promoters that perform consistently (or robustly) with respect to cellular growth phase (exponential and stationary) and fermentation scale (microtiter plates, tubes, flasks, and bioreactors). However, nearly all endogenous promoters (especially in Saccharomyces cerevisiae) do not perform in this manner. In this work, a hybrid promoter engineering strategy is leveraged to create novel synthetic promoters with robustness across these conditions. Using a multi-dimensional RNA-seq dataset, promoters with specific phase dependencies were identified. Fragments enriched with functional transcription factors were identified using MEME suite. These motif-containing fragments could impart activity dependence in the opposing condition. Specifically, we obtain two new promoters with high and consistent expression across both phases by increasing the exponential phase activity of the starting stationary-phase scaffold by 38 and 23-fold respectively. Further, we show that these promoters function consistently across various laboratory growth scales over time in a microtiter plate and in flasks. Overall, this work presents and validates a new strategy for engineering promoters in S. cerevisiae with high levels of expression that are robust to cellular growth phase and the scale of the culture.

Modular, Synthetic Boolean Logic Gates Enabled in Saccharomyces cerevisiae through T7 Polymerases/CRISPR dCas9 Designs.

Synthetic control of gene expression, whether simply promoter selection or higher-order Boolean-style logic, is an important tool for metabolic engineering and synthetic biology. This work develops a suite of orthogonal T7 RNA polymerase systems capable of exerting AND/OR switchlike control over transcription in the yeastSaccharomyces cerevisiae. When linked with CRISPR dCas9-based regulation systems, more complex circuitry is possible including AND/OR/NAND/NOR style control in response to combinations of extracellular copper and galactose. Additionally, we demonstrate that these T7 system designs are modular and can accommodate alternative stimuli sensing as demonstrated through blue light induction. These designs should greatly reduce the time and labor necessary for developing Boolean gene circuits in yeast with novel applications including metabolic pathway control in the future.

Design and synthesis of synthetic UP elements for modulation of gene expression in Escherichia coli.

Metabolic engineering requires fine-tuned gene expression for most pathway optimization applications. To develop a suitable suite of promoters, traditional bacterial promoter engineering efforts have focused on modifications to the core region, especially the -10 and -35 regions, of native promoters. Here, we demonstrate an alternate, unexplored route of promoter engineering through randomization of the UP element of the promoter-a region that contacts the alpha subunit carboxy-terminal domain instead of the sigma subunit of the RNA polymerase holoenzyme. Through this work, we identify five novel UP element sequences through library-based searches in Escherichia coli. The resulting elements were used to activate the E. coli core promoter, rrnD promoter, to levels on par and higher than the prevalent strong bacterial promoter, OXB15. These relative levels of expression activation were transferrable when applied upstream of alternate core promoter sequences, including rrnA and rrnH. This work thus presents and validates a novel strategy for bacterial promoter engineering with transferability across varying core promoters and potential for transferability across bacterial species.

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data-Driven Metabolic Engineering.

The recent increase in high-throughput capacity of 'omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data-driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of 'omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data-driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system-scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.