Rational flux design in metabolic engineering approaches remains difficult since important pathway information is frequently not available. Therefore empirical methods are applied that randomly change absolute and relative pathway enzyme levels and subsequently screen for variants with improved performance. However, screening is often limited on the analytical side, generating a strong incentive to construct small but smart libraries. Here we introduce RedLibs (Reduced Libraries), an algorithm that allows for the rational design of smart combinatorial libraries for pathway optimization thereby minimizing the use of experimental resources. We demonstrate the utility of RedLibs for the design of ribosome-binding site libraries by in silico and in vivo screening with fluorescent proteins and perform a simple two-step optimization of the product selectivity in the branched multistep pathway for violacein biosynthesis, indicating a general applicability for the algorithm and the proposed heuristics. We expect that RedLibs will substantially simplify the refactoring of synthetic metabolic pathways.

Elimination of metabolic flux imbalances in microbial cell factories is an important part in the establishment of viable biotechnological production processes. However, due to the high complexity of cellular metabolism, the limited a priori knowledge about the majority of production pathways and a lack of forward design standards, metabolic engineers strongly rely on empirical screening methodologies to achieve the required improvement of cell behavior. Combinatorial pathway engineering provides an interesting tool to identify global solutions for intricate pathways, but methods for the reduction of combinatorial library size are inevitably required to restrict the experimental effort to an affordable size. Here we review recent advances from this field by scrutinizing commonly applied diversification methods and highlighting crucial strategies for the minimization of experimental effort.

Engineered enzyme cascades offer powerful tools to convert renewable resources into valueadded products. Man-made catalysts give access to new-to-nature reactivities that may complement the enzyme's repertoire. Their mutual incompatibility, however, challenges their integration into concurrent chemo-enzymatic cascades. Herein we show that compartmentalization of complex enzyme cascades within E. coli whole cells enables the simultaneous use of a metathesis catalyst, thus allowing the sustainable one-pot production of cycloalkenes from oleic acid. Cycloheptene is produced from oleic acid via a concurrent enzymatic oxidative decarboxylation and ring-closing metathesis. Cyclohexene and cyclopentene are produced from oleic acid via either a six-or eight-step enzyme cascade involving hydration, oxidation, hydrolysis and decarboxylation, followed by ring-closing metathesis. Integration of an upstream hydrolase enables the usage of olive oil as the substrate for the production of cycloalkenes. This work highlights the potential of integrating organometallic catalysis with whole-cell enzyme cascades of high complexity to enable sustainable chemistry.

Cell-free biosynthesis in the form of in vitro multi-enzyme reaction networks or enzyme cascade reactions emerges as a promising tool to carry out complex catalysis in one-step, one-vessel settings. It combines the advantages of well-established in vitro biocatalysis with the power of multi-step in vivo pathways. Such cascades have been successfully applied to the synthesis of fine and bulk chemicals, monomers and complex polymers of chemical importance, and energy molecules from renewable resources as well as electricity. The scale of these initial attempts remains small, suggesting that more robust control of such systems and more efficient optimization are currently major bottlenecks. To this end, the very nature of enzyme cascade reactions as multi-membered systems requires novel approaches for implementation and optimization, some of which can be obtained from in vivo disciplines (such as pathway refactoring and DNA assembly), and some of which can be built on the unique, cell-free properties of cascade reactions (such as easy analytical access to all system intermediates to facilitate modeling).

Supplementary Figure S1. Development and validation of a medium throughput HPLC method for the detection of L-serine in microbial culture supernatant. (a) Overlaid HPLC chromatogram of L-serine standards. (b) The recovery of L-serine from spiked samples (n=5). (c) Reproducability of the method (n=5) for different L-serine concentrations. (d) Calibration curve for L-serine.

Multiplexed gene expression optimization via modulation of gene translation efficiency through ribosome binding site (RBS) engineering is a valuable approach for optimizing artificial properties in bacteria, ranging from genetic circuits to production pathways. Established algorithms design smart RBS-libraries based on a single partially-degenerate sequence that efficiently samples the entire space of translation initiation rates. However, the sequence space that is accessible when integrating the library by CRISPR/Cas9-based genome editing is severely restricted by DNA mismatch repair (MMR) systems. MMR efficiency depends on the type and length of the mismatch and thus effectively removes potential library members from the pool. Rather than working in MMR-deficient strains, which accumulate off-target mutations, or depending on temporary MMR inactivation, which requires additional steps, we eliminate this limitation by developing a pre-selection rule of genome-library-optimized-sequences (GLOS) that enables introducing large functional diversity into MMR-proficient strains with sequences that are no longer subject to MMR-processing. We implement several GLOS-libraries in Escherichia coli and show that GLOS-libraries indeed retain diversity during genome editing and that such libraries can be used in complex genome editing operations such as concomitant deletions. We argue that this approach allows for stable and efficient fine tuning of chromosomal functions with minimal effort.

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Controlling the expression levels of multiple recombinant proteins for optimal performance is crucial for synthetic biosystems but remains difficult given the large number of DNA-encoded factors that influence the process of gene expression from transcription to translation. In bacterial hosts, biosystems can be economically encoded as operons, but the sequence requirements for exact tuning of expression levels in an operon remain unclear. Here, we demonstrate the extent and predictability of protein-level variation using diverse arrangements of twelve genes to generate 88 synthetic operons with up to seven genes at varying inducer concentrations. The resulting 2772 protein expression measurements allowed the training of a sequence-based machine learning model that explains 83% of the variation in the data with a mean absolute error of 9% relative to reference constructs, making it a useful tool for protein expression prediction. Feature importance analysis indicates that operon length, gene position and gene junction structure are of major importance for protein expression.