Optimizing the strain engineering process for industrial-scale production of bio-based molecules.

Biomanufacturing could contribute as much as ${\$}$30 trillion to the global economy by 2030. However, the success of the growing bioeconomy depends on our ability to manufacture high-performing strains in a time- and cost-effective manner. The Design-Build-Test-Learn (DBTL) framework has proven to be an effective strain engineering approach. Significant improvements have been made in genome engineering, genotyping, and phenotyping throughput over the last couple of decades that have greatly accelerated the DBTL cycles. However, to achieve a radical reduction in strain development time and cost, we need to look at the strain engineering process through a lens of optimizing the whole cycle, as opposed to simply increasing throughput at each stage. We propose an approach that integrates all 4 stages of the DBTL cycle and takes advantage of the advances in computational design, high-throughput genome engineering, and phenotyping methods, as well as machine learning tools for making predictions about strain scale-up performance. In this perspective, we discuss the challenges of industrial strain engineering, outline the best approaches to overcoming these challenges, and showcase examples of successful strain engineering projects for production of heterologous proteins, amino acids, and small molecules, as well as improving tolerance, fitness, and de-risking the scale-up of industrial strains.

Population Bottlenecks Strongly Affect the Evolutionary Dynamics of Antibiotic Persistence.

Bacterial persistence is a potential cause of antibiotic therapy failure. Antibiotic-tolerant persisters originate from phenotypic differentiation within a susceptible population, occurring with a frequency that can be altered by mutations. Recent studies have proven that persistence is a highly evolvable trait and, consequently, an important evolutionary strategy of bacterial populations to adapt to high-dose antibiotic therapy. Yet, the factors that govern the evolutionary dynamics of persistence are currently poorly understood. Theoretical studies predict far-reaching effects of bottlenecking on the evolutionary adaption of bacterial populations, but these effects have never been investigated in the context of persistence. Bottlenecking events are frequently encountered by infecting pathogens during host-to-host transmission and antibiotic treatment. In this study, we used a combination of experimental evolution and barcoded knockout libraries to examine how population bottlenecking affects the evolutionary dynamics of persistence. In accordance with existing hypotheses, small bottlenecks were found to restrict the adaptive potential of populations and result in more heterogeneous evolutionary outcomes. Evolutionary trajectories followed in small-bottlenecking regimes additionally suggest that the fitness landscape associated with persistence has a rugged topography, with distinct trajectories toward increased persistence that are accessible to evolving populations. Furthermore, sequencing data of evolved populations and knockout libraries after selection reveal various genes that are potentially involved in persistence, including previously known as well as novel targets. Together, our results do not only provide experimental evidence for evolutionary theories, but also contribute to a better understanding of the environmental and genetic factors that guide bacterial adaptation to antibiotic treatment.

A NON-LINEAR STRUCTURE-PROPERTY MODEL FOR OCTANOL-WATER PARTITION COEFFICIENT.

Octanol-water partition coefficient (K(ow)) is an important thermodynamic property used to characterize the partitioning of solutes between an aqueous and organic phase and has importance in such areas as pharmacology, pharmacokinetics, pharmacodynamics, chemical production and environmental toxicology. We present a non-linear quantitative structure-property relationship model for determining K(ow) values of new molecules in silico. A total of 823 descriptors were generated for 11,308 molecules whose K(ow) values are reported in the PhysProp dataset by Syracuse Research. Optimum network architecture and its associated inputs were identified using a wrapper-based feature selection algorithm that combines differential evolution and artificial neural networks. A network architecture of 50-33-35-1 resulted in the least root-mean squared error (RMSE) in the training set. Further, to improve on single-network predictions, a neural network ensemble was developed by combining five networks that have the same architecture and inputs but differ in layer weights. The ensemble predicted the K(ow) values with RMSE of 0.28 and 0.38 for the training set and internal validation set, respectively. The ensemble performed reasonably well on an external dataset when compared with other popular K(ow) models in the literature.

Design of improved permeation enhancers for transdermal drug delivery.

One promising way to breach the skin's natural barrier to drugs is by the application of chemicals called penetration enhancers. However, identifying potential enhancers is difficult and time consuming. We have developed a virtual screening algorithm for generating potential chemical penetration enhancers (CPEs) by integrating nonlinear, theory-based quantitative structure-property relationship models, genetic algorithms, and neural networks. Our newly developed algorithm was used to identify seven potential CPE molecular structures. These chemical enhancers were tested for their toxicity on (a) mouse embryonic fibroblasts (MEFs) with MTT assay, and (b) porcine abdominal skin by histology using H/E staining at the end of a 48-h exposure period to the chemicals. Further, melatonin permeability in the presence of the enhancers was tested using porcine skin and Franz diffusion cells. Careful toxicity tests showed that four of the seven "general" CPEs were nontoxic candidate enhancers (menthone, 1-(1-adamantyl)-2-pyrrolidinone, R(+)-3-amino-1-hydroxy-2-pyrrolidinone, and 1-(4-nitro-phenyl)-pyrrolidine-2,5-dione). Further testing of these four molecules as potential melatonin-specific CPEs revealed that only menthone and 1-dodecyl-2-pyrrolidinone provided sufficient enhancement of the melatonin permeation. The results from our permeability and toxicity measurements provide validation of the efficacy and ability of our virtual screening algorithm for generating potential chemical enhancer structures by virtual screening algorithms, in addition to providing additional experimental data to the body of knowledge.

Screening of chemical penetration enhancers for transdermal drug delivery using electrical resistance of skin.

PURPOSE: A novel technique is presented for identifying potential chemical penetration enhancers (CPEs) based on changes in the electrical resistance of skin. METHODS: Specifically, a multi-well resistance chamber was designed and constructed to facilitate more rapid determination of the effect of CPEs on skin resistance. The experimental setup was validated using nicotine and decanol on porcine skin in vitro. The multi-well resistance chambers were capable of operating at 37 degrees C in order to simulate the physiological temperature of the human body. Further, the utility of the multi-well resistance chamber technique was validated using standard Franz diffusion cells. Electrical resistance measurements were used to evaluate the potency of seven new potential CPEs, identified using virtual screening algorithms. From the resistance measurements, the chemicals 1-dodecyl-2-pyrrolidinone (P), menthone (M) and R(+)-3-amino-1-hydroxy-2-pyrrolidinone (C) were identified as the better penetration enhancers among the seven tested. Further, traditional permeation experiments were performed in Franz diffusion cells to confirm our findings. RESULTS: The permeation test results indicated that, of the three CPEs deemed potentially viable using the newly-developed resistance screening technique, both P and M increased the permeation of the test drug (melatonin) through skin in 48 h. CONCLUSION: In summary, this resistance technique can be used to effectively pre-evaluate potential CPEs, thereby reducing the time required to conduct the permeability studies.