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Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, fatty acids, and tryptophan. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing. Synthetic Biology often lacks the predictive power needed for efficient bioengineering. Here the authors present ART, a machine learning and probabilistic predictive tool to guide synthetic biology design in a systematic fashion.

Concerns over climate change have necessitated a rethinking of our transportation infrastructure. One possible alternative to carbon-polluting fossil fuels is biofuels produced by engineered microorganisms that use a renewable carbon source. Two biofuels, ethanol and biodiesel, have made inroads in displacing petroleum-based fuels, but their uptake has been limited by the amounts that can be used in conventional engines and by their cost. Advanced biofuels that mimic petroleum-based fuels are not limited by the amounts that can be used in existing transportation infrastructure but have had limited uptake due to costs. In this Review, we discuss engineering metabolic pathways to produce advanced biofuels, challenges with substrate and product toxicity with regard to host microorganisms and methods to engineer tolerance, and the use of functional genomics and machine learning approaches to produce advanced biofuels and prospects for reducing their costs.Biofuels produced by conversion of biomass by engineered microorganisms have the potential to replace fossil fuels and reduce carbon emissions. In this Review, Keasling and colleagues discuss engineering of metabolic pathways to produce advanced biofuels and approaches to reduce metabolite toxicity and cost and increase titre, rate and yield.

New synthetic biology capabilities hold the promise of dramatically improving our ability to engineer biological systems. However, a fundamental hurdle in realizing this potential is our inability to accurately predict biological behavior after modifying the corresponding genotype. Kinetic models have traditionally been used to predict pathway dynamics in bioengineered systems, but they take significant time to develop, and rely heavily on domain expertise. Here, we show that the combination of machine learning and abundant multiomics data (proteomics and metabolomics) can be used to effectively predict pathway dynamics in an automated fashion. The new method outperforms a classical kinetic model, and produces qualitative and quantitative predictions that can be used to productively guide bioengineering efforts. This method systematically leverages arbitrary amounts of new data to improve predictions, and does not assume any particular interactions, but rather implicitly chooses the most predictive ones. Using artificial intelligence to predict biological dynamics New synthetic biology capabilities (e.g. CRISPR) dramatically improve our ability to engineer biological systems for the benefit of society (biofuels, medical drugs). However, this effort is hampered because we cannot reliably predict the outcome of our bioengineering efforts. Mathematical kinetic models have been traditionally used to predict pathway dynamics, but they take a long time to develop and require significant biological expertize. Here, we substitute traditional kinetic models with a machine learning approach that is able to learn pathway dynamics straight from data examples. This new approach can be systematically applied to any product, pathway or host and significantly speeds up bioengineering.

Metabolic models can estimate intrinsic product yields for microbial factories, but such frameworks struggle to predict cell performance (including product titer or rate) under suboptimal metabolism and complex bioprocess conditions. On the other hand, machine learning, complementary to metabolic modeling necessitates large amounts of data. Building such a database for metabolic engineering designs requires significant manpower and is prone to human errors and bias. We propose an approach to integrate data-driven methods with genome scale metabolic model for assessment of microbial bio-production (yield, titer and rate). Using engineered E. coli as an example, we manually extracted and curated a data set comprising about 1200 experimentally realized cell factories from ~100 papers. We furthermore augmented the key design features (e.g., genetic modifications and bioprocess variables) extracted from literature with additional features derived from running the genome-scale metabolic model iML1515 simulations with constraints that match the experimental data. Then, data augmentation and ensemble learning (e.g., support vector machines, gradient boosted trees, and neural networks in a stacked regressor model) are employed to alleviate the challenges of sparse, non-standardized, and incomplete data sets, while multiple correspondence analysis/principal component analysis are used to rank influential factors on bio-production. The hybrid framework demonstrates a reasonably high cross-validation accuracy for prediction of E.coli factory performance metrics under presumed bioprocess and pathway conditions (Pearson correlation coefficients between 0.8 and 0.93 on new data not seen by the model).

Sustainably grown biomass is a promising alternative to produce fuels and chemicals and reduce the dependency on fossil energy sources. However, the efficient conversion of lignocellulosic biomass into biofuels and bioproducts often requires extensive testing of components and reaction conditions used in the pretreatment, saccharification, and bioconversion steps. This restriction can result in a significant and unwieldy number of combinations of biomass types, solvents, microbial strains, and operational parameters that need to be characterized, turning these efforts into a daunting and time-consuming task. Here we developed a high-throughput feedstocks-to-fuels screening platform to address these challenges. The result is a miniaturized semi-automated platform that leverages the capabilities of a solid handling robot, a liquid handling robot, analytical instruments, and a centralized data repository, adapted to operate as an ionic-liquid-based biomass conversion pipeline. The pipeline was tested by using sorghum as feedstock, the biocompatible ionic liquid cholinium phosphate as pretreatment solvent, a “one-pot” process configuration that does not require ionic liquid removal after pretreatment, and an engineered strain of the yeast Rhodosporidium toruloides that produces the jet-fuel precursor bisabolene as a conversion microbe. By the simultaneous processing of 48 samples, we show that this configuration and reaction conditions result in sugar yields (~70%) and bisabolene titers (~1500 mg/L) that are comparable to the efficiencies observed at larger scales but require only a fraction of the time. We expect that this Feedstocks-to-Fuels pipeline will become an effective tool to screen thousands of bioenergy crop and feedstock samples and assist process optimization efforts and the development of predictive deconstruction approaches.

Engineering metabolism to efficiently produce chemicals from multi-step pathways requires optimizing multi-gene expression programs to achieve enzyme balance. CRISPR-Cas transcriptional control systems are emerging as important tools for programming multi-gene expression, but poor predictability of guide RNA folding can disrupt expression control. Here, we correlate efficacy of modified guide RNAs (scRNAs) for CRISPR activation (CRISPRa) in E. coli with a computational kinetic parameter describing scRNA folding rate into the active structure ( r S  = 0.8). This parameter also enables forward design of scRNAs, allowing us to design a system of three synthetic CRISPRa promoters that can orthogonally activate (>35-fold) expression of chosen outputs. Through combinatorial activation tuning, we profile a three-dimensional design space expressing two different biosynthetic pathways, demonstrating variable production of pteridine and human milk oligosaccharide products. This RNA design approach aids combinatorial optimization of metabolic pathways and may accelerate routine design of effective multi-gene regulation programs in bacterial hosts. Guide RNA folding affects functionality of CRISPR-Cas transcriptional control systems. Here, the authors report computational gRNA design together with creation of synthetic CRISPRa promoters for orthogonal expression control and demonstrate the application in pteridine and human milk oligosaccharide production in E. coli .

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13 C Metabolic Flux Analysis ( 13 C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13 C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13 C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13 C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in “non-gaussian” situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13 C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13 C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P- 13 C MOMA and P- 13 C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty.

We present a droplet-based microfluidic system that enables CRISPR-based gene editing and high-throughput screening on a chip. The microfluidic device contains a 10 × 10 element array, and each element contains sets of electrodes for two electric field-actuated operations: electrowetting for merging droplets to mix reagents and electroporation for transformation. This device can perform up to 100 genetic modification reactions in parallel, providing a scalable platform for generating the large number of engineered strains required for the combinatorial optimization of genetic pathways and predictable bioengineering. We demonstrate the system’s capabilities through the CRISPR-based engineering of two test cases: (1) disruption of the function of the enzyme galactokinase (galK) in E. coli and (2) targeted engineering of the glutamine synthetase gene (glnA) and the blue-pigment synthetase gene (bpsA) to improve indigoidine production in E. coli.

Background Lipomyces starkeyi is an oleaginous yeast with a native metabolism well-suited for production of lipids and biofuels from complex lignocellulosic and waste feedstocks. Recent advances in genetic engineering tools have facilitated the development of L. starkeyi into a microbial chassis for biofuel and chemical production. However, the feasibility of redirecting L. starkeyi lipid flux away from lipids and towards other products remains relatively unexplored. Here, we engineer the native metabolism to produce malic acid by introducing the reductive TCA pathway and a C4-dicarboxylic acid transporter to the yeast. Results Heterogeneous expression of two genes, the Aspergillus oryzae malate transporter and malate dehydrogenase, enabled L. starkeyi malic acid production. Overexpression of a third gene, the native pyruvate carboxylase, allowed titers to reach approximately 10 g/L during shaking flasks cultivations, with production of malic acid inhibited at pH values less than 4. Corn-stover hydrolysates were found to be well-tolerated, and controlled bioreactor fermentations on the real hydrolysate produced 26.5 g/L of malic acid. Proteomic, transcriptomic and metabolomic data from real and mock hydrolysate fermentations indicated increased levels of a S. cerevisiae hsp9/hsp12 homolog (proteinID: 101453), glutathione dependent formaldehyde dehydrogenases (proteinIDs: 2047, 278215), oxidoreductases, and expression of efflux pumps and permeases during growth on the real hydrolysate. Simultaneously, machine learning based medium optimization improved production dynamics by 18% on mock hydrolysate and revealed lower tolerance to boron (a trace element included in the standard cultivation medium) than other yeasts. Conclusions Together, this work demonstrated the ability to produce organic acids in L. starkeyi with minimal byproducts. The fermentation characterization and omic analyses provide a rich dataset for understanding L. starkeyi physiology and metabolic response to growth in hydrolysates. Identified upregulated genes and proteins provide potential targets for overexpression for improving growth and tolerance to concentrated hydrolysates, as well as valuable information for future L. starkeyi engineering work.

Electroporation-mediated gene delivery is a cornerstone of synthetic biology, offering several advantages over other methods: higher efficiencies, broader applicability, and simpler sample preparation. Yet, electroporation protocols are often challenging to integrate into highly multiplexed workflows, owing to limitations in their scalability and tunability. These challenges ultimately increase the time and cost per transformation. As a result, rapidly screening genetic libraries, exploring combinatorial designs, or optimizing electroporation parameters requires extensive iterations, consuming large quantities of expensive custom-made DNA and cell lines or primary cells. To address these limitations, we have developed a High-Throughput Microfluidic Electroporation (HTME) platform that includes a 384-well electroporation plate (E-Plate) and control electronics capable of rapidly electroporating all wells in under a minute with individual control of each well. Fabricated using scalable and cost-effective printed-circuit-board (PCB) technology, the E-Plate significantly reduces consumable costs and reagent consumption by operating on nano to microliter volumes. Furthermore, individually addressable wells facilitate rapid exploration of large sets of experimental conditions to optimize electroporation for different cell types and plasmid concentrations/types. Use of the standard 384-well footprint makes the platform easily integrable into automated workflows, thereby enabling end-to-end automation. We demonstrate transformation of E. coli with pUC19 to validate the HTME’s core functionality, achieving at least a single colony forming unit in more than 99% of wells and confirming the platform’s ability to rapidly perform hundreds of electroporations with customizable conditions. This work highlights the HTME’s potential to significantly accelerate synthetic biology Design-Build-Test-Learn (DBTL) cycles by mitigating the transformation/transfection bottleneck.