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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). [ABSTRACT FROM AUTHOR]

Flowers of the hop plant provide both bitterness and "hoppy" flavor to beer. Hops are, however, both a water and energy intensive crop and vary considerably in essential oil content, making it challenging to achieve a consistent hoppy taste in beer. Here, we report that brewer's yeast can be engineered to biosynthesize aromatic monoterpene molecules that impart hoppy flavor to beer by incorporating recombinant DNA derived from yeast, mint, and basil. Whereas metabolic engineering of biosynthetic pathways is commonly enlisted to maximize product titers, tuning expression of pathway enzymes to affect target production levels of multiple commercially important metabolites without major collateral metabolic changes represents a unique challenge. By applying state-of-the-art engineering techniques and a framework to guide iterative improvement, strains are generated with target performance characteristics. Beers produced using these strains are perceived as hoppier than traditionally hopped beers by a sensory panel in a double-blind tasting. [ABSTRACT FROM AUTHOR]

Background: Glycolysis breakdowns glucose into essential building blocks and ATP/NAD(P)H for the cell, occupying a central role in its growth and bio-production. Among glycolytic pathways, the Entner Doudoroff pathway (EDP) is a more thermodynamically favorable pathway with fewer enzymatic steps than either the Embden-Meyerhof-Parnas pathway (EMPP) or the oxidative pentose phosphate pathway (OPPP). However, Escherichia coli do not use their native EDP for glucose metabolism. Results: Overexpression of edd and eda in E. coli to enhance EDP activity resulted in only a small shift in the flux directed through the EDP (~20 % of glycolysis flux). Disrupting the EMPP by phosphofructokinase I (pfkA) knockout increased flux through OPPP (~60 % of glycolysis flux) and the native EDP (~14 % of glycolysis flux), while overexpressing edd and eda in this ΔpfkA mutant directed ~70 % of glycolytic flux through the EDP. The downregulation of EMPP via the pfkA deletion significantly decreased the growth rate, while EDP overexpression in the ΔpfkA mutant failed to improve its growth rates due to metabolic burden. However, the reorganization of E. coli glycolytic strategies did reduce glucose catabolite repression. The ΔpfkA mutant in glucose medium was able to cometabolize acetate via the citric acid cycle and gluconeogenesis, while EDP overexpression in the ΔpfkA mutant repressed acetate flux toward gluconeogenesis. Moreover, 13C-pulse experiments in the ΔpfkA mutants showed unsequential labeling dynamics in glycolysis intermediates, possibly suggesting metabolite channeling (metabolites in glycolysis are pass from enzyme to enzyme without fully equilibrating within the cytosol medium). Conclusions: We engineered E. coli to redistribute its native glycolytic flux. The replacement of EMPP by EDP did not improve E. coli glucose utilization or biomass growth, but alleviated catabolite repression. More importantly, our results supported the hypothesis of channeling in the glycolytic pathways, a potentially overlooked mechanism for regulating glucose catabolism and coutilization of other substrates. The presence of channeling in native pathways, if proven true, would affect synthetic biology applications and metabolic modeling. [ABSTRACT FROM AUTHOR]

Here we report the first metatranscriptomic analysis of gene expression and regulation of ‘ Candidatus Accumulibacter’-enriched lab-scale sludge during enhanced biological phosphorus removal (EBPR). Medium density oligonucleotide microarrays were generated with probes targeting most predicted genes hypothesized to be important for the EBPR phenotype. RNA samples were collected at the early stage of anaerobic and aerobic phases (15 min after acetate addition and switching to aeration respectively). We detected the expression of a number of genes involved in the carbon and phosphate metabolisms, as proposed by EBPR models (e.g. polyhydroxyalkanoate synthesis, a split TCA cycle through methylmalonyl-CoA pathway, and polyphosphate formation), as well as novel genes discovered through metagenomic analysis. The comparison between the early stage anaerobic and aerobic gene expression profiles showed that expression levels of most genes were not significantly different between the two stages. The majority of upregulated genes in the aerobic sample are predicted to encode functions such as transcription, translation and protein translocation, reflecting the rapid growth phase of Accumulibacter shortly after being switched to aerobic conditions. Components of the TCA cycle and machinery involved in ATP synthesis were also upregulated during the early aerobic phase. These findings support the predictions of EBPR metabolic models that the oxidation of intracellularly stored carbon polymers through the TCA cycle provides ATP for cell growth when oxygen becomes available. Nitrous oxide reductase was among the very few Accumulibacter genes upregulated in the anaerobic sample, suggesting that its expression is likely induced by the deprivation of oxygen. [ABSTRACT FROM AUTHOR]

An environmentally important bacterium with versatile respiration, Shewanella oneidensis MR-1, displayed significantly different growth rates under three culture conditions: minimal medium (doubling time ∼3 h), salt stressed minimal medium (doubling time ∼6 h), and minimal medium with amino acid supplementation (doubling time ∼1.5 h). 13C-based metabolic flux analysis indicated that fluxes of central metabolic reactions remained relatively constant under the three growth conditions, which is in stark contrast to the reported significant changes in the transcript and metabolite profiles under various growth conditions. Furthermore, 10 transposon mutants of S. oneidensis MR-1 were randomly chosen from a transposon library and their flux distributions through central metabolic pathways were revealed to be identical, even though such mutational processes altered the secondary metabolism, for example, glycine and C1 (5,10-Me-THF) metabolism. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009 [ABSTRACT FROM AUTHOR]

From the standpoints of both basic research and biotechnology, there is considerable interest in reaching a clearer understanding of the diversity of biological mechanisms employed during lignocellulose degradation. Globally, termites are an extremely successful group of wood-degrading organisms and are therefore important both for their roles in carbon turnover in the environment and as potential sources of biochemical catalysts for efforts aimed at converting wood into biofuels. Only recently have data supported any direct role for the symbiotic bacteria in the gut of the termite in cellulose and xylan hydrolysis. Here we use a metagenomic analysis of the bacterial community resident in the hindgut paunch of a wood-feeding ‘higher’ Nasutitermes species (which do not contain cellulose-fermenting protozoa) to show the presence of a large, diverse set of bacterial genes for cellulose and xylan hydrolysis. Many of these genes were expressed in vivo or had cellulase activity in vitro, and further analyses implicate spirochete and fibrobacter species in gut lignocellulose degradation. New insights into other important symbiotic functions including H2 metabolism, CO2-reductive acetogenesis and N2 fixation are also provided by this first system-wide gene analysis of a microbial community specialized towards plant lignocellulose degradation. Our results underscore how complex even a 1-μl environment can be. [ABSTRACT FROM AUTHOR]

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. In metabolic engineering, mechanistic models require prior metabolism knowledge of the chassis strain, whereas machine learning models need ample training data. Here, the authors combine the mechanistic and machine learning models to improve prediction performance of tryptophan metabolism in baker's yeast. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]