Machine learning framework for assessment of microbial factory performance

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)

Determining the unsaturated hydraulic conductivity of a compacted sand-bentonite mixture under constant volume and free-swell conditions

Highly compacted sand-bentonite mixtures are often considered as possible engineered barriers in deep high-level radioactive waste disposals. In-situ, the saturation of these barriers from their initially unsaturated state is a complex hydro-mechanical coupled process in which temperature effects also play a role. The key parameter of this process is the unsaturated hydraulic conductivity of the barrier. In this paper, isothermal infiltration experiments were conducted to determine the unsaturated hydraulic conductivity according to the instantaneous profile method. To do so, total suction changes were monitored at different locations along the soil specimen by using resistivity relative humidity probes. Three constant volume infiltration tests were conducted showing, unexpectedly, a decrease of the hydraulic conductivity during infiltration. One test performed under free-swell conditions showed the opposite and standard trend. These observations were interpreted in terms of microstructure changes during wetting, both under constant volume and free swell conditions

Antecedent use of fluoroquinolones is associated with resistance to moxifloxacin in Clostridium difficile

ObjectiveMoxifloxacin is characterized by high activity against Gram-positive cocci and some Gram-positive and -negative anaerobes, including Clostridium difficile. This study investigates the role of prior quinolone use in relation to patterns of susceptibility of C. difficile to moxifloxacin.MethodsSixty-three clinical isolates of C. difficile were investigated for toxigenicity, susceptibility to moxifloxacin, and mutations in the DNA gyrase gene. The medical histories for 50 of these patients were available and used to identify previous fluoroquinolone use.ResultsThirty-three (52.4%) strains showed resistance to moxifloxacin (MICs ≥ 16 mg/L). All moxifloxacin-resistant strains harbored a mutation at amino acid codon Ser-83 of gyrA. Forty-five isolates (71.4%) were toxigenic; all moxifloxacin-resistant strains were in this group. Resistance to moxifloxacin was associated with prior use of fluoroquinolones (P-value 0.009, chi-square).ConclusionsAlthough the use of moxifloxacin to treat C. difficile-associated diarrhea is not likely to be common, these data show a relationship between antecedent fluoroquinolone use and resistance to moxifloxacin in C. difficile isolates, and raise questions regarding selection pressure for resistance placed on colonizing bacteria exposed to fluoroquinolones. Mutations in gyrA are involved in moxifloxacin resistance

Controlling suction by vapour equilibrium technique at different temperatures, application to the determination of the water retention properties of MX80 clay

Problems related to unsaturated soils are frequently encountered in geotechnical or environmental engineering works. In most cases, for the purpose of simplicity, the problems are studied by considering the suction effects on volume change or shear strength under isothermal conditions. Under isothermal condition, very often, a temperature independent water retention curve is considered in the analysis, which is obviously a simplification. When the temperature changes are too significant to be neglected, it is necessary to account for the thermal effects. In this paper, a method for controlling suction using the vapour equilibrium technique at different temperatures is presented. First, calibration of various saturated saline solutions was carried out from temperature of 20 degrees C to 60 degrees C. A mirror psychrometer was used for the measurement of relative humidity generated by saturated saline solutions at different temperatures. The results obtained are in good agreement with the data from the literature. This information was then used to determine the water retention properties of MX80 clay, which showed that the retention curve is shifting down with increasing of temperature

BayFlux: A Bayesian Method to Quantify Metabolic Fluxes and their Uncertainty at the Genome Scale.

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13C Metabolic Flux Analysis (13C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13C 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 13C 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 13C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P-13C MOMA and P-13C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty

A concerted systems biology analysis of phenol metabolism in Rhodococcus opacus PD630

Rhodococcus opacus PD630 metabolizes aromatic substrates and naturally produces branched-chain lipids, which are advantageous traits for lignin valorization. To provide insights into its lignocellulose hydrolysate utilization, we performed 13C-pathway tracing, 13C-pulse-tracing, transcriptional profiling, biomass composition analysis, and metabolite profiling in conjunction with 13C-metabolic flux analysis (13C-MFA) of phenol metabolism. We found that 1) phenol is metabolized mainly through the ortho–cleavage pathway; 2) phenol utilization requires a highly active TCA cycle; 3) NADPH is generated mainly via NADPH-dependent isocitrate dehydrogenase; 4) active cataplerotic fluxes increase plasticity in the TCA cycle; and 5) gluconeogenesis occurs partially through the reversed Entner–Doudoroff pathway (EDP). We also found that phenol-fed R. opacus PD630 generally has lower sugar phosphate concentrations (e.g., fructose 1,6-bisphosphatase) compared to metabolite pools in 13C-glucose-fed Escherichia coli (set as internal standards), while its TCA metabolites (e.g., malate, succinate, and α-ketoglutarate) accumulate intracellularly with measurable succinate secretion. In addition, we found that phenol utilization was inhibited by benzoate, while catabolite repressions by other tested carbon substrates (e.g., glucose and acetate) were absent in R. opacus PD630. Three adaptively-evolved strains display very different growth rates when fed with phenol as a sole carbon source, but they maintain a conserved flux network. These findings improve our understanding of R. opacus’ metabolism for future lignin valorization

A High-Quality Genome-Scale Model for Rhodococcus opacus Metabolism

Rhodococcus opacus is a bacterium that has a high tolerance to aromatic compounds and can produce significant amounts of triacylglycerol (TAG). Here, we present iGR1773, the first genome-scale model (GSM) of R. opacus PD630 metabolism based on its genomic sequence and associated data. The model includes 1773 genes, 3025 reactions, and 1956 metabolites, was developed in a reproducible manner using CarveMe, and was evaluated through Metabolic Model tests (MEMOTE). We combine the model with two Constraint-Based Reconstruction and Analysis (COBRA) methods that use transcriptomics data to predict growth rates and fluxes: E-Flux2 and SPOT (Simplified Pearson Correlation with Transcriptomic data). Growth rates are best predicted by E-Flux2. Flux profiles are more accurately predicted by E-Flux2 than flux balance analysis (FBA) and parsimonious FBA (pFBA), when compared to 44 central carbon fluxes measured by 13C-Metabolic Flux Analysis (13C-MFA). Under glucose-fed conditions, E-Flux2 presents an R2 value of 0.54, while predictions based on pFBA had an inferior R2 of 0.28. We attribute this improved performance to the extra activity information provided by the transcriptomics data. For phenol-fed metabolism, in which the substrate first enters the TCA cycle, E-Flux2’s flux predictions display a high R2 of 0.96 while pFBA showed an R2 of 0.93. We also show that glucose metabolism and phenol metabolism function with similar relative ATP maintenance costs. These findings demonstrate that iGR1773 can help the metabolic engineering community predict aromatic substrate utilization patterns and perform computational strain design

Induced four fold anisotropy and bias in compensated NiFe/FeMn double layers

A vector spin model is used to show how frustrations within a multisublattice antiferromagnet such as FeMn can lead to four-fold magnetic anisotropies acting on an exchange coupled ferromagnetic film. Possibilities for the existence of exchange bias are examined and shown to exist for the case of weak chemical disorder at the interface in an otherwise perfect structure. A sensitive dependence on interlayer exchange is found for anisotropies acting on the ferromagnet through the exchange coupling, and we show that a wide range of anisotropies can appear even for a perfect crystalline structure with an ideally flat interface.Comment: 7 pages, 7 figure

Mass measurements of neutron-deficient Y, Zr, and Nb isotopes and their impact on rp and νp nucleosynthesis processes

© 2018 The Authors. Published by Elsevier B.V. This manuscript is made available under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence (CC BY-NC-ND 4.0). For further details please see: https://creativecommons.org/licenses/by-nc-nd/4.0/Using isochronous mass spectrometry at the experimental storage ring CSRe in Lanzhou, the masses of 82Zr and 84Nb were measured for the first time with an uncertainty of ∼10 keV, and the masses of 79Y, 81Zr, and 83Nb were re-determined with a higher precision. The latter are significantly less bound than their literature values. Our new and accurate masses remove the irregularities of the mass surface in this region of the nuclear chart. Our results do not support the predicted island of pronounced low α separation energies for neutron-deficient Mo and Tc isotopes, making the formation of Zr–Nb cycle in the rp-process unlikely. The new proton separation energy of 83Nb was determined to be 490(400) keV smaller than that in the Atomic Mass Evaluation 2012. This partly removes the overproduction of the p-nucleus 84Sr relative to the neutron-deficient molybdenum isotopes in the previous νp-process simulations.Peer reviewe