Alleviation of carbon catabolite repression in Enterobacter aerogenes for efficient utilization of sugarcane molasses for 2,3-butanediol production

Table S3. Comparison of fed-batch fermentation with EMY-01, EMY-68, EMY-70S, and EMY-70SP using sugarcane molasses

Production of 2,3-butanediol in Saccharomyces cerevisiae by in silico aided metabolic engineering

BACKGROUND: 2,3-Butanediol is a chemical compound of increasing interest due to its wide applications. It can be synthesized via mixed acid fermentation of pathogenic bacteria such as Enterobacter aerogenes and Klebsiella oxytoca. The non-pathogenic Saccharomyces cerevisiae possesses three different 2,3-butanediol biosynthetic pathways, but produces minute amount of 2,3-butanediol. Hence, we attempted to engineer S. cerevisiae strain to enhance 2,3-butanediol production. RESULTS: We first identified gene deletion strategy by performing in silico genome-scale metabolic analysis. Based on the best in silico strategy, in which disruption of alcohol dehydrogenase (ADH) pathway is required, we then constructed gene deletion mutant strains and performed batch cultivation of the strains. Deletion of three ADH genes, ADH1, ADH3 and ADH5, increased 2,3-butanediol production by 55-fold under microaerobic condition. However, overproduction of glycerol was observed in this triple deletion strain. Additional rational design to reduce glycerol production by GPD2 deletion altered the carbon fluxes back to ethanol and significantly reduced 2,3-butanediol production. Deletion of ALD6 reduced acetate production in strains lacking major ADH isozymes, but it did not favor 2,3-butanediol production. Finally, we introduced 2,3-butanediol biosynthetic pathway from Bacillus subtilis and E. aerogenes to the engineered strain and successfully increased titer and yield. Highest 2,3-butanediol titer (2.29 g·l(-1)) and yield (0.113 g·g(-1)) were achieved by Δadh1 Δadh3 Δadh5 strain under anaerobic condition. CONCLUSIONS: With the aid of in silico metabolic engineering, we have successfully designed and constructed S. cerevisiae strains with improved 2,3-butanediol production

Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts

We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. In order to demonstrate the performance, we also evaluate a few seconds of gravitational-wave data segment using the trained networks and obtain the false alarm probability. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short gamma-ray bursts.Comment: 30 pages, 10 figure