Non-canonical D-xylose and L-arabinose metabolism via D-arabitol in the oleaginous yeast Rhodosporidium toruloides.

R. toruloides is an oleaginous yeast, with diverse metabolic capacities and high tolerance for inhibitory compounds abundant in plant biomass hydrolysates. While R.

Engineering transcriptional regulation of pentose metabolism in Rhodosporidium toruloides for improved conversion of xylose to bioproducts.

Efficient conversion of pentose sugars remains a significant barrier to the replacement of petroleum-derived chemicals with plant biomass-derived bioproducts. While the oleaginous yeast Rhodosporidium toruloides (also known as Rhodotorula toruloides) has a relatively robust native metabolism of pentose sugars compared to other wild yeasts, faster assimilation of those sugars will be required for industrial utilization of pentoses. To increase the rate of pentose assimilation in R.

Active and machine learning-based approaches to rapidly enhance microbial chemical production.

In order to make renewable fuels and chemicals from microbes, new methods are required to engineer microbes more intelligently. Computational approaches, to engineer strains for enhanced chemical production typically rely on detailed mechanistic models (e.g.

H and C metabolic flux analysis elucidates in vivo thermodynamics of the ED pathway in Zymomonas mobilis.

Zymomonas mobilis is an industrially relevant bacterium notable for its ability to rapidly ferment simple sugars to ethanol using the Entner-Doudoroff (ED) glycolytic pathway, an alternative to the well-known Embden-Meyerhof-Parnas (EMP) pathway used by most organisms. Recent computational studies have predicted that the ED pathway is substantially more thermodynamically favorable than the EMP pathway, a potential factor explaining the high glycolytic rate in Z. mobilis.

Model-enabled gene search (MEGS) allows fast and direct discovery of enzymatic and transport gene functions in the marine bacterium .

Whereas genomes can be rapidly sequenced, the functions of many genes are incompletely or erroneously annotated because of a lack of experimental evidence or prior functional knowledge in sequence databases. To address this weakness, we describe here a odel-nabled ene earch (MEGS) approach that (i) identifies metabolic functions either missing from an organism's genome annotation or incorrectly assigned to an ORF by using discrepancies between metabolic model predictions and experimental culturing data; (ii) designs functional selection experiments for these specific metabolic functions; and (iii) selects a candidate gene(s) responsible for these functions from a genomic library and directly interrogates this gene's function experimentally. To discover gene functions, MEGS uses genomic functional selections instead of relying on correlations across large experimental datasets or sequence similarity as do other approaches.