Protein constraints in genome-scale metabolic models: Data integration, parameter estimation, and prediction of metabolic phenotypes.

Genome-scale metabolic models provide a valuable resource to study metabolism and cell physiology. These models are employed with approaches from the constraint-based modeling framework to predict metabolic and physiological phenotypes. The prediction performance of genome-scale metabolic models can be improved by including protein constraints.

PARROT: Prediction of enzyme abundances using protein-constrained metabolic models.

Protein allocation determines the activity of cellular pathways and affects growth across all organisms. Therefore, different experimental and machine learning approaches have been developed to quantify and predict protein abundance and how they are allocated to different cellular functions, respectively. Yet, despite advances in protein quantification, it remains challenging to predict condition-specific allocation of enzymes in metabolic networks.

Accurate prediction of in vivo protein abundances by coupling constraint-based modelling and machine learning.

Quantification of how different environmental cues affect protein allocation can provide important insights for understanding cell physiology. While absolute quantification of proteins can be obtained by resource-intensive mass-spectrometry-based technologies, prediction of protein abundances offers another way to obtain insights into protein allocation. Here we present CAMEL, a framework that couples constraint-based modelling with machine learning to predict protein abundance for any environmental condition.

Papiliotrema laurentii: general features and biotechnological applications.

Papiliotrema laurentii, previously classified as Cryptococcus laurentii, is an oleaginous yeast that has been isolated from soil, plants, and agricultural and industrial residues. This variety of habitats reflects the diversity of carbon sources that it can metabolize, including monosaccharides, oligosaccharides, glycerol, organic acids, and oils. Compared to other oleaginous yeasts, such as Yarrowia lipolytica and Rhodotorula toruloides, there is little information regarding its genetic and physiological characteristics.

Ethanol stress responses in Kluyveromyces marxianus: current knowledge and perspectives.

The rising concern with the emission of greenhouse gases has boosted new incentives for biofuels production, which are less polluting than fossil fuels. Special attention has been given to the second-generation ethanol, as it is produced from abundant feedstocks which do not compete with food production, such as lignocellulosic biomass and whey. Kluyveromyces marxianus stands out in second-generation ethanol production due to its capacity of assimilating lactose, the sugar found in whey, and tolerating high temperatures used in simultaneous saccharification processes.

Insights into oleaginous phenotype of the yeast Papiliotrema laurentii.

Oleaginous yeasts have stood out due to their ability to accumulate oil, which can be used for fatty acid-derived biofuel production. Papiliotrema laurentii UFV-1 is capable of starting the lipid accumulation in the late exponential growth phase and achieves maximum lipid content at 48 h of growth; it is, therefore, interesting to study how its oleaginous phenotype is regulated. Herein, we provide for the first time insights into the regulation of this phenotype in P.

Assessment of ethanol tolerance of Kluyveromyces marxianus CCT 7735 selected by adaptive laboratory evolution.

Kluyveromyces marxianus CCT 7735 shows potential for producing ethanol from lactose; however, its low ethanol tolerance is a drawback for its industrial application. The first aim of this study was to obtain four ethanol-tolerant K. marxianus CCT 7735 strains (ETS1, ETS2, ETS3, and ETS4) by adaptive laboratory evolution.