Background: Up to now, there have been three published versions of a yeast genome-scale metabolic model: iFF708, iND750 and iLL672. All three models, however, lack a detailed description of lipid metabolism and thus are unable to be used as integrated scaffolds for gaining insights into lipid metabolism from multilevel omic measurement technologies (e.g. genome-wide mRNA levels). To overcome this limitation, we reconstructed a new version of the Saccharomyces cerevisiae genome-scale model, iIN800 that includes a more rigorous and detailed description of lipid metabolism.

For a bio-based economy, microbial lipids offer a potential solution as alternative feedstocks in the oleochemical industry. The existing genome data for the promising strains, oleaginous yeasts and fungi, allowed us to investigate candidate orthologous sequences that participate in their oleaginicity. Comparative genome analysis of the non-oleaginous (Saccharomyces cerevisiae, Candida albicans and Ashbya gossypii ) and oleaginous strains (Yarrowia lipolytica, Rhizopus oryzae, Aspergillus oryzae and Mucor circinelloides) showed that 209 orthologous protein sequences of the oleaginous microbes were distributed over several processes of the cells. Based on the 41 sequences categorized by metabolism, putative routes potentially involved in the generation of precursors for fatty acid and lipid synthesis, particularly acetyl-CoA, were then identified that were not present in the non-oleaginous strains. We found a set of the orthologous oleaginous proteins that was responsible for the biosynthesis of this key two-carbon metabolite through citrate catabolism, fatty acid b-oxidation, leucine metabolism and lysine degradation. Our findings suggest a relationship between carbohydrate, lipid and amino acid metabolism in the biosynthesis of acetyl-CoA, which contributes to the lipid production of oleaginous microbes. INTRODUCTIONLipids are dynamically bioactive molecules that contribute to the regulation of several complex systems of living cells. Besides the medical perspective, the remarkable growth of the lipid field is undoubtedly driven by the demand for feedstock for the oleochemical industry. In addition to the production of n-3 and n-6 polyunsaturated fatty acids that are beneficial to human health, extensive attention is being directed to biodiesel production from micro-organisms to replace non-sustainable petroleum (Liu & Zhao, 2007; Vicente et al., 2010). Certain strains, such as Rhodosporidiun toruloides, Lipomyces starkeyi, Yarrowia lipolytica and Mucor circinelloides, are known to accumulate substantial amounts of lipids, accounting for more than 20 % of their biomass, and are thus called oleaginous strains (Ageitos et al., 2011;Beopoulos et al., 2009;Meng et al. 2009;Ratledge, 2004). Due to their short cultivation time, their high level of intracellular lipids that are predominantly triacylglycerol (TAG) and their utilization of various substrates, oleaginous yeasts and fungi have become important model systems for alternatives to traditional sources of lipids derived from fossil, animal and plant origins (Beopoulos et al., 2009;Ratledge, 2004). Therefore, an understanding of lipid physiology is required to improve the efficient production of lipids of commercial interest. An integrated approach has been implemented using recent developments for studying the lipid metabolism of these promising micro-organisms. It has been reported that the lipid production of these oleaginous species is enhanced by controlling cultivation or nutritional conditions (Certik et al., 1999;Ruenwai et al., 2010). Based on the bioc...

An ensemble classifier approach for microRNA precursor (pre-miRNA) classification was proposed based upon combining a set of heterogeneous algorithms including support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF), then aggregating their prediction through a voting system. Additionally, the proposed algorithm, the classification performance was also improved using discriminative features, self-containment and its derivatives, which have shown unique structural robustness characteristics of pre-miRNAs. These are applicable across different species. By applying preprocessing methods—both a correlation-based feature selection (CFS) with genetic algorithm (GA) search method and a modified-Synthetic Minority Oversampling Technique (SMOTE) bagging rebalancing method—improvement in the performance of this ensemble was observed. The overall prediction accuracies obtained via 10 runs of 5-fold cross validation (CV) was 96.54%, with sensitivity of 94.8% and specificity of 98.3%—this is better in trade-off sensitivity and specificity values than those of other state-of-the-art methods. The ensemble model was applied to animal, plant and virus pre-miRNA and achieved high accuracy, >93%. Exploiting the discriminative set of selected features also suggests that pre-miRNAs possess high intrinsic structural robustness as compared with other stem loops. Our heterogeneous ensemble method gave a relatively more reliable prediction than those using single classifiers. Our program is available at http://ncrna-pred.com/premiRNA.html.