Abstract: Aloe Vera is a short-stemmed succulent plant which is commonly used in Myanmar traditional medicine. A. vera gel was also used as food addictive. This study aims to improve the Myanmar folk medicine to a functional beverage. In this research, Aloe vera was fermented with Saccharomyces cerevisiae for 6 months. Three different processes were carried out. Process I contains A. vera 10%, sugar 30%, water 50%, and starter culture 10%, process II contains A. vera 10%, sugar 15%, honey 15%, and water 50%, starter culture 10%; process III contains A. vera 10%, honey 30%, water 50%, starter culture 10%. During wine fermentation, the wine parameters such as alcohol content, total soluble solid (ºBrix), pH, color and cell population were analyzed. After 30 days of fermentation, total cell population remained 2.8x106 in P-I, P-II and 3.2x106 in P-III. Total soluble solid content dropped to 15.8 in P-I, P-II and 15.7 in P-III. After 30 days, clear wine was transferred to other vassals for racking. After 6 months of racking, microbial population reached under detectable level and alcohol content was round about 11% but not significantly different among these processes. P-II was found to have the highest color intensity at 450 nm and it got the most taster satisfaction when sensory evaluation was carried out using five hedonic scales after 6 month of racking.
Abstract: Cellular complexity stems from the interactions
among thousands of different molecular species. Thanks to the
emerging fields of systems and synthetic biology, scientists are
beginning to unravel these regulatory, signaling, and metabolic
interactions and to understand their coordinated action. Reverse
engineering of biological networks has has several benefits but a
poor quality of data combined with the difficulty in reproducing
it limits the applicability of these methods. A few years back,
many of the commonly used predictive algorithms were tested
on a network constructed in the yeast Saccharomyces cerevisiae
(S. cerevisiae) to resolve this issue. The network was a synthetic
network of five genes regulating each other for the so-called in
vivo reverse-engineering and modeling assessment (IRMA). The
network was constructed in S. cereviase since it is a simple and well
characterized organism. The synthetic network included a variety
of regulatory interactions, thus capturing the behaviour of larger
eukaryotic gene networks on a smaller scale. We derive a new set of
algorithms by solving a nonlinear optimization problem and show
how these algorithms outperform other algorithms on these datasets.
Abstract: Storage stability is the important factor of baker's yeast quality. Effect of the storage period (fifteen days) on storage sugars and cell viability of baker's yeast, produced from three S. cerevisiae strains (FC-620, FH-620, and FAT-12) as comparison with baker's yeast produced by S. cerevisae F-707 (original strain of baker's yeast factory) were investigated. Studied trehalose and glycogen content ranged from 10.19 to 14.79 % and from 10.05 to 10.69 % (d.w.), respectively before storage. The trehalose and glycogen content of all strains was decreased by increasing the storage period with no significant differences between the reduction rates of trehalose. Meanwhile, reduction rates of glycogen had significant differences between different strains, where the FH-620 and FC-620 strains had lowest rates as 18.12 and 20.70 %, respectively. Also, total viable cells and gassing power of all strains were decreased by increasing the storage period. FH-620 and FC-620 strains had the lowest values of reduction rates as an indicator of storage resistant. Where the reduction rates in total viable cells of FH-620 and FC-620 strains were 22.05 and 24.70%, respectively, while the reduction rates of gassing power were 20.90 and 24.30%, in the same order. On other hand, FAT-12 strain was more sensitive to storage as compared to original strain, where the reduction rates were 35.60 and 35.75%, respectively for total viable cells and gassing power.
Abstract: Problem of food preservation is extremely important
for mankind. Viscous damage ("illness") of bread results from
development of Bacillus spp. bacteria. High temperature resistant
spores of this microorganism are steady against 120°C) and remain in
bread during pastries, potentially causing spoilage of the final
product. Scientists are interested in further characterization of bread
spoiling Bacillus spp. species. Our aim was to find weather yeast
Saccharomyces cerevisiae strains that are able to produce natural
antimicrobial killer factor can preserve bread illness. By diffusion
method, we showed yeast antagonistic activity against spore-forming
bacteria. Experimental technological parameters were the same as for
bakers' yeasts production on the industrial scale. Risograph test
during dough fermentation demonstrated gas production. The major
finding of the study was a clear indication of the presence of killer
yeast strain antagonistic activity against rope in bread causing
bacteria. After demonstrating antagonistic effect of S. cerevisiae on
bacteria using solid nutrient medium, we tested baked bread under
provocative conditions. We also measured formation of carbon
dioxide in the dough, dough-making duration and quality of the final
products, when using different strains of S. cerevisiae. It is
determined that the use of yeast S. cerevisiae RCAM 01730 killer
strain inhibits appearance of rope in bread. Thus, natural yeast
antimicrobial killer toxin, produced by some S. cerevisiae strains is
an anti-rope in bread protector.
Abstract: The aim of this study was to select the best strains of
Saccharomyces cerevisiae able to resist lead and cadmium. Ten
strains were screened on the basis of their resistance at different
concentrations of 0, 2, 4, 8, 12, 16, 20 and 24 ppm for Pb and 0, 0.5,
1, 2, 4, 6, 8 and 10 ppm for Cd. The properties of baker's yeast
quality were decreased by the increase of Pb or Cd in growth
medium. The slope values of yield, total viable cells and gassing
power of produced baker's yeast were investigated as an indicator of
metal resistant. In addition, concentrations of Pb and Cd in produced
baker's yeast were determined. The strain of S. cerevisiae FH-620
had the highest resistance against Pb and Cd and had the minimum
levels of both two investigated metals in produced baker's yeast.
Abstract: In this study, we are interested in a species of the
family of Asteraceae (Tagetes erecta). This family is considered as a
source of antimicrobial extracts with strong capacity. The extraction
of the flavonoids is carried out by the method of liquid/liquid with the
use of successive solvents. Afterwards, we evaluated the biological
activity of the flavonoids on five pathogenic bacterial stocks such as
Escherichia coli, Bacillus subtilis, Klebsiella pneumoniae,
Pseudomonas aeruginosa and Staphylococcus aureus and two stocks
of yeasts to knowing Candida albicans) and Saccharomyces
cerevisiae, by employing the method of the aromatogramme starting
from a solid disc. The result of the antimicrobial activity shows an
action and a variable degree of sensitivity according to bacterial
stocks tested. It will be noted that the flavonoids have an inhibiting
effect on E. coli, B. subtilis, K. pneumoniae and S. aureus. But a
resistance with respect to the extract by P. aeruginosa, C. albicans
and S. cerevisiae is to be mentioned.
Abstract: The biomass-based fuels have become great concern in order to replace the petroleum-based fuels. Biofuels are a wide range of fuels referred to liquid, gas and solid fuels produced from biomass. Recently, higher chain alcohols such as 3-methyl-1-butanol and isobutanol have become a better candidate compared to bioethanol in order to replace gasoline as transportation fuel. Therefore, in this study, 3-methyl-1-butanol was produced through a fermentation process by yeast. Several types of yeast involved in this research including Saccharomyces cerevisiae, Kluyveromyces lactis GG799 and Pichia pastoris (KM71H, GS115 and X33). The result obtained showed that K. lactis GG799 gave the highest concentration of 3-methyl-1-butanol at 274 mg/l followed by S. cerevisiae, P. pastoris GS115, P. pastoris KM71H and P. pastoris X33 at 265 mg/l, 190 mg/l, 182 mg/l and 174 mg/l respectively. Based on the result, it proved that yeast have a potential in producing 3-methyl-1-butanol naturally.
Abstract: Abundant and cheap agricultural waste of oil palm trunk (OPT) juice was used to produce bioethanol. Two strains of Saccharomyces cerevisiae and a strain of Pichia stipitis were used to produce bioethanol from the OPT juice. Fermentation was conducted at previously optimized condition at 30oC and without shaking. The kinetic parameters were estimated and calculated. Monod equation and Hinshelwood model is used to relate the specific growth to the concentration of the limiting substrate and also to simulate bioethanol production rate. Among the three strains, single S. cerevisiae Kyokai no. 7 produce the highest ethanol yield of 0.477 g/l.h within the shortest time (12 h). This yeast also produces more than 20 g/l ethanol concentration within 10 h of fermentation.
Abstract: Coproduction of fructose and ethanol from dates extract by a glucose-selective S. cerevisiae ATCC 36859 strain has been studied. Various initial sugar concentrations (i.e., 131.4, 315.3, 408.2, and 500.0 g/l) have been tested. The fermentation experiments were performed in a water shaker bath at 30°C and 120 rpm. The results showed that highest yields of fructose (95.0%) and ethanol (72.8%) were achieved for the 131.4 g/l concentration. Increasing the initial concentration to 315.3 g/l resulted in lower yields of fructose (82.2%) and ethanol (61.0%). However, further increase to 408.2 g/l increased the fructose yield (97.5%) at the expense of ethanol yield (42.0%) due to probable substrate inhibitions that resulted in lower glucose conversion. At 500 g initial sugar/l the growth rate of ATCC 36859 was highly inhibited.
Abstract: Yeast cells live in a constantly changing environment that requires the continuous adaptation of their genomic program in order to sustain their homeostasis, survive and proliferate. Due to the advancement of high throughput technologies, there is currently a large amount of data such as gene expression, gene deletion and protein-protein interactions for S. Cerevisiae under various environmental conditions. Mining these datasets requires efficient computational methods capable of integrating different types of data, identifying inter-relations between different components and inferring functional groups or 'modules' that shape intracellular processes. This study uses computational methods to delineate some of the mechanisms used by yeast cells to respond to environmental changes. The GRAM algorithm is first used to integrate gene expression data and ChIP-chip data in order to find modules of coexpressed and co-regulated genes as well as the transcription factors (TFs) that regulate these modules. Since transcription factors are themselves transcriptionally regulated, a three-layer regulatory cascade consisting of the TF-regulators, the TFs and the regulated modules is subsequently considered. This three-layer cascade is then modeled quantitatively using artificial neural networks (ANNs) where the input layer corresponds to the expression of the up-stream transcription factors (TF-regulators) and the output layer corresponds to the expression of genes within each module. This work shows that (a) the expression of at least 33 genes over time and for different stress conditions is well predicted by the expression of the top layer transcription factors, including cases in which the effect of up-stream regulators is shifted in time and (b) identifies at least 6 novel regulatory interactions that were not previously associated with stress-induced changes in gene expression. These findings suggest that the combination of gene expression and protein-DNA interaction data with artificial neural networks can successfully model biological pathways and capture quantitative dependencies between distant regulators and downstream genes.
Abstract: Whole genome duplication (WGD) increased the
number of yeast Saccharomyces cerevisiae chromosomes from 8 to
16. In spite of retention the number of chromosomes in the genome
of this organism after WGD to date, chromosomal rearrangement
events have caused an evolutionary distance between current genome
and its ancestor. Studies under evolutionary-based approaches on
eukaryotic genomes have shown that the rearrangement distance is an
approximable problem. In the case of S. cerevisiae, we describe that
rearrangement distance is accessible by using dedoubled adjacency
graph drawn for 55 large paired chromosomal regions originated
from WGD. Then, we provide a program extracted from a C program
database to draw a dedoubled genome adjacency graph for S.
cerevisiae. From a bioinformatical perspective, using the duplicated
blocks of current genome in S. cerevisiae, we infer that genomic
organization of eukaryotes has the potential to provide valuable
detailed information about their ancestrygenome.