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Abstract: The Ethanol fuel, which is usually fermented by yeast, can provide a renewable energy. Because of the huge demand for ethanol fuel, the regulation of genes in saccharomyces cerevisiae is a great way to maximize the productivity of ethanol. This paper mainly introduces some genes regulation way and use the matlab software to optimize the metabolism pathway. But, it can only be used for an initial assumption to create some possible pathways, due to the limitation of matlab. The monitor of target genes expression and the measurement of the whole carbon metabolism are also described in this paper.
Key words: Genes regulation, measurement of genes expression, measurement of genes transcription.
Ethanol is a clean and renewable energy fuel which can be obtained by chemical synthesis and fermentation. For chemical synthesized ethanol, the mainly synthesis methods include direct catalytic hydration of ethylene, indirect hydration of ethylene and homologation of methanol that produced from petroleum sources. However, most of these synthesized ethanol is used as raw material for fine chemical products, such as acrylic polymer and paraldehyde.(Kosaric, N. et al, 2001)
Fermentation is widely applied to produce ethanol to supply to ethanol fuel market, due to the rich glucose sources from corn, sugarcane and other cheap agricultural products. Besides, with the development of biochemical techniques, the target genes can be regulated by upregulating and knocking-down to make the biochemical process more efficient and cheaper than chemical synthesis(Yang, C.H. , 2007).
Tow typical microbes can be used for ethanol fermentation (yeast and bacteria). Comparing with bacteria, ethanol produced by yeast is of high selectivity, low accumulation of byproducts, as well as high ethanol yield, high fermentation rate. In addition, the tolerant performance of yeast to high substrate concentrations, high concentrate ethanol, and low pH are beyond the performance of bacteria does. (Kosaric, N. et al, 2001). The genetic stability and viability of yeast cells under different process conditions and at high temperature are also acceptable. Even though, there are difficulties in finding a strain that has all these characteristics, some yeast strains can quite suitable for ethanol fermentation. Some widely used, high efficient strains are S. uvarum, candida utilis and Saccharomyces cerevisiae. Saccharomyces anamensis and Schizosaccharomyces pombe are also used in some cases(Yang, C.H. , 2007).
Saccharomyces cerevisiae can metabolite different carbon compounds into different products, depends on the metabolic pathways (aerobic and anaerobic situations). Under anaerobic situation, ethanol is produced, the equation for Saccharomyces cerevisiae from glucose to ethanol is :
Metabolism pathway of glucose by saccharomyces cerevisiae on the tow conditions is shown in Figure 1 (Kosaric, N. et al, 2001).
Figure 1. The simplified metabolism pathway of saccharomyces cerevisiae (Kosaric, N. et al, 2001).
Many factors can affect the final yield of ethanol, such as the substrate concentration, the oxygen, the concentration of ethanol, temperature and pH. However, changing the genes expression can have a greater effect on the output of ethanol, than altering these factors. A better pathway can be found by regulating these fluxes through computer simulation softwares, for example, the matlab.
The nutrients required by yeast for growth are: nitrogen, sulfur, carbon, oxygen, hydrogen, and quantities of minerals. Growth factors such as amino acids and vitamins are also needed. For Saccharomyces cerevisiae, the typically available C6 carbon sources for fermentation is shown in Table 1(Kosaric, N. et al, 2001).
Table 1. Typical C6 carbon sources for saccharomyces cerevisiae fermentation (Kosaric, N. et al, 2001)
Sugar Basic unit Type of basic subunit
Glucose Glucose Aldoes surgar
Fructose Fructose Ketose sugar
Sucrose Glucose, fructose Aldose and ketose
The choice of the raw material determined the whole fermentation processes, as for saccharomyces cerevisiae, the appropriate raw material can be various sugar crops, such as sugarcane, fodder beet and fruit crops, which can be readily fermented by direct fermentation. Corn, potato and starch can also be used as a good raw material after conversion. Raw materials used for fermentation differ with the actual conditions of each area. Brazil, for instance, adopted corn as the main raw material for ethanol fermentation, due to the suitable conditions for corn growing (Kosaric, N. et al, 2001).
Taking the supply of raw materials into consideration, potato is the appropriate raw material in U.K, because of the sufficient high quality potato supply. It can also grow in most kinds of climates and types of soil, including sandy and dry soil. The main component of potato is starch, which is the valid carbon source for ethanol fermentation. A fermentation process for potato fermentation is shown in Figure 2. (Kosaric, N., 2001). This is a semi-continuous process, which can also be applied to grain. The brief steps for the process are:
Potatoes are mashed and then hydrolyze the starch with amylases. The rapid steam treatment is used in the hydrolyzed stage at 150oC for 3 min.
The raw starch then is cooled to 70oC for liquefaction with commercial amylase preparations of bacterial origin; and then cooled further to 30oC.
The prepared raw material then goes to the alcohol fermentation stage from d to h.
Figure 2. The process of semi-continuous production of alcohol from potatoes. a. Preheater; b. Pulper; c. Enzyme treatment vessel; d. Flash cool; e Boiler tube; f Holding tank; g. Condenser; h. Liquefaction vessel. (Kosaric, N. et al, 2001).
The genomic strategies for maximizing ethanol
2.1 Over expression of INO1 and ADH1
The normal saccharomyces cerevisiae can not survive in the high concentrated ethanol environment. However, for ethanol fuel companies, which need a high concentrated ethanol, due to the improvement of ethanol tolerance can make the manufacture process more efficient. By over expression of INO1, the ethanol tolerance can be increased. Once the ethanol tolerance improved, it is possible to over express another genes, which control the produce of ethanol. Combining with the Saccharomyces cerevisiae metabolism model shown in figure 2 (F?rster et al., 2003), ADH1 is found as an appropriate gene to control the synthesis of ethanol. There are tow main stepes for this strategy, the over expression of INO1 and ADH1.
(1) The regulation of INO1
According to Hong ME`s report about increasing ethanol tolerance by inverse metabolic engineering, the over-expressed INO1, DOG1, HAL1 can make saccharomyces cerevisiae survive in a higher concentrated ethanol condition. The data of optimized saccharomyces cerevisiae is shown in table 2.(Hong ME., et al, 2010).
Table 2. The growth rate and productivity of optimized saccharomyces cerevisiae (Hong ME., et al, 2010).
INO1 DOG1 HAL1
Growth rate (h-1)
5% Ethanol and 20% glucose 0.053 0.063 0.051
Volumetric productivity (g/L/h)
5% Ethanol and 20% glucose 2.205 1.858 1.457
Among these three genes, INO1 is of higher growth rate and productivity, which is adopted in this strategy.
The regulation of ADH1
The reaction controlled by ADH1 can be simplified as: Acetic acid ''Ethanol (catalyzed by alcohol dehydrogenase). Up-regulate the concentration of ADH can increase the ethanol conversion rate.
2.2 The GAL1 knock-down
GAL1 is a structural and regulatory gene, which can affect the metabolism of galactose in saccharomyces cerevisiae. The GAL1 knock-domw has a greater effect on peripheral functions(pyruvate), than on central metabolism (Rezaee et al., 2004). However, knock-down GAL1 will increase the ethanol formation rate, but the biomass will decrease, comparing with the wild saccharomyces cerevisiae (Rezaee et al., 2004). Beacause of that, this strategy is more suitable for a small scale ethanol fermentation with high efficient. As for further research, it is possible to solve this problem by replacing GAL1with another specified genes, which can enhance the biomass.
2.3 GLN1 and GLT1 over-expression combine with GDH1 deletion
For saccharomyces cerevisiae ethanol anaerobic fermentation, a lot of byproducts formed with the production of enthanol, which consume a huge part of the yeast`s energy and nutrient. This strategy is aimed at reducing the byproducts, as well as increasing the ATP flux. For most of byproducts, the glycerol is the most important compound, which is produced by reoxidizing NADH. According to Torben`s research, the over-expression of GLN1 and GLT1 means a higher concentration of glutamine synthetase and glutamate synthase, which can increase the consumption of ATP, and the deletion of GDH1 will decrease the formation of NADH (Torben L. N et al, 2000). This strategy can improve the output of ethanol for 10%, but it is hard to obtain the required strains, due to the complex optimization of the whole process.
Optimize the pathway by modelling of metabolic networks with matlab
The simplified metabolic network for saccharomyces cerevisiae is shown in Figure 3 (F?rster et al., 2003).
Figure 3. The brief saccharomyces cerevisiae metabolism model used for matlab (F?rster et al., 2003)
There are 46 fluxes including biomass chosen for metabolism network, which are modeled by using matrix for each reaction. The yield of ethanol is determined by ADH1. Tow regulate methods are used (Single gene deletion and double genes deletion), with the scripts provided by professor Serafim Bakalis in Modern Genome-based bioscience lecture. In this case, the matlab will delete all the fluxes one by one, and then find the maximum value of biomass and ADH1. The selected results with both high biomass and ADH1 are shown in table 3. and table 4.
Table 3. The single gene deletion by matlab
The deleted genes
Flux Biomass (g) ADH1 (mol) The deleted genes
Flux Biomass (g) ADH1 (mol)
FUM1 73.2845478 0.07343498 LSC1LSC2 73.2845476 0.07343498
SDHcomplex 73.2845476 0.07343498 NADHX 24.3160350 1.23686819
KGD1KGD2 73.2845475 0.07343498 FADHX 73.2845476 0.07343498
ACETR 77.0835962 0.38827952 ShuttleX 28.1912895 1.01413304
The highest yield of ethanol is obtained by deleting NADHX, due to the most significant byproducts, glycerol is produced by reoxidizing NADH. This is a feasible strategy, which is mentioned in strategy part.
Table 4. The selected double genes deletion data by matlab
The deleted genes
Flux Biomass (g) ADH1 (mol)
KGD1KGD2 & SDHcomplex 73.28454717 0.0734350129
KGD1KGD2 & FRDS2 73.28454761 0.0734349862
SDHcomplex & FRDS2 73.28454765 0.0734349848
NADHX & FADHX 24.31603498 1.2368681913
NADHX & ATPX 24.31603502 1.2368681912
NADHX & ATPX and NADH &FADHX are the best pairs for double genes deletion, with a high ethanol yield. Compare with the single gene deletion, these fluxes results are the same, which indicate that, the yield of ethanol is mainly controlled by NADHX.
The limitations for this optimize method are that:
After deleting some genes, the real metabolic network may become unstable, due to many related reactions are not taken into consideration in this simplified metabolism network. Matlab can only simulate the pathway by using matrix.
The genes shown in the metabolic pathway, may be of multifunction to the Saccharomyces cerevisiae `s metabolism, which can affect the whole pathway and the final matlab results.
Hence, the matlab flux balance analysis can only be used as possible results for optimizing the genes exprssion. The adoption of final genetic strategy should depend on the practical researches.
4.The regulation of genes expression
The alteration of genes in saccharomyces cerevisiae, which can be classified as four main types: The gene knockdown; Gene knockouts (Overton, T, 2010); Gene over-expression(S. B. Primrose et al, 2001) and the introduction of a new gene(U. Klinner et al, 2004).
Gene knockdown is aimed at interrupting or inhibiting the mRNA transcription by introduce small interfering RNAs (siRNA) to combine with the mRNA. This will lead to a decreased concentration of formed protein or enzymes.
Gene knockout is completely remove the target gene from the genome of saccharomyces cerevisiae by replacing it with another cassette. The introduced cassette genome should contain corresponding homology regions to locate and replace the position of the target gene.
Gene over-expression is to introduce extra copies of the target gene on a vector in saccharomyces cerevisiae to obtain more mRNA transcript to get more target prodcuts. (Overton, T, 2010)
5. The analysis of gene expression by cDNA microarray
The expression of optimized target genes in saccharomyces cerevisiae can be examined by cDNA microarray technology. The main theory of this technique is the Reverse transcription of mRNA to DNA, which can be labeled by fluorescent dyes. The brief steps for this gene expression measurement is shown in Figure 4 (Choowong Auesukaree, 2006).
Figure 4. The cDNA microarray measurement(Choowong Auesukaree, 2006).
The stages to examine the target genes expression with cDNA microarray measurement:
(1) RNA extracted from wild Saccharomyces cerevisiae and optimized saccharomyces cerevisiae are prepared.
(2) The reverse transcription of prepared RNA to cDNA.
(3) Labeling both of the cDNA with different suitable fluorescence.
(4) Then, the two labeled cDNA samples are hybridized to the array.
(5) Scanning on the array by a laser scanner or a CCD camera to get the identified pictures.
(6) Finally, the final microarray image outputs are analyzed by computer programs to get the results.
There are other measurements that used to examine the gene expression,such as Northern blotting (Alwine, J.C et al, 1977) and differential display (Liang, P. et al, 1992). However, when compare with cDNA microarray measurement, these tow techniques can not do the lager scale quantitative measurement.
6. Metabolic flux analysis by 13C labeled isotopes
This technique is based on the 13C labeled isotopes that is marked on the glucose, after feeding with the labeled glucose to yeast, the 13C isotopes will be distributed around the whole carbon metabolism fluxes. Then, it can be identified by nuclear magnetic resonance, and the data can be analysed by framework softwares through computer to get the concentration of each flux in carbon metabolism, including the concentration of the product, ethanol. An example of metabolic flux analysis is shown in figure 5
(Patrick F. S. et al, 2007).
Figure 5. The typical metabolic flux analysis by 13C labeled isotopes (Patrick F. S. et al, 2007)
7. Quantify the production of relevant enzymes by electrophoresis
The emzymes of optimized saccharomyces cerevisiae can be charged by isoelectric focusing, because of the amphoteric characteristic of enzyme protein. When there is a electric field, the enzymes will be charged and aggregate on the electric pole. This process is shown in Figure 6 (Westermeier R., 2005).
Figure 6.The separation of enzymes by isoelectric focusing(Westermeier R., 2005).
The regulation of genes in saccharomyces cerevisiae makes the ethanol fermentation process more efficient and cheaper by increasing the ethanol tolerance and maximize the formation rate of ethanol. After optimizing the metabolic fluxes with matlab and practical research, high efficient saccharomyces cerevisiae can be applied to a lage scale of ethanol fermentation. The genes can be regulated by PCR, the transcription of relevant genes can be monitored by cDNA microarray technology, the Quantify of relevant enzymes can be measured by electrophoresis, and the metabolism fluxes can be observed by 13C labeled isotopes. Since the genetic code of saccharomyces cerevisiae has been known, there will be more high efficient genetic methods to produce ethanol fuel.