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Ethanol is an organic material and a straight chain alcohol with the molecular formula C2H5OH, which shows significant importance on many industries such as pharmaceutical, cosmetics, fuel and food. It has been used for ages as an alcoholic beverage in either way of distillation or fermentation as it is a major metabolite for living organisms. It has achieved a large amount of usage as fuel or fuel additive for gasoline to obtain higher compression yield in motor engines. Also, ethanol displays an antiseptic affect whereas it denatures the proteins of multiple organisms, e.g. bacteria, namely that is utilized especially in the places which need to be deodorized, such as hospitals.
Bio fuels are considered environmentally friendly since they are produced from renewable energy sources such as agricultural feedstock, e.g. corn, sugar cane, cellulose wastes, which can store the unflagging energy of sun by photosynthesis. However, as this production requires large abundant terrains for agricultural crops, some hot debates are present having the content whether bio-ethanol can replace gasoline or not in terms of overall gain while the whole production, pollution and energy yield are taken into consideration simultaneously.
Current production methods for fuel ethanol are separated into two major groups as synthetic and fermentation processes. In fact, bio-ethanol and synthetic ethanol are totally in same characteristics with the exact formulas apart from "isotopic composition of carbon atoms" . Synthetic production is based on direct catalytic and indirect hydration of ethylene. In direct catalytic production process is carried out with high pressure steam, usually sulfuric acid or phosphoric acid is used as the catalyst.
Indirect production process is slightly less preferential method, in which first ethyl sulphate is generated by hydration under the effect of concentrated sulphuric acid, following hydrolysis reaction to obtain product ethanol.
On the other hand, industrial fermentation processes are distinguished as their raw materials like sugar crops, corn or lignocellulosic biomass. Depending on the type of raw material, pre-treatment steps and final product ethanol yield vary.
BU METOTLAR Ä°ÇÄ°N KAYNAK BUL!!
In our project, we are supposed to optimise the production of ethanol on a basis of anaerobic fermentation by Saccharomyces cerevisiae, which is the most commonly used microorganism to produce bio ethanol with a percentage of around 80% worldwide . If S. cerevisiae is under ideal conditions, the reactions in the yeast happen quickly to generate ethanol with an amount of approximately 50 mmol per grams of cell protein .
Choice of substrate as galactose has a basis that in many industrial processes which utilize lignocellulosic raw material, galactose is present as hemicellulose structure . Moreover, cellulosic ethanol production reduces the environmental concerns over both productions from corn and petroleum, since it is produced from lignocellulosic materials (composition of cellulose, hemicellulose and lignin) such as grasses, wood or the "edible" parts of plants. Additionally, lower green house gas emissions and decreased demand on fossil fuels result in protection of environment . The main challenge of utilizing lignocellulosic materials is that its highly cost and complex pre-treatment requirement to destruct the lignocellulosic matrix, as it is shown in the Figure 1 [6, lig].
Dilute acid hydrolysis
Cellulose + cellulases Fermentation
Cellulose + lignin
Dilute acid hydrolysis
Cellulose + cellulases hemicellulose syrup Fermentation
Figure 1: Simplified flowchart for the conversion of biomass to ethanol. Upper shows current
model technology and lower shows potential simplified technology. (Adapted from Zaldivar et al. 2001)
Lignocellulose also needs to undergo hydrolysis reaction as well as the production from corn. However, production from corn consumes glucose directly while hexose and pentose sugars are produced by lignocellulosic substrates. Moreover, some toxic compounds may appear during lignocellulose pre-treatment (e.g. lignin residues, acids) which should be removed. Since pentose sugars cannot undergo fermentation in S.C yeast, it has to be genetically modified to develop the utilization percentage of pentose-concentrated lignocellulosic raw material.
In an engineering point of view, high production yield from the raw material is priority, which means all sugars are converted to ethanol as possible. Three significant process steps should be carried out to modify S.C. to enhance the main compound of hemicelluloses-xylose- and hence lignocellulose utilization. Bacterial xylose isomerise genes are implemented, then pentose utilize genes from P. stipitis are injected and finally, xylulose expenditure should be upgraded [lig].
S. cerevisiae needs to be tailored since it preferentially utilizes glucose rather than galactose . In the presence of galactose and glucose both at high concentrations, glucose utilization is much favourable as a result of glycolytic enzymes and glucose suppression on galactolytic enzymes. When the suppression is alleviated, use of galactose substrate is amended due to the activation of "galactose-inducible" genes .
Even though glucose and galactose are both hexoses, they are merely distinguished by their fourth carbon atom. However, they have distinctive initial catabolism pathway until they are transformed to glucose-6-phospate (Glu-6P). Leloir pathway is the galactose utilization pathway as shown in the Figure 2, which is accountable for galactose to break down to Glu-6P after subsequent enzymatic reactions .
Figure 2: Leloir Pathway 
Some genes which have important role in GAL gene regulatory network as follows:
GAL2 [galactose permease] is responsible for taking galactose into cell cytoplasm [9, 12].
GAL1 [galactokinase] converts galactose to galactose-1-phosphate [Gal-1P] using ATP as phosphate source .
GAL7 [galactose-1-phosphate uridylyltransferase] is necessary for generation of glucose-1-phosphate [Glu-1P] from Gal-1P, using a side reaction which is under control of GAL10 gene .
GAL5 [phosphoglucomutase] transmits Glu-1P into glucose-6-phosphate [Glu-6P], which is the end of Leloir pathway as from that point all the reactions are carried out identically in the glycolytic pathway [9, 12].
GAL3 and GAL4 genes are bound to manage GAL regulatory network, with a mechanism that in the deficient amounts of galactose the collaboration of Gal3 with Gal80 destabilizes the interplay of Gal80 and Gal4, which allows the GAL genes to be activated, since Gal4 is a transcriptional activator protein and particularly binds to GAL gene promoters [8, 9, 12].
Gal80 binds directly to Gal4 and deactivates its transcription mechanism [8, 9].
Mig1 is in presence of glucose, represses expression of GAL1 and GAL4, which affects the entire system [8, 9].
GAL6 is a member of GAL regulatory mechanism and has negative control over GAL genes such as GAL1, GAL2 and GAL7 [8, 9].
Figure 4: Gal Regulatory Network [Christensen]
GAL GENE REGULATORY NETWORK
In order to improve the overall yield in ethanol production, the genes encoding particular enzymes in yeast can be overexpressed with the possibility of negative inferences in metabolism such as a lack of transcriptional or translational inconvenience. As a result, overexpressing the genes eventually ends up with undesirable alteration in metabolite levels by downregulation of other enzymes which are affected .
The optimum way of improving the flux in a metabolic pathway is expressing positive activators and decreasing negative regulators without alteration of metabolite levels and physiological restraints on metabolism .
The recombinant strains are named as SO3 [Î”gal80 Î”mig1], SO7 [pGAL4, 2Âµ], SO15 [Î”gal6], SO16 [Î”gal6 Î”gal80 Î”mig1], SO37 [Î”gal6 Î”gal80 Î”mig1& pGAL4, 2Âµ] whereas Î” symbol represent the mutant genes that differ from wild-type strain WT .
The results of the experiments are shown in Table 1 in terms of maximum specific galactose uptake, biomass yield, ethanol yield and increase in flux [%] respectively . CDW ne
Max. sp. galactose uptake [mmol gal/g CDW/h]
[g CDW/ g gal]
ethanol yield [mmol EtOH/ g gal]
increase in flux [%]
Î”gal6 Î”gal80 Î”mig1
Î”gal6 Î”gal80 Î”mig1& pGAL4, 2Âµ
Table 1 : .... .
The maximum galactose utilization is obtained with SO16 mutant strain as 41% compared with the wild-type strain [WT]. SO3 strain, in which Mig1 and Gal80 genes are deleted, results in 15% more yield in ethanol production. Moreover, deleting GAL6 protein [SO15 strain] provides 24% increase in flux of galactose utilization, which was a highly expected result as this particular gene prevents GAL gene network in a negative way. Moreover, overexpressing GAL4 [SO7 strain] yields in 26% increase in flux through the galactose pathway. A combination of SO7 and SO16 strains [SO37 strain] is expected to provide a superior utilization amount, however this strain resulted in surprisingly with a 19% increase. Insufficient yield might be because of Gal4 acts as a promoter to GAL6 and GAL80 as well as other GAL genes; thus the Gal enzymes come up with condensed negative effects of these particular genes on Gal4. Presumably the level of Gal4 has already reached an upper limit in the Î”gal6 Î”gal80 Î”mig1 triple mutant as a result of the lack of either Gal6 or Gal80, because too high a level of Gal4 has major physiological effects on cells. .
Consequently, genetically engineered strains are not enforced to any physiological load while they display the same growth rate with galactose substrate as the wild-type strain (WT). Direct conversion from galactose to ethanol has increased about the ratio of 3.09 mol of ethanol per 1 mol galactose up-take, as the biomass production has not improved. Eventually, genetically modified galactose utilization pathway of S. cerevisiae has ended up with a considerable ethanol production increase .
In Figure 3, glucose utilization pathway of S. cerevisiae is shown. It is completely the same with galactose utilization pathway from the point that conversion of galactose to Glu-6P (G 6P in the figure) is done through Leloir pathway.
Figure 3. S.cerevisiae metabolism [Förster].
MODELLING of METABOLIC NETWORKS
MATLAB software is used for the modelling of ethanol production, which represents the characteristic behaviour of S. cerevisiae through metabolic pathway. The results that are obtained after several trials of deletions of genes are shown in Table 2. However, the results do not match the predictions as fluxes decrease when the genes are deleted. Normally it has to enhance fluxes to yield in more ethanol production. This may be because of the complexity of biological systems. MATLAB cannot completely understand the reaction chains as it assumes the reactions steady-state while biological systems are dynamic. Also, these data are obtained from the codes written for glucose utilization pathway. Nevertheless, they can still provide an insight about the affects of deleting genes in galactose utilization network and how the fluxes of the metabolites change.
ADH1 (flux units)
ADH1 (flux units)
all the other genes
Table 2: MATLAB results
Systems biology is based on the biological processes in which molecular constituents of a single cell, a tissue or whole organism are comprehensively considered and their cooperation to the entire system are observed and exploited simultaneously. Since the genome of S. cerevisiae is well-mapped, the databases for gene sequence are obtained due to monitor metabolic pathways. Hence, increasing the yield of ethanol production becomes possible by altering the genes as the sequence and function of the genes in the yeast, the metabolic pathway and the interaction between proteins and metabolites are known using high-throughput technologies called "omics". In order to analyse the huge amount of data and set of interactions within the cell, computer-based models are constructed in terms of chemical equations, genome-scale and metabolic networks, while taking the advantage of "omics" and literature data as well [13, 14]. Several tools are used for investigating the levels of all the "omes" in order to manipulate the biological processes in industrial scale.
There are several methods to change the levels of gene expression, such as gene knockouts [deletion technique], small interfering RNAs (siRNA) and gene overexpression . These techniques can be applied either separately or together in one single application.
Small interfering RNA (siRNA) is a lab produced double-stranded RNA molecule and most notably intervenes with particular genes while silencing the expressions in different levels [kaynak bul]. Moreover, overexpression refers to extra imitations of the desired genes are placed into chromosome, hence more transcriptions and proteins are generated encouraging more production [kaynak bul]. However, it is eventually obtained after several experiments that overexpressing gal4 regulator gene has resulted in less yield. Thus, manipulation of ethanol production in this work is obtained by deletion of the genes GAL6, GAL80 and MIG1 and subsequently has lead to reduction of biomass production .
The relevant strategy of alteration in level of gene expression has been chosen to be deletion technique, as it is mentioned before. In this method, DNA template and the target gene sequence (GAL6, GAL80, MIG1) undergo a polymer chain reaction (PCR) resulting the enlargement of the regions of target sequence with oligonucleotides. Then, "marker cassettes" link the two separate genes generated by PCR reaction and subsequently bound together. As a result, the desired gene deletion occurs so that they cannot affect the gene expression [ ].
Figure X: Construction of gene disruption cassettes 
Monitor the transcription of relevant genes:
Transcriptomic tools are useful for determination of genome sequence of the yeast . DNA microarrays, Northern blotting, differential display, serial analysis of gene expression (SAGE) and dotblot analysis are common techniques which can be applied in the designation of relevant mRNA abundance in the sample . Nevertheless, apart from DNA microarrays, the others provide limited number of analysis at the same time. For instance, SAGE is sufficient in lab-scale as the gene tags are relatively short and the quantity of mRNA samples required is comparatively higher [16, 17].
DNA microarrays technology is a method in which mRNA specimens are classified. A stagnant high-density microarray, each related with a particular gene is located under interaction of mRNA hybridization. First, mRNA specimens are classified fluorescently by a reverse transcribed nucleotide, after that hybridisation occurs between marked samples and their integral cDNA target sequence on the array. Quantitative determination of each gene expression level is demonstrated subsequently, when fluorescence on each fragment is measured by image analysis. Comparing the both specimen is doable due to varied tagging with discrete colours on different mRNAs.
Figure X: DNA Microarray Technology 
The main advantage in industrial scale for ethanol production is that it is cheaper, applicable as the requirement of slightly less sample preparation and gives simultaneous results . Subsequently, computer aided clustering analysis is practised for collecting massive amount of gene expression data to reveal transcription levels .
Quantify the production of relevant enzymes:
The mRNAs determined by transcriptomic tools are not all converted into proteins. The protein abundance in the cell is measured with two main proteomic tools namely 2D- gel electrophoresis and isobaric tag for relative and absolute quantitation technique (iTRAQ).
2D gel electrophoresis is an analytical tool based on isoelectric point and molecular weight separation on the gel generating inimitable points, taking the advantage of two dimensions to acquire superior resolution. However, this method is labour intensive and inapplicable for hydrophobic proteins . On contrary, iTRAQ is considered to be superior in industrial ethanol production. In this technique, single peaks are generated in scanners as a result of "isobaric mass design" displaying the quantity of particular proteins. This method accurate and provide the determination of hydrophobic proteins as well .
Determine the levels of metabolites and flux through the central carbon metabolism:
Flux balance analysis (FBA) has been shown to be a very useful technique for analysis of metabolic capabilities of cellular systems. Living organisms survive, grow or strive with the help of the available nutrients they find in their environment. They transform the nutrients into molecules they can use through a complex set of chemical reactions called metabolism. About organisms for which the whole metabolism is approximately known (in our case S. cerevisiae), one can address the following problem: given some known available nutrients, which set of metabolic fluxes maximizes the growth rate of the organism .
FBA assumes the metabolic reactions as thermodynamically irreversible and steady-state. It also requires exact boundary identifications for fluxes to reach an optimum solution set. FBA helps to construct linear equations for each metabolite based on the mass balance of metabolites. MATLAB is suitable for mathematical analysis of the metabolisms as it creates
linear equations (matrices) to solve .
Determine final product concentrations:
Gas chromatography is a sufficient separation technique based on "equilibrium operations" such as evaporation of compounds without deformation in the structure. The gaseous samples being separated (mobile phase) collaborates with the various stagnant phases placed on the column; every constituent has discrete retention times yielding in analytical separation [gc]. After eluting ethanol by chromatography, final concentration is detected by either mass spectrometry or flame ionization detector (FID) afterwards .
Chemical ionization of ethanol occurs while the structure is broken down and subsequently results in ion current which is commensurate with existing carbon atoms in the sample. Thus, the determination of concentration is detected due to the density of ion current collected on electrode .
Figure X: Flame Ionisation Detector (FID) 
FID displays superior characteristics such as reproducibility, simplicity and ability of adaptation to continuous industrial processes of organic materials .
[Figür 1 in kaynaÄŸÄ±] Zaldivar, J., Nielsen, J., Olsson, L., 2001. Fuel ethanol production from lignocellulose: a challenge for metabolic engineering and process integration, Applied Microbiology and Biotechnology, 56, 17-34.
[Figür 4'nin kaynaÄŸÄ±] Christensen, T. S., Oliveira, A. P., Nielsen, J., 2009. Reconstruction and logical modeling of glucose repression, BMC Systems Biology, 3:7, available from: http://www.biomedcentral.com/1752-0509/3/7.
[Figür 3 kaynaÄŸÄ±] Förster1, J., Famili, I., Fu, P., Palsson2, B. Ø. and Nielsen1, J., 2003. Genome-Scale Reconstruction of the Saccharomyces cerevisiae Metabolic Network, Genome Research, 13, 244-253.
[FID figür kaynaÄŸÄ±] OSHA, United States Department of Labour, Retrieved in December 2009 from: http://www.osha.gov/dts/ctc/gas_detec_instruments/slide21.html.
[deletion ] Nikawa, J., Kawabata, M., 1998. PCR-and ligation-mediated synthesis of markercassettes with long flanking homology regions for genedisruption in Saccharomyces cerevisiae, Nucleic Acids Research, 26(3), 860-861.
[gc] Grob, R. L., Barry, E. F., 2004.Modern practice of gas chromatographyâ€Ž, pp.25-26.
[lig] Zaldivar, J., Nielsen, J., Olsson, L., 2001. Fuel ethanol production from lignocellulose: a challenge for metabolic engineering and process integration, Applied Microbiology and Biotechnology, 56, 17-34.