In the preceding section, the possibility of improving the productivity in batch reactors was examined by increasing the initial concentration of xylose. Elevation of xylose concentration has both positive and negative effects, i. e., it facilitates simultaneous consumption initially, but prolongs the fermentation time after glucose consumption. Overall, this trade-off resulted in the increase of ethanol productivity only at low sugar concentrations. It was further shown that continuous operation produces significantly more ethanol than batch when only glucose is consumed, but less when mixed sugars are consumed. These findings suggest the investigation of new reactor configurations which may outperform conventional batch fermentation.

We consider the following five configurations (denoted by C1 to C5), each of which combines two different reactor operations (O1 and O2) (Table 4.1). C1 represents a conventional batch operation where mixed sugars are fermented to ethanol by recombinant S. cerevisiae. The same is repeated at every batch (i. e., O1 is identical to O2). In C2, O1 is a batch reactor for the growth of the "wild-type" S. cerevisiae which can ferment glucose alone. Leftover sugars in O1 are then fed to O2 (i. e., fed-batch operation) where mixed sugars are fermented using the recombinant strain. C3 is the same as C2 except that a chemostat is used

for O1. C4 and C5 are respective counterparts of C2 and C3, and these two groups are differentiated only by the xylose feeding policy in O2. That is, in C2 and C3, all leftover sugars in O1 are fed into O2 at its start-up (which implies that O2 is a batch system with elevated initial concentration of xylose). In C4 and C5, on the other hand, the xylose feeding rate is optimized such that the ethanol productivity in O2 is maximized.

We introduced a continuous reactor in C3 and C5 in the above. Chemostats have been preferred less than batch reactors in practice. One of the primary reasons for this is the genetic instability of fermenting organisms as continuous operation will impose strong selective pressure of fast growing cells instead of efficient ethanol producers. This will pose a serious problem for recombinant yeast strains, but may not for the wild-type. Thus, we consider C3 and C5 also as practically meaningful configurations.

In Table 4.2, an overall comparison is made for C1 to C5 at three different sugar concentrations with respect to the actual productivity and its relative change (in comparison to C1), respectively. From the comparison of the C2-C3 group and the C4-C5 group, it is clear that the effect of optimizing the feed rate is most significant at high sugar concentration, and appreciable at medium, but least at low. Strangely, at [GLC]/[XYL]=20/10, the productivities of C4 and C5 with optimal feeding policies are lower than those of C2 and C3, respectively, where all extra sugars are dumped into reactors at their start-up without optimization. This is because the initial feeding of C2 and C3 is closer to the "true" optimal than the feed profiles of C4 and C5 obtained from direct methods involving control profile discretization (Song et al., 2011). Other than this exception, C5 exhibits the highest productivity among all other configurations. In

comparison to C1, C5 achieves a substantial increase of the bioethanol productivity, i. e., by 48, 29 and 23 % when [GLC]/[XYL] = 20/10, 70/35, and 120/60, respectively. [GLC]/[XYL] denotes the mass concentration ratio of glucose and xylose.

4. Conclusion

Various possibilities of increasing the productivity of lignocellosic bioethanol at the fermentation step have been discussed, including metabolic pathway modification of fermenting organisms, optimization of reactor operating conditions, and synergistic combination of different types of reactors. Mathematical models play a key role in establishing rational strategies at such diverse levels. The success of the proposed methods, of course, depends on the reliability of the employed mode. We have demonstrated that the cybernetic models are uniquely effective for the in silico analysis of fermentation systems in view of their capacity to address productivity.

In regard to strain modification, it is emphasized that increasing the productivity rather than the yield is a more suitable goal as the former is directly related to economic competiveness. Note that emphasis on productivity is not at undue expense of yield since any pronounced drop on yield would also lead to a drop in productivity. On the other hand, sole stress on yield at the expense of productivity (due to a possible drop in growth rate) is not conducive to economics. Therefore, in the course of metabolic engineering undergoing several rounds of analysis and synthesis of strains, the productivity issue must be considered from the very outset. While the HCM framework based on a reduced subset of EMs can be useful in developing basic guidelines for flux redistribution of fermenting organisms, reasonable interpretation should be made under the possible loss of modes with significance for strain improvement. For metabolic engineering application, more sophisticated frameworks such as Lumped HCM (L-HCM) (Song & Ramkrishna, 2010; 2011) or Young’s model (Young et al., 2008) represent promising methodologies in the future.

It is also shown that the productivity of lignocellulosic bioethanol can significantly be enhanced by synergistic combination of continuous and fed-batch reactors and optimizing their operating conditions. While experimental verification should follow, our model-based study provides solid proof-of-concept support for the success of the proposed methods.

5. Acknowledgment

The authors acknowledge a special grant from the Dean’s Research Office at Purdue University for support.

Because the specific activity of cellulase enzymes is at least two-fold lower than that of other hydrolytic enzymes such as starch-hydrolyzing enzymes (Zhang and Lynd 2004; Wilson 2008), even native cellulolytic organisms must produce a high titer of cellulase to efficiently hydrolyze cellulose. The need for synthesis of a large quantity of cellulases is a "metabolic burden," even to native cellulolytic organisms (Zhang and Lynd 2005). Thus, heterologous expression of cellulolytic enzymes in industrial ethanol-producing hosts such as S. cerevisiae,

E. coli and Z. mobilis is especially challenging. Despite these obstacles, several recombinant strains have been engineered for efficient cellulosic ethanol production.

During the fermentation process, several by-products are produced together with ethanol. In co-culture fermentation which involves different strains, different side-products besides ethanol are produced. The list of by-products and their applications in industry are listed in Table 5.

The production of by-products somehow reduces the ethanol yield due to the competition from other metabolic conversions. The inhibition of lactic and acetic acids on yeast for ethanol production in corn mash was examined when both the acids synergistically reduced the rates of ethanol synthesis and the final quantities of ethanol produced by the yeast (Graves et al., 2007). The inhibitory effects of the acids were more apparent at elevated temperatures. So, a reduction in the formation of by-products is needed to achieve higher ethanol yield.

Alternatively, a fermentation process should not be only aimed for higher conversions of raw materials and ethanol productivity, but should rather take the advantage of the byproducts released during the transformation of feed stocks and convert them into valuable co-products. To reduce the inhibition effect, in-situ separation can be applied to separate the valuable co-product from the process. In this way, economical and environmental criteria can be met. However, depending on the objective and the economic analysis of the particular ethanol plant, the by-products may either generate extra revenue for the plant or just an inhibition the conversion process.

Among the ethanol byproducts, glycerol and lactic acid are used extensively by industries and can increase the production profit. These fermentative products have attracted interest owing to their prospect environmental friendliness and of using renewable resources instead of petrochemical. These byproducts have broad applications which can generate lucrative profit for the processes i. e. lactic acid. The global market for lactic acid is predicted to reach 328.9 thousand tonnes by 2015 (Plastics Today, 2011). The world consumption of lactic acid is stimulated by its applications in key industries such as cosmetics, biodegradable plastics and food additives. Lactic acid is used as a pH balancer in shampoos and soaps, and other alpha hydroxy acid applications, was expected to elevate the consumption in the market. Polymer lactic acid (PLA) for biodegradable plastics has properties similar to petroleum derived plastic and was expected to increase the demand for environmental friendly packaging. Food additives will continue to be the largest application area for lactic acid globally, but biodegradable plastics represent the fastest growing end-use application.

Glycerol or glycerin is a simple alcohol produced by S. cerevisiae during glucose fermentation to ethanol to maintain the redox balance. The global market for glycerin is forecasted to reach 4.4 billion pounds by 2015 (PRWeb, 2010). The increased demand for glycerin was reported to originate from various end-use area such as oral care, personal care, pharmaceutical and food and beverage. In fact, there are over 1,500 end-uses for the chemical. In most products, glycerin is used in very small portions with exception in a few end-uses which require a significant amount of glycerin in their formulation. Glycerin is also used in several novel applications such as propylene glycol, syngas and epichlorohydrin and it is expected to improve the glycerin demand.

Glycerol also can be potentially used as fuel additives for diesel and biodiesel formulation that assist to a decreasing in particles, hydrocarbons, carbon monoxide and unregulated aldehydes emissions. It can also act as cold flow improvers and viscosity reducer for use in biodiesel and antiknock additives for gasoline (Rahmat et al., 2010). Since glycerol is also produced in the fermentation broth, it is attractive as an entrainer to reduce the use of fresh entrainer in extractive distillation of azeotrope mixture of ethanol-water system.

2. Conclusion

Cassava is an attractive alternative as the carbon substrate for ethanol production especially where water availability is limited as it can tolerate drought and yields on relatively low fertility soil. The conventional method for the ethanol production involves liquefaction, saccharification and fermentation steps which are time consuming and cost ineffective, in view of the use of enzymes. Therefore, direct fermentation with integrated steps that incorporating recombinant or co-culture strains in a single reactor offers a more convenient method for the production of ethanol and its high value by products. By co-culture fermentation, high starch concentration can be used to reduce water usage in fermentation and subsequently in ethanol-water separation system. Furthermore, the fermentation medium can be prepared at lower temperature or raw starch can be used for direct fermentation to reduce the energy consumption. From the safety, economic and production process aspects, single-step bioconversion using co-culture microorganisms is a better alternative as far as production of ethanol and its by products from starch is concerned. The ethanol by-products such as lactic acid and glycerol can be value added co-products to generate extra revenue.

1.1.1 Genome shuffling of Saccharomyces cerevisae for multiple-stress resistant yeast to produce bioethanol

In the fermentation process, sugars are transformed into ethanol by addition of microoganism. Ethanol production from sugars has been commercially dominated by the yeast S. cereviseae (Tanaka, 2006). Practically, yeast cells are often exposed in multiple stress environments. Therefore, it is helpful to fermentation efficiency and economic benefits to breed the yeast strains with tolerance against the multiple-stress such as temperature, ethanol, osmotic pressure, and so on (Cakar et al., 2005). Yeast strain improvement strategies are numerous and often complementary to each other, a summary of the main technologies is shown in Table 2. The choice among them is based on three factors: (1) the genetic nature of traits (monogenic or polygenic), (2) the knowledge of the genes involved (rational or blind approaches) (3) the aim of the genetic manipulation (Giudici et al., 2005; Gasch et al., 2000 ).

It is difficult to improve the multi-tolerance of the yeast by rational genetic engineering technology before its mechanism completely clarified. Nevertheless, for quantitative traits, the number of responsible genes QTLs is so great that a "gene-by-gene" engineering strategy is impossible to perform. In these cases, blind strategies, such as genome shuffling (Zhang et al., 2002), could be applied in order to obtain quickly strains with recombinant traits. Genome shuffling is an accelerated evolutionary approach that, on the base of the recursive multiparental protoplast fusion, permits obtaining the desired complex phenotype more rapidly than the normal breeding methods (Figure 6). Genome shuffling technology can bring a rapidly improvement of breeding a hybrid with whole-genome random reorganization. After the initial strains in various long term evolution experiments (Figure 7), we successfully applied the genome shuffling technology that combines the advantage of multi — parental recursive fusion with the recombination of entire genomes normally associated with conventional mutant breeding to selecting the multiple-stress resistant yeast (Figure 8).

1.3 Steam explosion pretreatment

1.3.1 Operation of steam explosion reactor

The bulk density of the straw in the steam explosion reactor depends very much on the condition of the straw and the feeding method. When filling the pilot reactor with chopped straw manually, a bulk density of about 60 kg m-3 was achieved. Loading baled straw would lead to a bulk density of approximately 150 kg m-3 (bulk density of straw bales according Jenkins (1989): 100 — 200 kg m-3). The bulk density of straw pellets is 500 kg m-3 and higher (Theerarattananoon et al., 2011). For reliable discharge of the treated straw from the reactor in the explosion step, addition of water to the dry straw is usually required. The thermal energy requirement of the steam explosion treatment is met by steam directly fed into the reactor. In small steam explosion units, steam is also optionally used for jacket heating of the reactor. In adiabatic operation, the thermal energy is required for heating up the biomass and the added water. The steam in the vapour phase of the reactor is lost through a vent during the sudden pressure discharge of the reactor. The steam required for heating up the biomass and the added water mst,1 (in kg) can be calculated thus:

mst,1 = mS — cp, S ■ Щ + m-Cp, W ■ ATW +AhR |•

The mass of straw ms and the mass of the added water mW are in kg. The specific heat capacity of straw cp, S and the specific heat capacity of water cp, W are in kJ kg-1 K-1. The temperature difference between pretreatment temperature and feed temperature for straw ATS and water ATW are in K. The enthalpy of vaporization for water AhV at pretreatment temperature and the net reaction enthalpy AhR of the pretreatment process are in kJ kg-1.

The venting loss of steam mst,2 (in kg) can be calculated thus:

The bulk density of the straw in the reactor pS, b as well as the density of straw pS, the density of water pW and the density of steam pst, all at operation temperature and pressure, are in kg m-3. The factor for the volumetric use of reactor volume is r|V.

An increase in steam consumption of 10% can be estimated because of non-adiabatic operation of the steam explosion system and steam leakages (Sassner et al., 2008). The total steam consumption is therefore calculated thus:

mst = 1.1 . (mst,1 + mst,2) (4)

A reduction in the cost of pretreatment can be achieved by minimisation of the specific steam demand. Ahn et al. (2009) determined the specific heat capacity of wheat straw with a water content of 4.3 g water/g dry sample to be 1.63+0.07 kJ kg-1 K-1. The specific heat capacities of other types of straw were in the same range. The specific heat capacity of water is about 2.5 times higher than the specific heat capacity of straw. Therefore, the total water content of the input material is a main influencing factor on the thermal energy consumption of steam explosion pretreatment. Minimizing the rate of water addition to the straw is a way to reduce the steam consumption. Preheating of the added water using waste heat e. g. from the condenser of the distillation or increasing the bulk density of the straw in the reactor are also ways to reduce the steam consumption (Fig. 3).

A reduction in steam temperature would reduce the steam demand too, but at the same time reduce the effect of steam explosion treatment.

For the discharge of the treated straw from the reactor in the explosion step a certain fraction of the reactor volume has to remain filled with uncondensed steam. The remaining steam — filled fraction of the reactor volume under various operation conditions is shown in Fig. 4.

The steam explosion pretreatment of straw pellets is restricted by the pore volume available for the addition of water and condensing steam. From this point of view, a type of compacted straw with a density between 150 kg m-3 and 500 kg m-3 would be preferable.

The composition of the raw and the pre-treated straw is measured by strong acid hydrolysis of the carbohydrates. Dried and milled samples (160 mg) are treated with 72 % (w/w) H2SO4 (1.5 mL) at 30°C for 1 hour. The solutions were diluted with 42 mL of water and autoclaved at 121°C for 1 hour. After hydrolysate filtration, the Klason lignin content is determined as the weight of the filter cake subtracted the ash content. The filtrate is analysed for sugars on HPLC. The recovery of D-glucose, D-xylose and L-arabinose is determined by standard addition of sugars to samples before autoclavation. The sugars are determined after separation on a HPLC-system (Shimadzu) with a Rezex ROA column (Phenomenex) at 63°C using 4 mmol/L H2SO4 as eluent and a flow rate of 0.6 mL/min. Detection is done by a refractive index detector (Shimadzu Corp., Kyoto, Japan). Conversion factors for dehydration on polymerization are 162/180 for glucose and 132/150 for xylose and arabinose (Kaar et al., 1991; Thygesen et al., 2005).

Compared to H2O and CO2, O2 is much active in partially oxidizing ethanol for hydrogen production by following a representative Reaction (4) which is a slightly endothermic reaction, indicating that much less external energy is needed for reaction proceeding.

C2HsOH(l) + 0.5 O2 3 H2 + 2 CO (AHr,298K = 56 kJ/mol) (4)

As a result, the ethanol partial oxidation can take place at much lower temperature (200 ~300 oC) in the presence of catalyst than those required for steam or dry reforming (typically 450 ~650 oC). Depending on the reaction conditions and catalyst used, in addition to CO, various ethanol oxidation products with different oxidation states have been observed including acetaldehyde, acetone, acetic acid, and CO2. Plenty of catalyst systems have been extensively studied for catalyzing ethanol oxidation at low temperature. Among them, Ni — Fe alloy [29] from transition metal group and Pt from noble metal group based catalyst [30] have drawn special attentions. According to literature reporting, 51% ethanol conversion and 97% hydrogen selectivity has been successfully achieved at temperature as low as 370 K over Pt/ZrO2 [31]. Although O2 usage significantly improves the ethanol reactivity and lowers down the energy input, it reduces the hydrogen production by half, referring to Reaction (1). Moreover, the likelihood of hot-spot formation makes the control of this reaction difficult.

Cassava plants photosynthesize and store solar energy in a form of carbohydrate, mainly as starch in edible, underground roots. The roots are very moist having the water content around 59-79% w/w (Table 4). On dry solid basis, starch is a major component of cassava roots, accounting upto 77-94% w/w, the rests are protein (1.7-3.8% w/w), lipid (0.2-1.4% w/w), fiber (1.5-3.7% w/w as crude fiber, i. e. cellulose and lignin) and ash (1.8-2.5% w/w) (Table 4). Some sugars, i. e. sucrose, glucose and fructose are also found in storage roots at 4-8% w/w (dry basis). In addition to cellulosic fiber, the roots also contain non-starch

n. a. = not available

Source: Food and Agriculture Organization of the United Nations [FAO], 2011

Table 2. World average root productivity (tons/ hectare) and those of major producers.

	oo
Thailand Indonesia1 India2 Vietnam3 China Cassava area (hectare/ fanner) 2-3 0.3-1.0 0.5-1.0 0.2-0.9 0.2-0.4 Intercrops None (95%), Maize (5%) Maize+rice — soybean/ peanut None/vegetables None/maize None/peanut Land preparation Tractor (3 + 7 disc) Manual / animal / tracto r Tractor Animal/tractor Manual/animal Fertilizer use — Organic (ton/hectare) -Inorganic (kgN+P20+K20/hectare) Little 30-120 Low, 3-10 Medium, N-only 10-20 High 0-5 0-60 3-5 NPK Planting time March-May (70%) Sept-Nov Oct-Dec (90%) Jan-Mar (90%) Sept-Oct Feb-May (80%) Oct-Nov Feb-Apr (90%) Harvest time Dec-May Aug-Dec Jul-Sept Oct-Jan Feb-Mar Sept-Oct Nov-Jan Planting space (m) 0.8×1.2,0.8×0.8 l. OxO.8,2.0×0.5 l. Oxl. O 1.2×0.8,0.8×0.8 l. Oxl. O, 0.8×0.8 Planting method Vertical Vertical Vertical Horizontal Horizontal Weed control Hoe 2-3x small tractor/ Paraquat Hoe l-2x Hoe 4-5x Hoe 2-3x Hoe 2-3x Harvest method Hand/tractor Hand Hand Hand Hand Main varieties KU 50, Rayong 90, Rayong 60, Rayong 5 Adira 4, local varieties H-226, H-165, local varieties KM 94, KM 60,34, HL 23 SC 205, SC 201, SC 124 Labor use (m-days/hectare) 50-60 150-300 200-350 100-200 90-180 Yield (tons/ hectare) 23.40 20 404 25 20 Production cost -Variable costs (USD/hectare) (Labor cost) (Other costs: Fertilizers, chemicals, cuttings, transportation) 365.91 (167.18) (198.73) 265.92 (185.37) (80.55) 663.85 (421.70) (242.15) 384.67 (213.60) (171.07) 427.62 (167.40) (260.22) — Fixed costs (USD/hectare) 48.89 46.67 236.50 60.00 94.94 — Total production costs USD/hectare 414.80 312.59 900.35 444.67 520.56 USD/ ton fresh roots 17.73 27.335 15.63 22.51 17.79 26.03

Table 4. Chemical composition of starch-accumulating edible parts of various starch crops.

polysaccharides, i. e. hemicellulose and pectic substances as evidenced by a presence of monosaccharide including rhamnose, fucose, arabinose, xylose, mannose, galactose, glucose in hydrolyzed cell wall materials (Kajiwara & Maeda, 1983; Menoli & Beleia, 2006; Charles et al., 2008). Some minerals such as sodium, calcium, potassium, magnesium, iron, copper, zinc, manganese and phosphorus are detected in fresh roots as well (Balagopalan et al., 1988; Rojanaridpiched, 1989; Charles et al., 2005).

Unlike grains of cereals having low moisture content (11-15%), cassava roots contain very high moisture contents and are very perishable. This is a constraint for cassava utilization as roots are subjected to deterioration and spoilage by microorganism attacks during storage. Fresh roots can be stored only a few days and should be transformed to products as soon as they are harvested. To prolong their shelf-life, the roots can be simply chopped and sun — dried; the final product is named as cassava chip with the moisture content approximately 14% (Table 4). Cassava roots also contain much lower protein contents than cereals.

The starch content of mature roots can range significantly, depending on genetic traits and environmental factors during plant development, as well as harvest time or ages after planting. Roots collected from crops being planted with the drought during initial state of growth have much lower starch contents and root yields than those from crops without the drought (Pardales and Esquibel, 1996; Santisopasri et al., 2001; Sriroth et al., 2001). Immature or young roots (less than 8 months) provide low starch yields due to low starch contents and root yields. The genetic and environmental growth condition can also influence starch qualities in term of starch composition (amylose and amylopectin content), ease of cooking as indicated by gelatinization or pasting temperature and cooked paste viscosity (Moorthy and Ramanujam, 1986; Asaoka et al., 1991;1992; Defloor et al., 1998; Sriroth et al., 1999; Santisopasri et al., 2001).

Very High Gravity (VHG) mashes are used for fuel ethanol production at industrial scale. Among the benefits include an increased productivity, a reduced capital cost, a higher ethanol concentration in the fermented mash (from 7-10% to 15-18% — v/ v — or more), and a decrease in water requirements. The most concentrated ethanol in fermented mashes also reduces distillation requirements, being an important issue because after feedstock, energy is the biggest production input, representing 30% of total ethanol cost (Pradeep et al., 2010; Wang et al., 2007). This economic consideration indicates the importance of the substrate concentration at the beginning of the process. The use of mashes with higher sugar concentration influences the decision of which fermentation microorganism will be selected and used.

Yeast osmotolerance is determined by genetics and by the carbohydrate level present in mashes, fermentation temperature, osmotic pressure/water activity and substrate concentration. Osmotolerant yeast fermenting in batch conditions can produce and tolerate levels of 16 to 17% (v/ v) alcohol (Casey & Ingledew, 1986). According to the same authors, higher alcohol beers can be produced if oxygenation and nitrogen sources are supplemented to worts. Predeep et al. (2010) reported a maximum ethanol concentration of 15.6% (v/v) converted with about 86.6% efficiency when finger millet mashes were fermented with Saccharomyces bayanus. Fermentation temperature is also an important factor affecting productivity, and generally speaking, at higher temperatures the time required to finish fermentation is decreased. Jones & Ingledew (1994) reported an increment in fermentation efficiency when dissolved solids concentration increased from 14 to 36.5 g/100 mL and also observed that the use of urea accelerated the rate of reaction and decreased time required to complete fermentation.

Working with VHG sweet sorghum juice rather than with ground sorghum grain, Wu et al. (2010b) reported an increase in glycerol (0.3 to 0.6%) and residual sugars (0.2 to 5.1%) when sugar in juices increased from 20 to 30%. A reduction in fermentation efficiency (93 to 72%) was also observed after 72 hours fermentation. Authors recommend the use of juices with no more than 20% soluble sugars in order to obtain the highest efficiency.

In general terms, yeasts can exhibit osmotic inhibition starting at 15% sugar, and this inhibition is higher in glucose followed by other carbohydrates such as sucrose and maltose. Sumari et al. (2010) stated that very few types of yeasts were known to tolerate sugar concentration above 40% and normally at this concentration their growth is sluggish. For this reason, the screening for osmotolerance and the development of new strains is necessary for industrial purposes. Sumari et al. (2010), using a molecular genetic approach, characterized a set of yeasts isolated from African brews and wines. One strain was able to ferment a medium with sucrose concentration of 1000 g/L. The phylogenetic analysis with rDNA clustered this microorganism away from the typical osmotolerant yeast. This indicates the opportunity to explore and look for new strains in nature.

Besides yeast, other microorganisms, as bacteria, are especially designed for ethanol fermentation. Escherichia coli is the typical modified microorganism for ethanol production because of the wide spectrum of metabolized carbohydrates, its well-known genetic makeup and the easiness of manipulation. Zymomonas mobilis, a rod shaped, gram negative, non­spore forming bacteria is naturally ethanologenic and compared to yeast, has higher rates of glucose uptake. Z. mobilis has also a higher ethanol production, increased yield and tolerance, making it a good option to use in VHG fermentation. Kesava et al. (1995), working with Z. mobilis, reported 95% conversion rates after 35 hours fermentation and ethanol yields of approximately 70 g/L when fermenting mashes containing 150 g glucose/L. The bacterium was able to ferment mashes containing 200 g glucose/L in a step-fed system. Perez-Carrillo et al. (2011) observed that Z. mobilis had lower nitrogen requirements compared to S. cerevisiae when fermenting mashes adjusted to 20° Plato. This bacterium has potential and possible advantages for commercial use in biorefineries.

1.1 Cellulolytic capability of organisms: Difference in the cellulose-degrading strategy

Different strategies for the cellulose degradation are used by the cellulase-producing microorganisms: aerobic bacteria and fungi secrete soluble extracellular enzymes known as non complexed cellulase system; anaerobic cellulolytic microorganisms produce complexed cellulase systems, called cellulosomes (Sun et al., 2002). A third strategy was proposed to explain the cellulose-degrading action of two recently discovered bacteria: the aerobic Cytophaga hutchinsonii and the anaerobic Fibrobacter succinogenes (Ilmen et al., 1997).

• Non-complexed cellulase system. One of the most fully investigated non-complexed cellulase system is the Trichoderma reesei model. T. reesei (teleomorph Hypocrea jecorina) is a saprobic fungus, known as an efficient producer of extracellular enzymes (Bayer et al., 1998). Its non-complexed cellulase system includes two cellobiohydrolases, at least seven endoglucanases, and several P-glucosidases. However, in T. reesei cellulases, the amount of fi-glucosidase is lower than that needed for the efficient hydrolysis of cellulose into glucose. As a result, the major product of hydrolysis is cellobiose. This is a dimer of glucose with strong inhibition toward endo — and exoglucanases so that the accumulation of cellobiose significantly slows down the hydrolysis process (Gilkes et al., 1991). By adding fi-glucosidase to cellulases from either external sources, or by using co-culture systems, the inhibitory effect of cellobiose can be significantly reduced (Ting et al., 2009).

It has been observed that the mechanism of cellulose enzymatic hydrolysis by T. reesei involves three simultaneous processes (Ting et al., 2009):

1. Chemical and physical changes in the cellulose solid phase. The chemical stage includes changes in the degree of polymerization, while the physical changes regard all the modifications in the accessible surface area. The enzymes specific function involved in this step is the endoglucanase.

2. Primary hydrolysis. This process is slow and involves the release of soluble intermediates from the cellulose surface. The activity involved in this step is the cellobiohydrolase.

3. Secondary hydrolysis. This process involves the further hydrolysis of the soluble fractions to lower molecular weight intermediates, and ultimately to glucose. This step is much faster than the primary hydrolysis and ft-glucosidases play a role for the secondary hydrolysis.

• Complexed cellulase system. Cellulosomes are produced mainly by anaerobic bacteria, but their presence have also been described in a few anaerobic fungi from species such as Neocallimastix, Piromyces, and Orpinomyces (Tatsumi et al., 2006; Watanabe & Tokuda, 2010). In the domain Bacteria, organisms possessing cellulosomes are only found in the phylum Firmicutes, class Clostridia, order Clostridiales and in the Lachnospiraceae and Clostridiaceae families. In this latter family, bacteria with cellulosomes are found in various clusters of the genus Clostridium (McCarter & Whiters, 1994; Wilson, 2008). Cellulosomes are protuberances produced on the cell wall of the cellulolytic bacteria grown on cellulosic materials. These protuberances are stable enzyme complexes tightly bound to the bacteria cell wall but flexible enough to bind strongly to cellulose (Lentig & Warmoeskerken, 2001). A cellulosome contains two types of subunits: non-catalytic subunits, called scaffoldins, and enzymatic subunits. The scaffoldin is a functional unit of cellusome, which contain multiple copies of cohesins that interact selectively with domains of the enzymatic subunits, CBD (cellulose binding domains) and CBM (carbohydrates binding modules). These have complementary cohesins, called dockerins, which are specific for each bacterial species (Fig. 4) (Gilligan & Reese, 1954; Lynd et al., 2002; Arai et al., 2006;).

For the bacterial cell, the biosynthesis of a cellulosome enables a specific adhesion to the substrate of interest without competition with other microorganisms. The cellulosome allows several advantages: (1) synergism of the cellulases; (2) absence of unspecific adsorption (McCarter & Whiters, 1994; Zhang & Lynd, 2004). Thanks to its intrinsic Lego-like architecture, cellulosomes may provide great potential in the biofuel industry.

The concept of cellulosome was firstly discovered in the thermophilic cellulolytic and anaerobic bacterium, Clostridium thermocellum (Wyman, 1996). It consists of a large number of proteins, including several cellulases and hemicellulases. Other enzymes that can be included in the cellulosome are lichenases.

• Third cellulose-degrading strategy. The third strategy was recently proposed to explain the

cellulose-degrading behavior of two recently sequenced bacteria: Cytophaga hutchinsonii and Fibrobacter succinogenes (Ilmen, 1997). C. hutchinsonii is an abundant aerobic cellulolytic soil bacterium (Fagerstam & Petterson, 1984), while F. succinogenes is an anaerobic rumen bacterium which was isolated by the Rockville, (Maryland), and San

Diego (California) Institute of Genomic Research (TIGR) (Mansfield et al., 1998). In the aerobic C. hutchinsonii no genes were found to code for CBM and in the anaerobic F. succinogenes no genes were identified to encode dockerin and scaffoldin. Thus, a third cellulose degrading mechanism was proposed. It includes the binding of individual cellulose molecules by outer membrane proteins of the microrganisms followed by the transport into the periplasmic space where they are degraded by endoglucanases (Ilmen, 1997).