Contrary to rational engineering, partial and/or addi­tional metabolic pathways of microorganisms can be engineered to enhance bioproduct production. The term "metabolic engineering" was first coined by Bailey and was described as a vast variety of manipulations and experimental procedures to improve the productiv­ity of a desired metabolite by an organism (Bailey, 1991). More specifically, examples of metabolic engineering can include increased productivity and/or yield, improvement of substrate uptake, widening the scope of substrate range for an organism, modification of metabolic flux, and elimination of unnecessary or competing metabolic pathways (Stephanopoulos, 1999).

Metabolic engineering, similar to rational engineer­ing, requires the selection of a good host/microor — ganism as a candidate for the production of biofuels and/or bioproducts from biomass. This could include engineering desired pathways into well-studied host microorganisms such as Escherichia coli and Saccharo — myces cerevisiae; these microorganisms have been used for industrial-scale production for several years. How­ever, some experts suggest that engineering desired pathways into microorganisms that already possess industrial properties may be more successful. This is due to the potential for metabolic burden to the cell; new metabolic pathways require amino acids, redox cofactors, and energy for synthesis and function of its enzymes (Lee et al., 2008a).

Furthermore, metabolic engineering poses several general challenges for researchers including the devel­opment of recombinant DNA technologies for selected host microorganisms, development of quantitative tools, methods to understand flux modification in complex biological systems, and the development of quantitative techniques to determine changes in fluxes or metabolite concentrations (Cameron and Tong, 1993). A few suc­cessful examples of metabolic engineering to improve general host and select host microorganisms metabolism for the digestion and conversion of biomass are outlined below.

Recently, the development of genome-scale modeling permits the prediction of how new metabolic pathways may impact growth and product production using meta­bolic models. These models result in a more rational approach to metabolic engineering (Patil et al., 2004). Moreover, stoichiometric models can be defined by established equations through the use of metabolic flux analysis (MFA); this is established by measuring ex­change fluxes experimentally (Lee et al., 2008b). For example, the native metabolism of E. coli under different growth conditions (Kayser et al., 2005) and during re­combinant protein production (Ozkan et al., 2005) has been determined using MFA. For efficient application in biofuel and bioproduct production, genome-scale models should be developed with constraints to opti­mize flux in desired pathways, while balancing impor­tant cofactors and energy metabolites (Lee et al., 2008b).

Host microorganisms such as E. coli and S. cerevisae have been improved time and again for the fermentation of sugars to ethanol. In particular, due to the broad range of carbohydrates metabolized by E. coli, it has been a po­tential candidate for the expression of ethanologenic pathways in some studies. For example, a portable cassette called the production of ethanol operon (PET operon) was used to genetically engineer the homoetha — nologenic pathway from Zymomonas mobilis into E. coli, which included the pyruvate decarboxylase and alcohol dehydrogenase B genes. Using the PET system, these genes were integrated into the chromosome of E. coli at the pfl locus. Meanwhile the fumarate reductase (frd) gene was deleted to eliminate succinate production, therefore preventing carbon loss. These metabolic changes resulted in the recombinant strain KO11, which produced ethanol yields as high as 95% in complex me­dium (Jarboe et al., 2007; Ohta et al., 1991). However, host strains such as E. coli may encounter metabolic bur­dens and are often not naturally adapted to the toxicity of end products like ethanol. Thus, there have also been some attempts to metabolically engineer known biomass-converting bacteria or fungal strains.

Typically, bacteria produce more desirable end prod­ucts through facultative and anaerobic digestion, as is the case for bacteria belonging to the class Clostridia. Much of the metabolic engineering in these species fo­cuses on product formation, which may include the elimination of undesirable products such as in the case of an engineering project conducted on Clostridium acetobutylicum—a well-known ethanogenic strain stud­ied often for the production of butanol. In brief, the ace- toacetate decarboxylase gene (adc) was disrupted in the hyperbutanol-producing strain C. acetobutylicum EA 2018 using TargeTron technology (Sigma Aldrich) (Jiang et al., 2009). TargeTron is a group II intron developed for rapid and site-specific gene disruption in prokaryotes. The disruption of adc led to an increase in butanol ratio from 70% to 80.05%, with a simultaneous reduction in acetone of 0.21 g/l (Jiang et al., 2009).

In contrast, one can implement metabolic engineering to improve native metabolism in microorganisms by engineering entirely novel pathways for desired product formation, which is more practically done in hosts able to hydrolyze biomass, such as the example with Clos­tridium cellulolyticum. Recently, Higashide et al. demon­strated the production of isobutanol from crystalline cellulose in C. cellulolyticum (Higashide et al., 2011). In this study, the development of valine biosynthesis pathway required the expression of five genes, alsS, ilvC, ilvD, kivD, and ahdA, to convert pyruvate into iso­butanol. Consequently, only the expression and function of kivD (2-keto-acid decarboxylase) and alsS (alpha — acetolactate synthase) were confirmed; nonetheless modified C. cellulolyticum produced up to 660 mg/l of isobutanol over a 7- to 9-day growth period (Higashide et al., 2011).

These examples of engineering and modeling to improve the metabolic capabilities of strains helped lay the foundation for future development of biomass­converting microorganisms. Combined with the ability to rationally design enzymes with greater stability and/or increased specific activity the modification of microorganisms in industrial production of biofuels and bioproducts looks promising.

There are numerous investigations on the mecha­nisms of electron transfer to the anode by the microbes, while the reports about electron transfer to a biocathode are rather limited (Lovley, 2008). The two electron trans­fer directions are opposite. The biocathode is an electron donor while the anode is an electron acceptor. Despite this difference, biocathodes use the same electron trans­fer mechanisms, DET and MET (Rosenbaum et al., 2011), because biofilm electron transfer can be bidirectional.

DET for Biocathodes

Similar to DET for anodes, DET for biocathodes also requires physical contact of the microbial cell wall with the electrode surface. At the site of direct contact, the electrons directly transfer to the outer cell membrane-bound redox macromolecules (such as c-type cytochromes) from the electrode (Figure 9.4(a); Huang et al., 2011c). However, this kind of DET can only utilize a monolayer of sessile cells on the cathode, thus limiting the biocathode performance. With an in­crease in biofilm thickness, the power generation decreased due to mass transfer resistance to oxidant diffusion from the bulk fluid to the cathode surface (Behera et al., 2010). Geobacter species and mixed cultures that use nitrate, fumarate, tetrachloroethene, O2, CO2, U(VI)/U(IV), and so on as an electron acceptor generally transfer the electrons via DET (Table 9.2). On biocathodes, most of the microbes are found to be Gram negative although some Gram-positive microbes exhibit the DET mechanism in cyclic voltammetry (Huang et al., 2011c). Compared with the pure culture

systems, the mixed culture biocathodes also can transfer electrons via DET. When nitrate, carbon dioxide or tri — chloroethene is used as the electron acceptor, the DET in the mixed culture biocathode improves the power generation (Aulenta et al., 2010; Cao et al., 2009; Clau — waert et al., 2007a).

Microalgae are a heterogeneous group of organisms consisting of both prokaryotes such as cyanobacteria and eukaryotes such as diatoms (Bacillariophyta), dinoflagelates (Dinophyta), green algae (Chlorophyta), yellow-green algae (Xanthophyta), and red algae (Rhodophyta) (Brennan and Owende, 2010; Hu et al.,

2008) . Similarly, other oleaginous microorganisms are defined as microorganisms with lipid content in excess of 20%. The number of bacteria that produce lipids that could be used for biodiesel production is very small. As a result, bacteria are mainly used for special lipid produc­tion such as Docosahexaenoic acid (DHA). Many yeasts and fungi also produce high quantities of lipid. Yeasts with high lipid content include Candida curvata (58%), Cryptococcus albidus (65%), Lipomyces strakeyi (64%) and Rhodotorula glutinous (72%). Oleaginous fungi include Aspergillus oryzae (57%), Mortierella isabellina (86%), Humi — cola lanuginose (75%) and Mortierella vinacea (66%) (Meng et al., 2009). In terms of microalgae, species are generally unicellular organisms but there are also a number of sim­ple multicellular organisms that occur as colonial or fila­mentous groups of cells. Microalgae are capable of autotrophic, heterotrophic and mixotrophic growth. Microalgae populate a wide variety of ecological niches due to a wide range of tolerance for various growth con­ditions such as availability of nutrients, salinity, pH and temperature (Brennan and Owende, 2010; Gong and Jiang, 2011; Schenk et al., 2008). Currently, microalgae contribute very little biolipid to the overall bioenergy market as full-scale commercialization has yet to be realized. Despite this fact, microalgae remain the feed­stock with the greatest potential for supplying future demand for bioenergy in the form of liquid fuels. The idea of using microalgae as a source of biolipids for bio­fuel is not a new one, however. For example, the Aquatic Species Program was launched in 1978 by what is now known as the National Renewable Energy Laboratory (NREL) with its main focus being, "the production of bio­diesel from high lipid-content algae grown in ponds, uti­lising waste CO2from coal fired power plants" (Sheehan et al., 1998). Over 3000 microalgae strains were initially collected, 300 of which were eventually identified as oil rich. When the program was officially closed in 1998 the conclusions were that no "fundamental engineering and economic issues" were identified that would hamper the feasibility of large-scale microalgae culture. The au­thors noted, however, that total biomass and algal lipids produced were still below "theoretical potential, and the requirements for economic viability" (Sheehan et al., 1998). The economic viability was, of course, based on a time when oil prices in the United States were among their all-time lowest at less than $20 per barrel (adjusted for inflation). Today the average oil price is approxi­mately $100 per barrel and this, along with increased pressure to reduce GHG emissions as well as significant technical advances, has made microalgae-derived bio­fuels even more relevant to meet current bioenergy demands.

Gasification

Gasification of biomass is to convert it into useful gases such as carbon monoxide, hydrogen and light hydrocarbons (Brown, 2003). Since the mid-1980s, inter­est has grown on the subject of catalysis for biomass gasification. The advances in this area have been driven by the need for producing tar-free gases from biomass. The avoidance of tars and the yield of hydorgen are deciding factors the economic viability of the biomass

gasification process. Major reactions in gasification are as follows (Brown, 2003).

1

C + — O2 4 CO

2 2

C + CO2 4 2CO C + H2O 4 H2 + CO C + 2H2 4 CH4 CO + H2O 4 H2 + CO2 CO + 3H2 4 CH4 + H2O

The desired product from gasification of biomass is hydrogen or syngas. Syngas can be burned directly in gas engines, be used to produce methanol, or be con­verted into synthetic fuels via the Fischer—Tropsch process. Though gases are target products, gasification of biomass leaves behind solid residuals such carbon and inorganic compounds (ash).

Gasification of biomass is normally performed in the presence of steam and the process depends on the occur­rence of the steam-reforming reactions. Water, in the form of steam, is often added to promote additional produc­tion of hydrogen via the water—gas shift reaction. As the biomass is heated, moisture contained in the biomass is converted to steam, which can react with biomass. However, in practice, proper drying of biomass before feeding it into gasification equipment is still needed in view of energy-input.

Small amounts of oxygen can also be added to the gas feed. The heat from exothermic oxidation reactions can then be used by the endothermic steam-reforming reac­tion. In addition, oxygen has a function to delay the cata­lyst deactivation by helping burn off some of the coke formed.

Catalytic Gasification

Table 15.2 lists some typical results from the gasifica­tion of lignocellulosic biomass in the presence of catalysts.

Biomass gasification is inevitably accompanied with tar formation. Nevertheless, tar can be effectively minimized by catalytic cracking. Naturally occurring dolomite (CaMg(CO3)2), for example, has been used as a catalyst for gasification of biomass in a fluid bed reactor to reduce the tar content by transforming it to gases (Delgado and Aznar, 1997). The mineral-based catalyst generally con­tains CaO, MgO, CO2 and trace minerals such as SiO2, Fe2O3 and Al2O3. The tar cracking efficiency over the dolomites depends on their chemical composition. In general, dolomites with the lowest content of CaO and MgO show the lowest tar cracking efficiency. Yu et al. gasified birch on the four types of dolomites (deposites in Zhenjiang, Nanjing, Shanxi, and Anhui, China) and a Swedish dolomite (Sala) (Yu et al., 2009). The result was that Anhui dolomite showed a low catalytic capacity to crack tar at 973 and 1073K due to its lowest content of CaO and MgO among the tested dolomites. An alterna­tive can be naturally occurring particles of olivine, which are a mineral containing magnesium oxide, iron oxide and silica. Regarding their attrition resistance, Olivine is advantageous over dolomite (Devi et al., 2005).

Alkali salts are often added to biomass by dry mixing or wet impregnation and used as catalysts for the elim­ination of tar and upgrading of the product gas (Li et al., 1996; Encinar et al., 1998). But it has considerable difficulty in catalyst recovery and disposal of ash. Carbonates, oxides and hydroxides of alkali metals can effectively cata­lyze the decomposition of tar during catalytic gasification (McKee, 1983). Earlier, for example, Mudge et al. investi­gated the catalytic steam gasification of wood using alkali carbonates and naturally occurring minerals (trona, borax), which were either impregnated or mixed with the biomass (Mudge and Baker, 1985). The order of activity reported was potassium > carbonate > sodium carbonate > trona > borax.

The Ni-based catalysts for biomass gasification in a fluid bed reactor are typically Ni-Al based one (Garcia et al., 2002; Arauzo et al., 1997) and Ni/olivine one (Courson et al., 2002, 2000). Ni catalysts help to remove tars and methane and to adjust the composition of syn­thesis gas. Sinag et al. studied the effect of nano-sized and bulky ZnO and SnO2 at 573 K on the water-gas shift reaction in gasification of cellulose. The results showed that the water-gas shift reaction proceeded faster over ZnO catalysts than that over SnO2 catalysts. Therefore, a higher yield of hydrogen was obtained in the presence of ZnO (Sinag et al., 2011).

However, catalysts often suffer from deactivation by sintering and/or coke deposition. The use of supercriti­cal water can prevent catalyst from deactivation by means of extracting the coke precursor from the catalyst surface (Baiker, 1999). In addition, it can improve solubi­lity of cellulosic materials and thus reduce mass-transfer limitation. It is also worth noting that, in addition to the active component in a catalyst, usually the acidity and basicity of a support is also an influential factor on product distribution and coke formation. Tasaka and coworkers disclosed that steam reforming of tar derived from cellulose gasification was efficiently catalyzed by 12 wt% Co/MgO catalyst at 873 K in a fluidized bed reactor (Tasaka et al., 2007).

Supported Ru, Pt or Pd catalysts also appear prom­ising in the catalytic gasification of lignocellulosic biomass. They were able to overcome the shortcomings of Ni-based catalysts and dolomite catalysts, although they are relatively costly. Usui et al. gasified cellulose in hot-compressed water at 623 K in the presence of a series of supported catalysts such as Zr(OH)4, (CH3COCH=C(O-)CH3)3Fe, ferrocene, Ru3(CO)12,

(CH3COCH=C(O-)CH3)2Co, NiC2O4, NiO, Ni(OH)2, PdI2 and Cu(OH)2. After reaction for 3 h, 5 wt% Pd sup­ported on Al2O3 showed the highest catalytic activity, leading to a 42.3 vol% yield of H2 and a 7.7 vol% yield of CH4 (Usui et al., 2000). Tomishige et al. found that the order of M/CeO2/SiO2 catalyst activity in the cedar wood gasification at 823 K was the following: Rh > Pd > Pt > Ni=Ru (Tomishige et al., 2004). For Rh/ CeO2/M-type (M=SiO2, Al2O3, and ZrO2) catalysts for cellulose gasification in a continuous-feeding fluidized — bed reactor, Asadullah et al. found that Rh/CeO2/SiO2 exhibited the best performance in terms of generating syngas or hydrogen (Asadullah et al., 2001, 2003).

HMF stands out among the platform chemicals for a number of reasons: It has retained all six-carbon atoms that were present in the hexoses and high selectivities have been reported for its preparation, in particular from fructose, which compares favorably with other platform chemicals, such as levulinic acid or bioethanol. HMF is formed through the acid-catalyzed dehydration of a hexose, as described in Scheme 17.2. Initially the synthesis of HMF from hexoses was performed in aqueous systems, catalyzed by homogeneous acids.

СбНіг06

Hexose

HMF

SCHEME 17.2 The acid-catalyzed dehydration of hexose into HMF.

SCHEME 17.3 The dehydration of glucose and fructose through acyclic intermediates.

A number of mechanistic pathways have been pro­posed for this reaction, which can generally be divided into two groups. The first group is based on a pathway through acyclic intermediates and the second group is based on a pathway through cyclic intermediates.

Although there are differences between the various acyclic pathways proposed for the aqueous dehydration of hexoses, they generally propose the formation of the 1,2-enediol intermediate in the Lobry De Bruijn — Alberda Van Ekenstein transformation (Speck Jr, 1958) between fructose and glucose as the key intermediate (Anet, 1964; Feather and Harris, 1973; Kuster, 1990; Newt, 1951). This intermediate is proposed to dehydrate to a 3-deoxyglucosone, followed by further dehydration and ring closure to form HMF. A schematic representa­tion is provided in Scheme 17.3.

The proposed aqueous hexose dehydration pathways through cyclic intermediates generally assume dehydra­tion to start at the C2 hydroxyl position of fructose (Scheme 17.4), leading to the formation of a tertiary car — bocation (Van Putten et al., 2013a; Feather and Harris, 1973; Newt, 1951). This is then followed by consecutive dehydrations at C3 and C4 to form HMF. It is clear that in this proposed mechanism, glucose dehydration requires glucose to first isomerize to fructose before it can dehydrate to HMF. Under the acidic reaction condi­tions, however, this is unfavorable as the isomerization is base catalyzed.

The HMF yields and selectivities from the dehydra­tion of fructose, a ketose, are generally much higher than those obtained from the dehydration of glucose, which is an aldose (Van Putten et al., 2013a). The HMF

yields for homogeneous acid-catalyzed fructose dehy­dration in water are limited to around 60% at full conver­sion, whereas for glucose this is only around 10% at full conversion. Fructose is known to be significantly less sta­ble than glucose, which shows in the required reaction conditions for dehydration. Fructose dehydrates to HMF at temperatures around 100 °C in the presence of acid, whereas glucose requires much more severe condi­tions of at least 140 °C in the presence of catalyst to form only small amounts of HMF (less than 10% yield). Quite large variations are seen in the reaction conditions applied by different groups. In some cases relatively high catalyst concentrations in the order of 0.1—1 M min­eral acid are applied in fructose dehydration at relatively low temperatures between 100 and 150 °C with reaction times in the order of minutes. Others applied lower acid concentrations, but at either longer reaction times or higher temperatures (Van Putten et al., 2013a). Also a significant amount of work has been done with hetero­geneous acid catalysts, like ion exchange resins and zeo­lites, showing comparable selectivities and yields to the homogeneous catalysts (Van Putten et al., 2013a).

The HMF yield is limited by its inherent instability under aqueous acidic conditions. In the presence of acid HMF reacts with water (so-called HMF hydration reaction) to form levulinic acid and formic acid, as described in Scheme 17.5 (Kuster, 1990). Other undesir­able side reactions are the formation of polymeric mate­rial, often referred to as humins (Kuster, 1990), and retroaldol reactions of sugars (Aida et al., 2007).

In order to minimize side reactions and HMF hydra­tion, biphasic systems have been researched in which the HMF is extracted to the organic phase (Romtin — Leshkov et al., 2006; Cope, 1959; Kuster and van der Steen, 1977; Kuster and Laurens, 1977; Moreau et al., 1996). The major extraction solvents used are methyliso — butylketone, 1-butanol and 2-butanol. The in situ extraction has improved HMF yields from fructose dehydration in some cases to around 70% at full conver­sion. Due to the high solubility of HMF in water rela­tively large amounts of solvent are needed, generally at least two equivalents, in order to extract sufficient amounts of HMF (Van Putten et al., 2013a).

In the early 1980s a number of researchers started per­forming HMF synthesis in organic solvents (Nakamura and Morikawa, 1980; Szmant and Chundury, 1981; Brown et al., 1982). The biggest initial challenge here is that, except from high-boiling coordinating solvents like

dimethyl sulfoxide (DMSO), N, N-dimethylformamide (DMF) and N-methylpyrrolidinone, most organic sol­vents do not dissolve sugars very well. The focus was mainly on solvents like DMSO and DMF, showing significant improvements in yield and selectivity (Nakamura and Morikawa, 1980; Szmant and Chundury, 1981; Brown et al., 1982; Musau and Munavu, 1987). In DMSO reaction temperatures of 100—120 °C are generally applied and the solvent shows catalytic activity as yields over 90% have been reported in the absence of catalyst (Brown et al., 1982; Musau and Munavu, 1987). An important issue here is the known decomposition of DMSO at temperatures over 100 °C.

Since 2003 ILs have been extensively researched as solvents for HMF synthesis by many research groups; however, 20 years before that HMF synthesis in pyridi — nium salts was already performed by Fayet and Gelas, resulting in 70% yield starting from fructose (Fayet and Gelas, 1983). Certain ILs are known to dissolve sugars in high concentrations. The vast majority of this research has been done in imidazolium-based ILs. As is the case for the coordinating organic solvents, the HMF yields for fructose dehydration to HMF in ILs, in which the IL is often also the catalyst, are generally high (70—90%) and levulinic acid formation is in most cases not mentioned (Van Putten et al., 2013a; Zakrzew- ska et al., 2010). In the work on ILs some conflicting re­sults have been published with the same or comparable ILs. As was already mentioned, HMF synthesis from glucose is much more challenging than from fructose. In 2007 Zhang and coworkers published a breakthrough in glucose dehydration to HMF by using CrCl2 as a cata­lyst in an imidazolium type IL (Zhao et al., 2007). They achieved an HMF yield of around 70%, which was essentially equal to the yield obtained from fructose in the same system. It is believed that CrCl2 behaves as an isomerization catalyst that forms fructose, which can be dehydrated readily to HMF.

Earlier research on HMF synthesis focused mainly on fructose and polymers thereof as substrates. Recent years have seen an enormous increase in interest in the development of biobased platform chemicals as a replacement for fossil-oil based feedstock. For this reason it is preferable to use cheap feedstocks that do not compete with food. Many parties have placed their focus on cellulose, a for humans nondigestible polymer of glucose, as a feedstock. Cellulose is present in large amounts in plant waste material. Application in HMF synthesis will require both hydrolysis and dehydration of the cellulose, either in one reactor or in two separate steps. Recent years have shown a dramatic increase in research on HMF synthesis from cellulose. The main focus has been in line with the work on glucose, applying bifunctional catalyst systems, especially chro­mium salts in combination with a Bronsted acid.

Especially in ILs the yields approach those obtained with glucose. The substrate concentration is mostly significantly lower, due to the much lower solubility of cellulose. Also the reaction times are typically much longer for cellulose compared to glucose, likely due to the required hydrolysis prior to dehydration to HMF (Van Putten et al., 2013a).

Although sugar dehydration to furans is a hot topic in academia, a lot of research has yet to be done in upscal­ing these processes to pilot plant and ultimately indus­trial scale. Especially for hexose dehydration to HMF this holds true. Only two pilot-scale processes are known for the production of HMF: a process from Sud — deutsche Zucker-Aktiengesellschaft and a process from Roquette Freres. The first process concerns HMF pro­duction in around 5 kg scale from fructose and inulin, a polymer of mainly fructose, catalyzed by oxalic acid at around 140 ° C in water in which the purification of HMF is done by chromatographic separation (Rapp, 1987). The second process concerns fructose dehydra­tion in a water-MIBK (1:9 v/v) biphasic system in the presence of cationic resins at temperatures between 70 and 95 °C (Fleche et al., 1982). In both processes the fructose concentration in water was 20—25 wt% and the HMF yields are in the range of 40—50%. The workup procedures for HMF mentioned in these patents appear unfavorable for a large-scale plant as large-scale chro­matographic separation is expensive and a very high solvent to water ratio requires a lot of energy for evapo­ration of the solvent from the product.

In order to produce HMF or a derivative thereof in a cost-effective way, some challenges must be overcome. HMF is unstable under the reaction conditions in the presence of water, leading to the formation of levulinic acid, formic acid and polymeric materials. For this reason contact with water should be minimized. This can be achieved by performing the reaction using other solvents or by continuously extracting the HMF from the aqueous phase.

The distribution of HMF over water and extraction solvents is generally not highly favorable toward the sol­vent, demanding large excess of extraction solvents and therefore energy-consuming workup (Romtin-Leshkov and Dumesic, 2009).

Performing HMF synthesis in other solvents than water is an appealing option. Here the choice has to be made between solvents that have lower boiling points, but exhibit low sugar solubility, and solvents that dissolve high concentrations of sugar, like DMSO and ILs, although from which product separation is difficult due to the high affinity of HMF for these solvents. Two processes focus on the production of derivatives of HMF in order to produce the furanic product more effec­tively. Mascal and coworkers have focused their efforts on the production of 5-chloromethylfurfural in a biphasic system of concentrated hydrochloric acid and

1,2- dichloroethane (Mascal and Nikitin, 2008). Avan — tium Chemicals opened their pilot plant in December of 2011 on alcohol-based production of HMF ethers, which will be used for the production of furan-based polymers (Gruter and Dautzenberg, 2007).

The polymer was discovered by (Shima and Sakai, 1977). The polymer has been found to be heat stable, and can even withstand autoclaving for 20 min. These properties led to the use of polylysine as a food preser­vative on a commercial scale in Japan (Yoshida and Nagasawa, 2003). Production of the compound is influ­enced substantially by the pH of the medium. Polylysine has not known to form any secondary or tertiary struc­ture, and its microbicidal activity is attributed to its pol­ycationic nature.

Production of e-Poly-L-Lysine

The strain Streptomyces albulus 346 spp. lysinopolyme — rus was the first organism to be isolated as a polylysine producer, following which many improvements were carried out on the same for industrial production of polylysine. Later more producers from the genus Streptomyces, Kitasatospora, and an ergot fungus epichole were found to produce polylysine. Polylysine is now industrially produced by aerobic fermentation, us­ing a mutant derived from S. albulus 346, isolated from soil. A maximum amount of polylysine, 0.5 g/l was reported under optimized conditions set at pH 6.0 (Shima and Sakai, 1981). A mutant strain resistant to S-(2-aminoethyl)-L-cysteine, an analogue for lysine and glycine were derived and gave higher productivity values of up to 20 mg/l of polylysine, after 120 h, with glucose as the carbon source and ammonium sulfate as the nitrogen source (Hiraki et al., 1998).

Streptomyces albulus 410, the strain that has been exploited for commercial production of polylysine displayed the two-stage polylysine production (pH 6.0 and pH 3.0—5.0). Accurate control of the process and pH led to a maximal production of about 48.3 g/l. It was found that the best pH for increasing the cellular mass was pH 6.0, while pH below 4.2 was favorable for high levels of polylysine production (Shi et al., 2007). Every poly(amino acid) produced is poly dispersed (has variable molecular weight), which in turn makes it difficult to obtain the product of the required specification. This problem was recently tackled to an extent, when new isolates belonging to the genus Streptomyces was obtained, which could pro­duce nearly monodispersed polylysine.

Degradation of Polylysine

There is not much report on the biodegradation of polylysine. The polylysine-resistant strain Chryseobacte — rium sp. OJ7 also was postulated to have a polylysine­degrading enzyme, with an exopeptidase activity (Obst and Steinbuchel, 2004). Polylysine was shown to be susceptible and was degraded by commercially avail­able enzymes proteases A, P and peptidase R from A. oryzae, Aspergillus melleus and Rhizopus oryzae (Kito et al., 2002).

Applications of Polylysine

The application of polylysine as a food preservative has been established. Other uses of polylysine include the production of an emulsifying agent through its conjugation with dextran. Polylysine can be used to coat biochips and surfaces of cell culture flasks, so as to provide a biocompatible adherent surface. One of the most industrially relevant applications of polylysine is its use as a drug delivery agent and an aid in cell trans­formation due to its polycationic nature.

Hydrogen

Frequently cited as the fuel of the future, hydrogen production, storage and utilization are being widely investigated. As a transportation fuel it presents a series of challenges in every link of the chain, from production to storage and distribution. Although having a low volu­metric energy density, hydrogen has the highest energy density per mass and the simple fact that its combustion generates almost only water and heat has seduced entire generations. "Yes, my friends, I believe that water will one day be employed as fuel, that hydrogen and oxygen which constitute it, used singly or together, will furnish an inexhaustible source of heat and light, of an intensity of which coal is not capable" (Verne). Cars that could run on water with minimal energy consumption have captured the imagination of many people and, not sur­prisingly, have inspired frauds like the almost magical conversion of saltwater into fuel using radiofrequency radiation, claimed by John Kanzius and broadcast live countrywide from Philadelphia, or the notorious "Stan — leyMeyer’s water fuel cell" to be used in an internal combustion engine, where a special device could split water giving an energy output sufficient to generate me­chanical energy for the vehicle with enough leftover to power a fuel cell that would provide more hydrogen and oxygen through water splitting. Considering that the combustion of hydrogen and oxygen regenerates water, both systems obviously defy the first and second laws of thermodynamics (Ball, 2007).

Despite the motivation behind these schemes, they touched upon the most limiting step in the development of the hydrogen fuel technology: production. In current industrial practice, hydrogen can be produced by pyrol­ysis, electrolysis or by steam reforming of hydrocarbons. The last is the dominant method, applied to fossil fuels, usually natural gas (methane). This makes hydrogen both expensive and unsustainable.

Hydrogen Bioproduction

Molecular hydrogen (H2) is the lightest gas possible. When released into the atmosphere it diffuses quickly toward the troposphere, thus, at the sea level it can only be found in trace amounts. For this reason, very lit­tle naturally occurring H2 is available and therefore a sustainable production system must be found if this molecule is to be used as a fuel. Efficient biological production of hydrogen could represent a breakthrough in the development of this energy carrier and many different approaches are being followed toward this goal. Undoubtedly, among all the possible fuels that could be produced by cyanobacteria, it is hydrogen that has received the most attention. Here we discuss the biological mechanisms for hydrogen production and advances toward yield improvements in cyanobacteria.

In the light reactions of photosynthesis, light is captured by photosystems I and II, acting together to transform solar energy into chemical energy, splitting water into molecular oxygen and protons (H+) and the reducing agent NADPH. The transmembrane proton gradient that is formed is used by ATP synthase to combine adenosine diphosphate (ADP) + Pi into ATP (Figure 22.1). This set of reactions is rather interesting because it effectively conserves ubiquitous solar energy in energy-dense molecules using an abundant substrate, water. Ironically, cyanobacteria (and all plants) had been all along for millions of years the very sought after solu­tion for breaking the strong bond between oxygen and hydrogen in the water molecule without using the spe­cial radiofrequency of John Kanzius or the mysterious fuel cell of Stanley Meyer.

During the water-splitting process, oxygen is released in its molecular form (O2), while hydrogen, in the form of protons, is further used to produce two molecules of high-energy content: ATP and NADPH. Together, they feed energy into the Calvin-Benson-Bassham cycle, where CO2 is fixed into organic molecules, as well as into many other reactions related to cellular homeostasis or secondary metabolism. Alternatively, before it is used to generate NADPH, the high-energy electron generated by photosynthesis can be directly used for the evolution of hydrogen, a process called direct bio­photolysis (Benemann and Weare, 1974). Therefore, hydrogen evolution through this route does not require CO2 fixation, and solar energy and water, together with the required enzymes, are sufficient for H2 formation (Hallenbeck and Benemann, 2002). The major problem with this process is that hydrogenases, the hydrogen — evolving enzymes, are extremely sensitive to oxygen (O2) and are irreversibly inactivated by even small concentrations of this gas. Thus, hydrogen evolution is usually a short-lived process, with a burst of hydrogen evolution when transitioning from a dark cycle into light as increasing oxygenic photosynthesis quickly inactivates the hydrogenase. Some species, especially filamentous ones (e. g. Anabaena sp. and Nostoc sp.), capable of forming specialized cells called heterocysts, can be shown to produce hydrogen over prolonged periods in light, as the heterocysts provide an oxygen-free environment that protects the hydrogenase against inactivation. In indirect biophotolysis, the captured light energy is used to fix CO2 and the organic molecules that are pro­duced are stored as reserve material. Under normal conditions, part of these carbon reserves will be oxidized over the dark period to maintain cellular ho­meostasis. However, under proper conditions such a culture can be induced to produce hydrogen, thus separating hydrogen evolution temporally and spatially from the oxygen evolved by oxygenic photo­synthesis (Hallenbeck, 2011). Thus, hydrogenase activ­ity is maintained and the simultaneous production of hydrogen and oxygen, an explosive mixture when concentrated in the headspace of a bioreactor, is avoided.

Separate Hydrolysis and Fermentation

SHF uses different stages for enzyme production, cel­lulose hydrolysis and fermentation of glucose. In this process, the hydrolysis of cellulose occurs before the fermentation of glucose, which is carried out in different reactors. In this case, the temperature of hydrolysis and fermentation can be optimized individually (Hamelinck et al., 2005). For this reason, this process is preferably used for ethanol production from lignocellulosic mate­rial, as can be seen in Table 3.3, since the optimal temper­ature for acid or enzymatic hydrolysis is different from that of fermentation.

The main characteristic of SHF is that the technique allows a high number of steps. Thereby the hydrolysis can be carried out with better efficiency than in SSF. Another important aspect to the use of this one is the unnecessity of development or trials of new microor­ganism or enzymes that are able to produce ethanol or sugar at different medium of that when usually fermentation happens (George et al., 2010). Tables 3.2 and 3.3 show that the SHF is the predominant tech­nique used for saccharification of any source of carbohydrates.

Unlike the above general protein family databases, Purdue’s Cell Wall Genomics (Yong et al., 2005) and UCR’s Cell Wall Navigator (Girke et al., 2004) databases are specifically designed for plant cell wall biosynthesis. As opposed to sequence similarity-based classification, both databases categorize proteins based on their physi­ological roles in cell wall synthesis. In UCR’s database, there are five categories: monosaccharide synthesis, polysaccharide synthesis, reassembly, structural proteins and glycoprotein synthesis, basically all centered on car­bohydrate molecules in cell walls. However, Purdue’s database is even broader with six categories: pathways for substrate generation, polysaccharide synthases, secretion and targeting pathways, assembly/architec- ture and growth, differentiation and secondary wall formation and signaling and response pathways. Partic­ularly, Purdue’s database also includes lignin synthesis and polymerization proteins as well as signaling proteins involved in cell wall synthesis. Both databases also pro­vide references and the literature associated with each category to support their classification. Proteins of the two databases are primarily from model organisms such as Arabidopsis and rice. However, it is easy to use the sequences as query to search for their homologs and even orthologs in other plants. The protein accessions and classification complied by the two databases serve as an excellent overview of the current achievement made by the entire plant cell wall community in terms of our latest understanding of cell wall synthesis.

Plant Coexpression Network Databases: PlaNet and ATTED

As we mentioned earlier, regulation of biomass for­mation and degradation is also extremely important. Studying the regulation has been benefited a lot from coexpression analysis of microarray data and recently on high-throughput RNA sequencing data. There are numerous tools, Web based or stand alone, allowing for coexpression analysis with user-submitted genes as query or by general browsing. Many online tools even offer prebuilt coexpression networks, with nodes in the network graphs representing genes and edges repre­senting coexpression relationships. These coexpression networks are very informative and insightful, in terms of suggesting candidate genes involving in the same metabolic pathways or potential regulators of genes of interest to the users (Ruprecht and Persson, 2012). Although there are many such tools available, in plant cell wall field PlaNet (Mutwil et al., 2011) family tools (coreCarb (Mutwil et al., 2009), AraNet, GeneCat) stand out as they were developed by researchers of the cell wall field, have a Web-based interface and have been shown to be effective in suggesting new genes for cell wall synthesis (Mutwil et al., 2009; Ruprecht et al., 2011).

PlaNet placed query genes in network graphs of three levels: (1) the coexpressed node vicinity network, con­taining the query gene and genes coexpressed two steps away and the links among them; (2) a larger coexpres­sion cluster containing the query gene and genes coex­pressed, which resulted from running a heuristic clustering algorithm; and (3) the largest meta-network with nodes now representing all coexpression clusters instead of individual genes. PlaNet also has a module called NetworkComparer, allowing a comparative anal­ysis of gene expression networks across seven plant or­ganisms. Such comparative coexpression analysis has recently become very popular as it can help deal with missing data in a single species, reduce false positives identified as coexpressed in a single species, and enable to study the conservation of coexpression network from an evolution perspective (Movahedi et al., 2012).

An earlier tool ATTED-II (Obayashi et al., 2009) is also well known, which was developed by plant biologists since 2003 as a database for Arabidopsis tissue-specific (ATTED) expression. Compared to PlaNet, ATTED-II provides richer annotation and a lot of useful links to external resources in the query gene browse page. It also has a nicer network graph with less genes (top certain amount of genes for better visualization) and gene function information (biologically meaningful gene names) and labeled TFs. Besides, ATTED-II also predicted cis-regulatory elements in the upstream re­gions of coexpression genes. However, ATTED-II currently includes only two plants: Arabidopsis and rice, and the gene locus page is only available for Arabidopsis.

There are several methods to process microalgae into biofuel products. Figure 10.4 shows some of the more common approaches to (1) harvest/dewater microalgae, (2) release fuel precursors by compromising the integrity of the algae, followed by (3) conversion of fuel precur­sors to biofuel products.

Many algal species can be preconcentrated by simply allowing unmixed cells to settle by gravity. Additional concentration can be achieved by flocculation, centrifu­gation, microfiltration, and drying. Freshly clarified me­dia can be recycled back to the growth environment, although there are limited data regarding the number of times growth media can be recycled. Concentrated wet algal cells may be subsequently compromised by passage through a pulsed electric field, mechanical bead-milling, sonication, or enzymatic degradation (Beal, 2012a). Solvent extraction of the biomass gener­ally requires that the biomass is dried as an initial step. Once fuel precursors are exposed, they may be converted as fuel products by specific fuel conversion approaches. These methods are discussed in detail below.

MICROALGAL BIOMASS TO BIOFUELS

Microalgae can provide several different types of renewable biofuels, and numerous options exist for the conversion of components of microalgal biomass to bio­fuel. These include methane produced by anaerobic digestion of the algal biomass (Spolaore et al., 2006); bio­diesel derived from microalgal oil (Roessler et al., 1990; Sawayama et al., 1995; Dunahay et al., 1996; Sheehan et al., 1998; Banerjee et al., 2002; Gavrilescu and Chisti, 2005); biohydrogen (Ghirardi et al., 2000; Akkerman et al., 2002; Melis, 2002; Fedorov et al., 2005; Kapdan and Kargi, 2006); and biocrude derived from organics comprising microalgae. An important distinction to note is whether extracted compounds, whole biomass, or both will be converted to biofuel. Microalgae that contain high-value bioproducts (e. g. carotenoids, sulfated polysaccharides, and phycobilliproteins) may undergo a two-phase extraction scheme where the value-added product is fractionated from the biofuel production stream prior to conversion of lipids to bio­diesel and carbohydrates to bioethanol. Alternatively, the remaining organic fraction of the biomass can be converted to biocrude by HTL.

BIODIESEL

Biodiesel is derived from fatty acyl lipids from plant and animal sources. Table 10.1 shows the average oil yield per hectare from various crops. Using the average oil yield per acre, the footprint needed to meet 50% of the U. S. transport fuel needs is calculated. For example,

the high-yielding crop oil palm requires a 45 Mha crop­ping area, or 24% of the existing agricultural footprint in the US to meet only 50% of the current transport fuel needs.

Given the large agricultural footprint required, it is clear that land-based oilseed crops cannot realistically satisfy current demand. Lipid-rich microalgae, however, hold more promise as a sustainable feedstock that can significantly contribute toward demand. Under controlled conditions, the footprint required to produce an order of magnitude higher oil yields requires an or­der of magnitude smaller cropping area compared to oil palm, assuming an oil content of 30% in the microal­gae. A caveat to these numbers is that the microalgal oil yields given in Table 10.1 are based on experimentally demonstrated biomass productivity in PBRs. Demon­strated biodiesel yields on a larger scale have been much smaller. The large-scale cultivation of lipid-rich microalgae remains a significant challenge in the algae biofuel industry and thus still under intense investigation.