Recombineering is an in vivo genetic engineering method that relies on the ability to induce homologous DNA recombination. Short for recombination-mediated genomic engineering, recombineering relies on bacteriophage homologous recombination proteins that induce and catalyze homologous recombination. These proteins are introduced into the cell by plasmids or bacteriophage vectors, are endogenously expressed, or are expressed in transformed strains. Being based on homologous recombination inducing proteins give recombineering the prime advantage of inde­pendence from the use of restriction enzymes to produce changes in DNA. Those changes in DNA can be in the form of insertions, deletions, or alterations through the introduction of a synthesized oligonucleotide substrate that contains the desired change and that replaces the native DNA (Court et al. 2002). To function properly, a recombineering system must have the ability to perform three tasks. First, it must be able to prevent nucleases from degrading the foreign oligonucleotides; second, it must have an exonuclease function to cleave the foreign DNA creating sites for DNA recombination; third, it must promote the annealing of DNA strands to ensure the foreign DNA recombines and is restored into the native DNA (Sharan et al. 2009).

One example of a system that provides all the requirements for recombineering is the X Red system, commonly used for bacterial recombineering. The X Red system consists of three bacteriophage recombination proteins from three respective genes: Gam protein produced by the gam gene; Beta protein produced by the bet gene; and Exo protein produced by the exo gene (Sharan et al. 2009). A simple description of the mechanism of the X Red system is that the Exo binds to ends of the oligonucleotide and cleaves the 5′ DNA ends. This transforms the oligonucleotide to an oligonucleotide with a 3′ overhang. The Beta protein then binds to the overhangs and facilitates annealing with the native DNA, thus completing the recombination process (Fig. 9.2) (Datta et al. 2008).

The design of the produced DNA construct should depend on the ultimate objective of recombineering. Recombineering has been successfully used to insert genetic markers, retrieve DNA fragments, insert non-selectable markers, and pro­duce point mutations (Court et al. 2002).

Homologous recombination has already been shown to be a viable mean of genetically engineering algae, albeit still at a lesser efficiency than bacteria. One such example is the utilization of homologous recombination to knockout nitrate and nitrite reductase genes in Nannochloropsis sp. In this case, the required proteins appeared to be expressed endogenously (Kilian et al. 2011). A study on homolo­gous recombination in C. reinhardtti showed that recombination occurs readily between overlapping plasmids and requires around 230 homologous base pairs (bp) only, but is lacking when the recombination targets endogenous DNA (Gumpel et al. 1994). The required proteins appear to also be expressed endogenously, but the introduction of exogenous homologous recombination proteins increases the rate of recombination. Homologous recombination has also been demonstrated in

the multicellular green algae Volvox carteri (Hallmann et al. 1997) and in the red alga Cyanidioschyzon merolae (Minoda et al. 2004). Although those examples of homologous recombination in algae clearly show the complexity that arises from the significant difference in the mechanism of homologous recombination and its efficiency in different species, they also clearly demonstrate the viability of engi­neering a homologous recombination system (recombineering) in algae.

Microalgae cultivation has an advancing role in solving some of the future limi­tations of traditional biomass production and markets (i. e., food, feed, energy, emission mitigation, chemicals, materials, etc.). In conjunction with conventional growth systems, new biomass industries such as microalgae must be developed in order to produce large-scale sustainable products cost-effectively. This book pre­sents some of the most promising existing microalgal biomass growth technologies and summarizes some of the novel methodologies for sustainable and commercial microalgae production.

There are many different and unrelated microalgae taxonomic groups that directly or indirectly utilize solar energy to produce organic compounds. At present only a little over a dozen species are medium-term candidates for large-scale cul­tivation. However, even an ideal microalga must sustain high productivity under varying environmental and production conditions, and produce commercially sought services and products.

To enable such an industry, both advancements in microalgae biological sci­ences and biomass production and processing engineering systems are a major parallel focus of this book. We are very pleased to have 17 excellent chapters detailing some of the latest research and developments in microalgae cultivation and processing techniques, heterotrophic production methods, introduction of wastewaters, the effect of CO2 injection, flocculation and auto-flocculation, in addition to species-specific extraction methods and bioproduct optimization. The chapters also include the production of a range of fuels, including anaerobically digested microalgae for biogas, and biodiesel, bioethanol, and also hybrid chemical and electric production systems. Furthermore, innovations in production via genetic engineering for microalgae strain improvement, synthetic biology approaches, genomic, and metabolic modeling approaches are analyzed and discussed in detail. In terms of large-scale production, selected chapters discuss the economics of harvesting and downstream processing, energy and economic modeling for large — scale facilities, and research on life-cycle environmental impact of microalgae biofuel and co-products.

As the book developed we were amazed at the breadth and detail of the ongoing advances in both the biological and engineering elements that surround modern microalgae production. We trust the reader will enjoy the book as much as we enjoyed writing and editing it.

The editors would like to thank all of the authors for their excellent and timely contributions. Considering the professionalism and the experience of the contrib­uting researchers, the editing of this book was a relative pleasure. We also thank the many people directly and indirectly involved in the production of this book, including the publisher, and also our families who supported us through our various endeavors.

Navid R. Moheimani School of Veterinary and Life Sciences, Algae R&D Centre,

Murdoch University, Australia

Mark P. McHenry School of Engineering and Information Technology, Murdoch University, Australia

Karne de Boer

School of Engineering and Information Technology, Murdoch University, Australia

Parisa A. Bahri

School of Engineering and Information Technology, Murdoch University, Australia

Various species of microalgae can be used for biodiesel oil production using either photoautotrophic, heterotrophic, or mixotrophic culture system. However, the suitability of each depends on the strain, availability of facilities, nature of organic carbon source, and other culture conditions. In a comparison of the lipid produc­tivity in 20 species of photosynthetic microorganisms, three strains produced more lipid in heterotrophic cultures when compared to photoautotrophic cultures and 11 strains produced more in mixotrophic cultures than in photoautotrophic cultures (Ratha et al. 2013).

Lipid productivity is a product of lipid content and biomass concentration and gives the total amount of lipid formed per unit culture volume and time (g-lipid/L. d). Thus, strategies for improving productivity aims to increase lipid content without significant reduction in cell growth, increase cell growth without significant reduc­tion in oil content, or increase both cell growth and lipid accumulation per cell. Several species of microalgae can be induced to overproduce lipids by the choice of culture system as well as by manipulations of the culture medium such as the source and concentrations of carbon, nitrogen, phosphorous, and silicate as well as by manipulation of culture conditions such as temperature, pH, and oxygen tension.

Nitrogen starvation is one of the most studied methods of inducing oil accumu­lation in microalgae. As a result of nitrogen starvation, the lipid content as high as 70-85 % of dry weight has been reported (Becker 1994). The effectiveness of nitrogen limitation in increasing lipid contents of microalgae has been demonstrated with many strains such as Prophyridium cruentum (Becker 1994), Chlorella vulgaris (Widjaja et al. 2009; Converti et al. 2009), Chlorellaprotothecoides (Miao and Wu

2004) , as well as many strains of cyanobacteria and other green algae (Shifrin and Chisholm 1981; Illman et al. 2000; Takagi et al. 2000; Li et al. 2008a, b). However, using nitrogen limitation to increase intracellular lipid content may have negative effects on cell growth and thus lipid productivity, which has been reported for several strains (Hsieh and Wu 2009; Xiong et al. 2008). On the whole, the effectiveness of nitrogen limitation in increasing lipid productivity depends on the strain. In the case of diatoms, such as Achnanthes brevipes and Tetraselmis sp. for example, nitrogen limitation leads to accumulation of carbohydrates, rather than lipids (Guerrini et al. 2000; Gladue and Maxey 1994). Under nitrogen starvation, accu­mulation of lipids has been attributed to mobilization of lipids from chloroplast membranes as chloroplastic nitrogen is relocated by 1.5-biphosphate carboxylase/ oxygenase (E. C. 4.1.1.39, Rubisco) (Garcia-Ferris et al. 1996). Some other reports have shown that increased lipid content of cells at low nitrogen concentration may be due to the high C/N ratio, rather than the absolute nitrogen concentration. For example, in culture of Chlorella sorokiniana, a C/N ratio of 20 gave the lowest cell lipid content, but increased at both higher and lower C/N values (Chen and Johns 1991). Furthermore, in marine (Cryptheconidium conhii) and freshwater (Chlorella sorokiniana) algae, accumulation of lipids may not be dependent on nitrogen exhaustion but on an excess of carbon in the culture media. Hence, in heterotrophic cultures, lipid accumulation was attributed to consumption of sugars at a rate higher than the rate of cell generation, leading to conversion of excess sugar into lipids (Chen and Johns 1991; Ratledge and Wynn 2002; de Swaaf et al. 2003).

In the case of diatoms, lipid accumulation is related to depletion of silicates because of their dependence on silica for growth (Roessler 1988; Wen and Chen 2000a, b, 2003; Wilhelm et al. 2006). Roessler (1988) reported that silicon defi­ciency could induce lipid accumulation in Cyclotella cryptica by two distinct processes: (1) an increase in the proportion of newly assimilated carbons which are converted to lipids and (2) a slow conversion of previously assimilated carbon from non-lipid compounds to lipids.

Phosphorus starvation has also been used to induce lipid synthesis (Zhila et al. 2005; Weldy and Huesemann 2007). Khozin-Goldberg and Cohen (2006) found that phosphate limitation could cause significant changes in the fatty acid and lipid composition of Monodus subterraneus. However, in other species such as Nan — nochloris atomus and Tetraselmis sp., phosphorus deficiency led to reduced lipid content of the cells (Reitan et al. 1994).

Lipid synthesis may also be induced under other stress culture conditions such as high light intensity (Guedes et al. 2010; Khotimchenko and Yakovleva 2005; Qin 2005; Weldy and Huesemann 2007), low temperature (Renaud et al. 2002; Qin

2005) , high salinity (Kotlova and Shadrin 2003; Takagi et al. 2006; Qin 2005; Wu and Hsieh 2008), pH control (Guckert and Cooksey 1990), CO2 concentration (Chiu et al. 2009; de Morais and Costa 2007), and high iron concentration (Liu et al. 2008). The polyunsaturated fatty acid contents of microalgae tend to increase at low temperatures. The high PUFA content at low temperature might be explained by the need for the algae to produce more PUFAs to maintain cell membrane fluidity. Another reason might be that low temperature could lead to a high level of intracellular molecular oxygen and hence improve the activity of the desaturase and elongase involved in the biosynthesis of PUFAs (Jiang and Chen 2000).

Aside from lipid productivity, it is important to consider the quality of lipids produced by microalgae, since the quality of the oil influences the quality of the biodiesel. European Biodiesel standards (EN 14214 and 14213) limit the contents of fatty acid methyl esters with four and more double bonds to a maximum of 1 % (mol/mol). According to the EN 14214, for example, the linolenic acid (C18:3) should not exceed 12 % (mol/mol) (Knothe 2006). These oils require additional

treatment, such as partial catalytic hydrogenation (Dijkstra 2006). An advantage of microalgae oil is that the composition of the oils can be controlled by controlling the culture condition, as previously discussed. In some strains, it has been reported that nutrient limitation results in a change in lipid composition from free fatty acids to TAGs which are more suitable for biodiesel production (Widjaja et al. 2009). In some strains, nitrogen starvation might not result in an increase in total lipid content, but a change in lipid composition. Zhila et al. (2005) reported that nitrogen limitation increased oleic acid contents of Botryococcus braunii, yet the content of total lipids and triacylglycerols did not change. The saturation of fatty acids is directly dependent on the amount of excess sugar and the culture conditions (Tan and Johns 1991; Wood et al. 1999; Wen and Chen 2000a, b). Wood et al. (1999) noted that as the concentration of sugar increased, the fatty acid became more saturated. The type of nitrogen source also affects the quality of oil. In cultures of N. laevis, ammonia favored saturated and monounsaturated fatty acids (C14:0, C16:0, C16:1), and nitrate and urea promoted polyunsaturated fatty acids (C20:4 and C20:5) (Wen and Chen 2001a, b).

Genetic and metabolic engineering has high potentials for improving both the lipid quality and lipid productivity and therefore the economy of microalgae bio­diesel. Much work has been done on genetic engineering of cyanobacteria, and many functional genes have been successfully cloned in cyanobacteria (Qin et al. 1999). For example, Acc1 (a kind of restriction enzyme) has been cloned from Cylclotella cryptica (an oceanic diatom) for the production of biofuel (Roessler 1988). Genetic engineering of diatoms has increased the lipid contents from 5-20 % to over 60 % under laboratory conditions. The improvement of lipid content in genetically engineered microalgae is mainly due to the high expression of acetyl — coA carboxylase gene, which plays an important role in the control of the level of lipid accumulation (Huang et al. 2010). Due to the various advantages of hetero­trophic cultures, attempts have also been made to genetically modify obligate photoautotrophs for heterotrophic growth. For example, Glut 1 gene that encodes the glucose transporter protein was introduced into Phaeodactylum tricornutum (which is an obligate phototroph), thereby making it possible to grow it hetero — trophically (Zaslavskaia et al. 2001).

Carbon constitutes half of the weight of the biomass, and it is usually supplied as CO2 (Gonzalez-Lopez et al. 2012). CO2 is normally added to the microalgae cultures as either air (generally no more than 1.0 % CO2) or flue gas (typically 10-20 % CO2) (Cheng et al. 2006). Environmental CO2 bioremediation using microalgae has a great potential, and many parameters have been investigated to optimize this process. Table 7.1 shows microalgae production parameters, including initial cell concentration, input CO2 concentration, aeration rates, temperature, light intensity, and PBR technologies, all normalized for CO2 fixation rates as g L-1 d-1. Using Tetraselmis sp. and Nannochloropsis oculata to fix CO2 from the air, it was reported an improved CO2 fixation rate for Tetraselmis sp. (0.0241 g L 1 d!) relative to N. oculata (0.017 g L-1 d-1) (Chik and Yahya 2012). Similarly, in another work, the CO2 sequestration rate for Dunaliella tertiolecta (0.12 g L-1 d-1) and Chlorella vulgaris (0.09 g L-1 d-1) was studied and it was demonstrated that D. tertiolecta fixed CO2 at a higher rate and greater efficiency than C. vulgaris (Hulatt and Thomas 2011). Furthermore, when 0.03 % CO2 was used for Chlorella pyr — enoidosa SJTU-2 and Scenedesmus obliquus SJTU, CO2 fixation rates of 0.134 and

0. 150 g L-1 d-1 were obtained, respectively, which showed higher fixation rates than those reported in the literature (Hulatt and Thomas 2011). The results pre­sented in Table 7.1 show that a wide range of CO2 biofixation rates are achieved (from 0.0241 to 0.150 g L 1 d!) from a number of different microalgae strains (Tetraselmis sp., N. oculata, C. vulgaris, D. tertiolecta, C. pyrenoidosa SJTU-2, S. obliquus SJTU, and Synechoccus sp.), using different PBRs (aerated flask, bubble column, and light diffusing optical fiber). The major differences in these publica­tions are the use of different algal species and the reported higher fixation rates that could be due to the use of two genetically engineered strains of microalgae (Tang et al. 2011). Determining the major parameters for increased CO2 fixation rates requires a more in depth study. For example, research on CO2 mitigation efficiency under 0.03, 0.55, and 1.10 % environmental CO2 concentrations for cyanobacte­rium Synechococcus sp. using light diffusing optical fibers (LDOF) showed 100, 33, and 15.4 % CO2 fixation efficiency, respectively (Takano et al. 1992). As the CO2 fixation efficiency decreased with increased CO2, the least percentage of input CO2 (0.03 %) attained the highest CO2 fixation efficiency (100 %), as supplying less CO2 allow microalgae cells to consume CO2 more efficiently. Yet, microalgal cell growth highly depends on supplied CO2 concentrations, and CO2 concentrations in air (0.03-1 %) do not commonly yield sufficient microalgal growth in PBRs. As industrial flue gases consist of higher CO2 concentrations suitable for microalgae production (Wang et al. 2008), using flue gas as feed for microalgae, not only can increase the productivity and cell growth of microalgae, but also remove CO2 from atmosphere more efficiently (Chen et al. 2012; Chiu et al. 2011). Furthermore, calcified microalgae (i. e., coccolithophorid microalgae) are of additional interests for CO2 bioremediation as they are able to form CaCO3 together with

photosynthetic carbon fixation (Moheimani et al. 2012b). In this case, carbon is removed by photosynthesis as a part of the carbon cycle, while the CaCO3 can be discharged (precipitated) out of the carbon cycle.

Edward and Palsson developed the first genome-scale metabolic reconstruction in 1999 for Haemophilus influenza (Edwards and Palsson 1999) and described its emerging systems properties through flux balance analysis (FBA). In FBA, fluxes of metabolites through biochemical reactions are constrained by four parameters: mass conservation, thermodynamics (reaction reversibility), steady state assumption for internal metabolite concentrations, and nutrient availability. All of these constraints define the boundary conditions required to solve systems of linear equations in which a biologically motivated objective function (e. g., biomass production) is optimized.

The solution of an FBA problem is the optimal distribution of fluxes through the metabolic network, comprising a metabolic phenotype or functional state of the network (Orth et al. 2010). Assumptions and boundary flux parameters can reduce the size of the feasible solution space. An objective function, which is a set of reactions that the cell is assumed to be optimizing for a given mode of growth, can be mathematically (through linear optimization) maximized (or minimized) to derive flux values that support optimal solution(s) for the metabolic state under investigation. The solution is of course sensitive to growth and boundary param­eters for a given model. As an example, iRC1080 reconstruction of C. reinhardtii metabolic network (Chang et al. 2011) was optimized for biomass production under two different conditions of growth, with light and acetate, and dark with acetate. As the alga grows in the dark, it relies solely on acetate as a source of energy and carbon, while photosynthesis provides both under light growth. Consequently, major flux redistributions can be expected system-wide and are observed between simulated growths under these two conditions (Fig. 10.4).

Metabolic models can make use of high-throughput data such as gene expression data (including mRNA and protein expression data), and 13C flux data by directly imposing additional constraints on the metabolic model based on the values obtained from wet-bench experiments. For example, if obtained experimental data show that glycolytic enzymes are highly active under certain conditions, flux can be pointed through glycolysis by constraining the relevant fluxes in silico, thus driving flux through the activated reactions and allowing estimation of changes in global flux distributions (Shlomi et al. 2005). Phenotypic data, such as Biolog data (Bochner 2003, 2009), which describe cellular metabolic profiles, can also be used to validate in silico phenotypes (Oberhardt et al. 2008). Notebaart and his col­leagues used Saccharomyces cerevisiae as a model organism for this type of analysis. In their work, they carried out a comparison of in silico metabolic fluxes versus microarray gene expression data in E. coli and S. cerevisiae. Their results revealed that metabolic genes whose fluxes are directionally coupled generally

show similar expression patterns, share transcriptional regulators, and reside in the same operon (Notebaart et al. 2008).

Flux variability analysis (FVA), which is a variant of FBA, determines the max­imum and minimum values of all the fluxes that will satisfy the constraints and allow for the same optimal objective value (Gudmundsson and Thiele 2010). For example, FVA can be applied to predict the range of possible by-product production rates under maximal biomass production, which can be linked to gene expression data. Varia­tions of FVA can also be used to determine blocked or nonessential reactions.

The main advantages of microalgae growth compared to land plants are the ability to grow on arid land using saline water (Fig. 1.1). This means that microalgae cultures will not compete with food crops over agricultural land and freshwater. However, microalgae, the same as any other photosynthetic organism, would still require fertilisers (especially nitrogen and phosphorous) to grow. If grown in sea water, macronutrients are necessary to be added to the culture to achieve high growth rate. Borowitzka and Moheimani (2013a) indicated that for producing

100,0 bbl of algal oil year 1, there is a need for 14,447 and 219 tons of nitrogen (as NaNO3) and phosphorous (as NaH2PO4), respectively. Such a high volume of fertilisers will significantly affect the overall cost of production. Furthermore, phosphorous is a non-renewable resource, and at current rates of extraction, global commercial phosphate rock reserves may be depleted in less than 100 years (Cordell et al. 2009). That means that algae cultures, irrespective of their product, will be in direct competition with food crops over fertilisers. Obviously, one very important consideration in developing any potential large-scale algae production facility is the recycling of the medium (Fig. 1.1). Recycling medium especially post-extraction/conversion would allow the recycling of a large amount of fertilisers especially if the wet biomass is being converted to biodiesel and biomethane (Fig. 1.1). Furthermore, there is a possibility of combining microalgae cultivation with wastewater treatment. Combining microalgae cultures with wastewater treat­ment plants (domestic or animal waste) can provide microalgae with required nutrients and result in lower cost wastewater treatment than traditional approaches.

The potential of combining microalgae cultures and domestic wastewater treatment was first proposed in 1960s with the main interest to produce biofuel (Oswald and Golueke 1960). There are currently some facilities around the world (i. e. New Zealand, USA) using high rate algal ponds (HRAPS) for treating tertiary domestic wastewater. In general, microalgae growth in tertiary-level wastewater treatment can significantly reduce the electromechanical cost of treatment (Craggs et al. 2013). Another advantage of using microalgae in the domestic wastewater treatment process is more efficient nutrient removal and sunlight-driven disinfection (Davies-Colley et al. 2005). Animal waste (i. e. piggery waste) can also be treated using microalgae cultures. The environmental impacts of intensive pig production can be significant. A poorly managed piggery may risk wastewater pollution to local waterways, produce odour emissions and release greenhouse gases into the atmosphere (Maraseni and Maroulis 2008). Wastewater generated through high- intensity pig production is high in ammonia and phosphorous while also having high chemical and biological oxygen demands (Olguin et al. 2003). High phos­phorous levels have been shown to correlate to high turbidity levels giving the effluent a dark colour (Ong et al. 2006). One wastewater treatment system that is gaining acceptance in Australian piggeries is anaerobic digestion ponds. These systems typically consist of a covered pond containing wastewater which is bio­logically treated by heterotrophic microorganisms in the absence of oxygen. The covered digesters allow the production and capture of biogas including methane and carbon dioxide. The benefits obtained from these ponds are the removal of solids through settling, capture of biogas for use as a biofuel and the reduction of odour emissions. The utilisation of methane as a fuel source can effectively reduce dependence on energy sources from outside the piggery. One challenge is that the anaerobic digestion effluent from piggeries is very high in ammonium (toxic to most organisms). If a process incorporating CO2 uptake such as algae culture was to be adopted, ideally CO2 (generated via burning CH4 or separated from the raw biogas stream) will be captured and reused within the piggery. A recent review of wastewater management in Australian piggeries recommended that along with anaerobic digestion, microalgae culture systems should be investigated further as a potential component of the Australian piggery wastewater management strategy (Buchanan et al. 2013).

To date, all trials on culturing microalgae on undiluted and untreated anaerobic digestion piggery effluent (ADPE) have failed to gain widespread acceptance in the industry. On the other hand, there are reports of the successful microalgal culti­vation on piggery anaerobic digestate after dilution with freshwater (Park et al.

2010) . Interestingly, in some cases, the digestate was diluted more than 15 times with freshwater. In the context of an Australian piggery system, such a method would never be practical due to the shortage of freshwater. Ayre (2013) isolated three microalgae capable of growing on undiluted, sand-filtered, piggery anaerobic digestate. This proof-of-concept study clearly illustrated the potential for culturing microalgae in such effluent with a high ammonium content. The produced algae biomass on piggery anaerobic digestate will sequester carbon and remove nutrients (i. e. nitrogen and phosphorous). The produced biomass could alternatively be used as pig feed, although the biomass pathogen load would need to be closely moni­tored (Buchanan et al. 2013). Another potential application for the biomass is the co-anaerobic digestion with the piggery waste.

Wastewater has a relatively low nutrient content, with usually less than 1 % N and less than 0.5 % P. Because of this low nutrient content, it is not cost-effective to transport wastewater over long distances to microalgal farms. Therefore, microalgal farms should be situated as close as possible to the wastewater sources. It is also costly to store wastewater during periods when microalgae production is low, and the production of microalgal biomass should ideally more or less match the gen­eration of wastewater. Such spatial and temporal mismatches between availability of wastewater and microalgal productivity may limit the potential to convert wastewater nutrients into microalgal biomass. Large cities generate enormous volumes of wastewater that could be used for microalgae biomass production. These cities, however, often lack sufficiently large areas of low-cost land nearby that can be used for microalgae production (Fortier and Sturm 2012). In addition, many of the world’s largest cities are situated in temperate climates where micro­algal productivity is low in winter and freezing temperatures may even require complete shutdown of microalgal farms for a few months (Chiu and Wu 2013). At high latitudes in winter, it may be impractical to use wastewater for microalgae production (Van Harmelen and Oonk 2006), and at low latitudes, high temperatures may also limit productivity during the warmest months.

Availability of land is probably less problematic when animal manure is used for microalgae production than when domestic wastewater is used. Livestock farms are generally situated away from cities. In general, agricultural land is available that can be converted in microalgae cultivation ponds. In many countries, however, there is an ongoing debate whether microalgae cultivation is allowed on agricultural land or not (Trentacoste et al. 2014). Due to economies of scale, we can assume that the minimum size of a microalgae farm would be about 10 ha (Lundquist et al. 2010). A 10-ha farm that produces 300 ton dry microalgal biomass ha-1 year-1 consumes 27 ton N year 1. This corresponds to the N output of a municipality of 9000 inhabitants, or a farm with at least 7000 pigs or 400 cattle. Wastewater from smaller farms or villages may be collected and transported to a microalgae farm, but this is only possible over relatively small distances due to the high cost for transporting wastewater. Therefore, smaller and isolated sources of wastewater will unlikely be practical for microalgae production.

TALENs comprise a powerful class of tools that are redefining the boundaries of biological research. TALEs are naturally occurring proteins from the plant patho­genic bacteria genus Xanthomonas, and contain DNA-binding domains composed of a series of 33-35 amino acid repeat domains that each recognizes a single base pair. TALE specificity is determined by two hypervariable amino acids that are known as the repeat variable di-residues (RVDs). TALEs can be quickly engineered to bind practically any desired DNA sequence (Boch 2011). TALENs can be used to edit genomes by inducing double-strand breaks (DSB) (Fig. 8.2b), which cells respond to with repair mechanisms (Boch 2011). Several methods have been developed that enable rapid assembly of custom TALE arrays such as golden gate cloning, high-throughput solid-phase assembly, and ligation-independent cloning techniques (Gaj et al. 2013).

Site-specific nucleases have enabled the introduction of targeted modifications in several model organisms common to biological research, including zebrafish, rat, mouse, Drosophila, Caenorhabditis elegans, and many other non-model species including the monarch butterfly, frogs and livestock (Gaj et al. 2013). In addition to valuable animal models, TALENs have been used to introduce targeted alterations in plants, including Arabidopsis and several crop species (Curtin et al. 2012), allowing the incorporation of valuable traits, such as disease and herbicide resistance.

In algae, studies of TALENs to modify the genome of Chlamydomonas have been initiated (Borchers et al. 2012; Spalding and Wright 2011). For instance, TALENs to knockout the Chlamydomonas Sta6 and CAH3 genes, which are responsible for starch production and CO2 uptake, have been designed. These early studies are of interest due to the status of Chlamydomonas as a model organism for biofuel production.

The diversity of organisms modified by these site-specific nucleases will undoubtedly continue to grow, expanding the repertoire of model systems for basic research. TALENs will also enhance research efforts in algal biomass production, thus opening new avenues for algal biofuels commercialization.

Custom-designed TALE arrays are commercially available through Cellectis Bioresearch (Paris, France), Transposagen Biopharmaceuticals (Lexington, KY, USA), and Life Technologies (brand of Thermo Fisher Scientific, Carlsbad, CA, USA).

Bailliez et al. (1986) found that the immobilized B. braunii cultures in calcium alginate beads had higher chlorophyll and photosynthetic activities compared to their free cells. S. obliquus cells immobilized in alginate (Brouers et al. 1983), and C. vulgaris and Anacystis nidulans in agar (Kayano et al. 1981; Weetall and Krampitz 1980), also showed a significant increase in their chlorophyll content. Enhanced chlorophyll and photosynthetic activity was explained by the protection of immobilized cells from photoinhibition due to the self-shadowing effect, and a possible increase in the concentrations of particular ions in the microenvironment of cells which can improve photosynthesis (Bailliez et al. 1986; Tamponnet et al. 1985).

Individually co-immobilized cells of C. vulgaris and C. sorokiniana with A. brasilense growth-promoting bacterium also yielded higher chlorophyll a and b, violaxanthin, and lutein accumulation compared to the immobilized algal cells without any bacterium (de-Bashan et al. 2002a).

Lebeau et al. (2000) reported that the immobilization of the marine diatom Haslea ostrearia in agar had a positive effect on the continuous production of the marennin pigment, which is primarily used for the oyster-breeding industry.

Some potential limitations of the secondary product formation by immobilized cells are the commonly reported slower growth rates of the microorganisms compared to their free-cell suspension systems and slower diffusion rates of the target-products (i. e., hydrogen) from the cells into their environment. Resolving these issues with the combination of optimized immobilization matrices and innovative bioreactor designs (e. g., some attempts include membrane-based cell recycle bioreactor (Chang et al. 1994); dual-layer coaxial hollow fiber-type biore­actor (Yang et al. 2006); and a multimembrane bioreactor in a pressure cycling mode (Efthymiou and Shuler 1987), aiming to increase the nutrient transfer to the cells) can potentially bring other dimensions to the research areas of those afore­mentioned bioprocesses.

Two criteria that drive strategies for algae-based wastewater treatment for biofuel production are the need to produce a high-quality effluent on a consistent basis and the need to produce biomass with high oil content. While there are proponents for hydrothermal liquefaction (HTL) treatment of algal biomass to develop a “green crude,” the goal in the scenario described above is to separate the oil and the biomass. The nitrogen and phosphorus content captured in the biomass is needed in the production of methane in the digester, and later, the biosolids from the digester can be composted with green waste to make a high-quality fertilizer.

To meet both criteria, compromises must be made in the selection of algae. In traditional wastewater oxidation ponds, there are a wide array of different pro­karyotic and eukaryotic photosynthetic organisms. These ponds are subject to seasonal shifts in dominant populations as well as changes due to predation by protozoans and zooplankton. The variation in algal population dynamics can be minimized by periodic inoculation of the pond with a desired unialgal strain cul­tured in photobioreactors. Another consideration is the relationship between bac­teria in the wastewater and the algae. In addition, evidence exists for a role for heterotrophic microbes in algae auto-flocculation (Lundquist et al. 2011). A close examination of algae collected from wastewater ponds using a light microscope will usually show a significant number of heterotrophs associated or attached to the surface of the algae. If the algae are to be co-cultivated in photobioreactors as described above, it would be prudent to include the strains of wastewater bacteria associated with the specific type(s) of algae and which promote flocculation. The microalgae encompass a phylogenetically diverse assemblage of prokaryotic and eukaryotic photosynthetic microorganisms found in a wide range of habitats ranging from terrestrial environs to fresh and marine to hypersaline waters. It follows then that the ecology, morphology, biochemistry, and physiology are also diverse. Although the number of algal species is estimated to range between 30,000 and 300,000 with 7500 species systematically estimated from the literature (Guiry

2012) , less than 1 % have been isolated and characterized (Radner and Parker

1994) . Thus, the biotechnological potential of these microorganisms is just beginning to be explored for production of high-value and value-added products and biofuels. Microalgae have been used for decades as a source of high-value compounds with pharmaceutical activity including anticancer, antimicrobial, anti­viral agents, and pigments including a variety of carotenoids, cosmetics, nutra — ceuticals, and feed supplements for poultry, livestock, and mariculture (see Walker et al. 2005 for a review). Many groups are now exploring the use of transformable eukaryotic strains to produce heterologous proteins since they are capable of intron- splicing, glycosylation, and multimeric protein assembly (Spolaore et al. 2006).

Several aspects of algae biology and physiology are relevant to their economic potential of microalgae as a feedstock for biodiesel coupled to bioremediation. Of particular interest to the biofuels industry are productivity, biochemical composi­tion, and the influence of environmental and cultural practices on physiological processes, especially lipid metabolism and its regulation, photosynthetic efficiency, cell wall structure, and heterotrophic/mixotrophic capabilities. In addition, tech­niques to control algal/microbial pond community structure are essential for quality control of biodiesel composition from algae biomass. The ASTM International consensus-based standards group, whose standards are recognized in the United States, have specifications for the quality of biodiesel. The fuel characteristics are strongly influenced by FAME composition including chain length and degree of saturation. FAMEs isolated from algae species range in size from 12 to 38 carbons. The hydrocarbons that comprise petroleum products range in length as follows: 5-12 carbons for gasoline, 10-15 for diesel fuel, and 12-16 for kerosene (the main component of jet fuel). Refineries crack the longer hydrocarbons found in crude oil, then distill and blend the resulting compounds to formulate standard petroleum products. Maintenance of species composition, especially in outdoor ponds and when using wastewaters, is problematic. Cultivation of Spirulina in open outdoor ponds has been a success story in commercial algaculture. This strain grows in nearly pure culture in the alkaline, high-salinity waters of Lago de Texcoco. Competition from invading species is minimized due to the inhospitable nature of these waters. Control of species composition is crucial to quality control of biodiesel production. Lipid profiles are characteristic of some organisms and have even been used as a taxonomic feature. However, microalgae show great inter — and intraspecific variation in fatty acid profiles and these profiles can be affected by culture conditions (Roessler 1990).