Energy-efficient and cost-effective microalgae dewatering, nutrient recycling and effluent water quality control are some of the major challenges facing industrial — scale microalgae production for commodity feeds and fuels (Benemann 2013; Borowitzka and Moheimani 2013b; Wyman and Goodman 1993a). Irrespective of the cultivation system, the biomass concentration of the algae culture is generally low (a few mg L 1 in open ponds to a few g L 1 in intensive closed photobiore­actors). Dewatering is therefore critical for producing any materials from micro­algae. The objective of harvesting and dewatering is to raise the concentration of the microalgal biomass by more than two orders of magnitude to over 10 % solids, sufficiently concentrated for subsequent processing or drying. It is widely believed that this is best achieved using a combination of technologies in a two-stage process (Benemann et al. 1982; Shelef et al. 1984; Vandamme et al. 2013), such as floc­culation followed by centrifugation. This necessitates that large volumes of water need to be processed to harvest the biomass. This concentration process is typically energy intensive and results in high harvesting, thickening and dewatering costs (Mohn 1988). Available harvesting and dewatering process selection often interacts with both up — and downstream process steps in microalgae production, such as strain selection and medium composition, biomass fractionation (e. g. in a biore­finery) and water or nutrient recycling (de Boer et al. 2012; Wijffels et al. 2010).

Depending on the strain and organic carbon source, a major disadvantage of het­erotrophic culture is that light is required for increased productivity. In some strains, for example, lipid productivity is higher under photoautotrophic cultures when compared with heterotrophic cultures. Furthermore, the cost of organic carbon source can be very high, making heterotrophic production of biodiesel oil uneconomical. Solar light energy is abundant and freely available in outdoor photoautotrophic cultures. Thus, it is desirable to use light from solar energy to reduce production costs. Depending on the location and season, however, only a few hours of the day have the high light intensity needed to support photoautotrophic growth. During the night, cells not only cease to grow, but they metabolize the already intracellularly stored energy for cell maintenance, thereby leading to a decrease in biomass con­centration (Ogbonna and Tanaka 1996). Cyclic photoautotrophic-heterotrophic culture seeks to overcome this problem by cultivating cells photoautotrophically during the day, while adding the required amount of organic carbon source to grow the cells heterotrophically at night. By taking advantage of both alternating photo­autotrophic and heterotrophic cultures, the cells grow continuously during both day and night, leading to increased productivity (Ogbonna and Tanaka 1998; Ogbonna et al. 2001). This is especially useful for the cultivation of some microalgae that are not truly mixotrophs, yet can switch between phototrophic and heterotrophic metabolisms, depending on environmental conditions (Kaplan et al. 1986). However, the technical challenge of minimizing contamination risk through the selection and control of the organic carbon source added during the night remains.

Data in Table 7.1 present the effect of CO2 concentration of the input gas on biomass concentration and CO2 removal of cyanobacterium Synechococcus sp. (Takano et al. 1992). When the CO2 concentration of the input gas increased from 0.03 to 0.55 %, the biomass concentration rose by 1.5-fold and the CO2 removal rate more than doubled. However, when the input gas concentration further increased from 0.55 to 1.10 %, only a slight increase in CO2 removal occurred (Takano et al. 1992). Furthermore, Fig. 7.4 shows the influence of the input CO2 content on S. obliquus WUST4 (Li et al. 2011) in the range of 6-18 %. The highest CO2 fixation efficiency (67 %) was achieved at 12-14 % CO2, indicating higher CO2 concentration is an inhibitory factor to CO2 fixation, and is an species — dependent variable tolerance.

Wastewater treatment consists of the removal of unwanted chemicals, or biological contaminants from impure water sources, such as from the liquid wastes released by houses, industrial operations, or agricultural processes. Conventional wastewater treatment methods include physical processes such as filtration and sedimentation; chemical processes such as flocculation and chlorination; and biological processes such as generation of activated sludge (Metcalf and Eddy 2003). However, these methods are mainly based on the separation of pollutants from the wastewater with a requirement for a further stage to eliminate these pollutants. This brings a need for an integrated wastewater treatment process that eliminates the undesired portion of the wastewater while converting them into valuable products, which can be successfully achieved by applying a selected immobilization process. Immobilized algal systems are particularly effective for the removal of nutrients (i. e., phosphate and nitrate) and various metals from wastewaters, which will be discussed in the following sections.

Wastewater not only contains nutrients such as N and P, but also a range of other contaminants that may interfere with microalgal growth. The presence of growth — inhibiting substances probably explains why microalgal growth rates in real wastewaters are often slightly lower than in synthetic wastewaters. Contaminants not only pose a problem because they inhibit microalgal growth, but they can also accumulate in the microalgal biomass and limit the valorization of the microalgal biomass, or fractions thereof. Wastewater can contain a wide range of toxic chemicals such as heavy metals, persistent organic pollutants, and surfactants.

This is particularly the case in industrial wastewaters, although domestic waste­water or animal manure may also contain substantial quantities of pollutants such as heavy metals (Nicholson et al. 1999). Even chemicals with a low toxicity (such as those used in personal care products), may have an inhibitory effect on microalgal growth rates (e. g., Wilson et al. 2003).

Animal manure wastewaters often contain very high concentrations of N present in reduced form as ammonium. Ammonium is converted to free ammonia at high pH and is toxic to many microalgae, with some microalgae inhibited by concen­trations as low as 20 mg L 1 (Azov and Goldman 1982). High concentrations of ammonium may result in ammonia toxicity even when pH is low (Peccia et al. 2013). Some substances do not inhibit microalgal growth, but accumulate in the microalgal biomass and limit valorization. The cell wall of microalgae is often rich in carboxyl, amino, hydroxyl, or sulfate groups. These groups are anionic and can bind metals through ion exchange (Wang and Chen 2009). Microalgae are efficient absorbers of heavy metals and even low concentrations of heavy metals present in wastewater can be absorbed into microalgal biomass. This is not necessarily a problem when the biomass is converted into biofuels using chemical or physical methods. It may be a problem when biological methods are used to convert the biomass into fuel (e. g., anaerobic digestion, fermentation). It may also be a problem when the protein-rich residue of the biomass remaining after extraction of lipids for biodiesel production is to be used as animal feed, as is proposed in the microalgal biorefinery context (Wijffels et al. 2010). Rwehumbiza et al. (2012) showed that metals used for flocculating microalgae remain in the protein-rich residue after extraction of lipids. However, microalgal absorbtion of heavy metals from waste­water can also be an advantage, and numerous studies have demonstrated that microalgae can be applied to remove heavy metals from a variety of wastewaters (see for instance Wilde and Benemann 1993; Gadd 2009). Wastewater may also contain microbial contaminants such as cysts of parasites, infectious bacteria, or viruses. These may also interfere with the use of microalgal biomass fractions as animal feed. However, due to the high pH, high oxygen concentrations, and exposure to light, many harmful microorganisms tend to be inactivated in micro­algal cultures (Davies-Colley et al. 1999).

Many wastewaters of agricultural origin such as piggery waste or anaerobic digestion wastewater often have a dark color. This dark color is predominantly due to the presence of humic substances derived from incomplete breakdown of lignin in plant material (Brezonik and Arnold 2011). This dark color limits the light pene­tration in the water and reduces microalgal productivity (Martin et al. 1985); potentially a 20-30 % lower productivity when compared to growth rates in a non­colored culture medium (De Pauw et al. 1980). In many laboratory studies on production of microalgae in animal manure wastewater, the wastewater is diluted prior to the experiments and inhibition of microalgal productivity by the dark col­oring is barely noticed. However, in large-scale systems, dilution of wastewater with pure water will be unsustainable due to the high water demand. To prevent inhibition of growth by colored substances, the wastewater can be pre-treated with oxidizing agents such as sodium hypochlorite, ozone or hydrogen peroxide, or by coagulants, flocculants, or adsorbents (Markou et al. 2012b; Depraetere et al. 2013). Alterna­tively, nutrients can be separated from the wastewater containing colored substances and then added to the microalgae culture medium. This can be carried out, for instance, using dialysis membranes (Blais et al. 1984). Also nutrients can be sorbed onto zeolites and released from the zeolites in fresh medium (Markou et al. 2014).

The ongoing progress in sequencing of algal genomes will permit annotation, comprehensive cloning and manipulation of genes, which altogether allow omics approaches to generate large-scale experimental datasets. This advancement will aid in identification of key regulators of metabolism and enables the eventual manip­ulation of cellular pathways. Synthetic biology combines the use of molecular tools with knowledge gained from systems level analysis of organisms to generate innovative experimental designs. For example, advances in long DNA synthesis make it possible to construct complex genetic circuits designed and informed by metabolic modeling and pathway analyses. With these advances, synthetic biolo­gists have made tremendous progress on the construction of genetic circuits and even entire chromosomes.

The majority of synthetic biology efforts are focused on microbes as many of the most pressing problems, such as sustainability in food and energy production ultimately rely on modification of microorganisms. As such, synthetic modifica­tions of algal strains to enhance desired physiological properties is likely needed to improve their productivity. There has been increasing efforts by synthetic biologists to push for the creation of accessible tools that would improve the potential of algal technology. With synthetic biology, still a young field, the future of this auspicious approach is clearly apparent. While much remains to be achieved to exploit the full potential of algae through various approaches, synthetic biology is likely to play a central role in this process in the coming years.

Acknowledgments Support for this work was provided by New York University Abu Dhabi (NYUAD) Institute grant G1205, NYUAD Research Enhancement Fund AD060, and NYUAD Faculty Research Funds; K. J. was supported through NYUAD Global Affiliate Fellow program; L. Y. was supported in part by NIH R01EB013584, DOD W81XWH-10-10327, and OCRF PPD/ BCM/01.12.

Many strains of microalgae are known to grow either heterotrophically or mixo — trophically. They include Haematococcus pluvialis, Chlamydomonas reinhardtii, Chlamydomonas globosa, Scenedesmus acutus, Selenastrum capricornutum, Scenedesmus bijuja, Ankistrodesmus sp., and many strains of Chlorella (Salim 2013; Ogbonna et al. 1998; Chojnacka and Marquez-Rocha 2004; Chojinacka and Noworyta 2004; Chen 1996). Growth of cyanobacteria can also be enhanced by mixotrophic culture, depending on the carbon source used (Lodi et al. 2005).

However, only a few of these strains that can utilize organic carbon sources accumulate more than 20 % oil under normal growth conditions. The oleaginous strains (those that accumulate more than 20 % oil) include Chlamydomonas, Dunaliella, Botryococcus, Chlorella, Phaeodactylum, Thalassiosira, Nannochlor- opsis, and Isochrysis. Out of these, many are known to be capable of growing heterotrophically. The choice of the strain to be used for heterotrophic or mixo — trophic oil production depends on the oil productivity (a product of the cell growth rate and oil contents of the cells), and the quality of the oils.

Many reports have shown that photoautotrophic and heterotrophic culture conditions result in different biomass and lipid yields for the same microalga strain (Xu et al. 2006; Cheng et al. 2009). Chlorella emersonii and C. protothecoides gave the highest average lipid and biomass yield among many strains of microalgae tested (Suali and Sarbatly 2012). Liang et al. (2009) also reported very high oil productivities with Chlorella vulgaris, while Mandal and Mallick (2009) reported that Scenedesmus obliquus has very high potential for oil production. Heterotro — phically cultivated C. protothecoides was reported to be composed of 40-60 % lipid, 10-28 % protein, 11-15 % carbohydrate, and 6 % ash (Xu et al. 2006; Miao and Wu 2004; Zhang et al. 2008). In view of their lipid content, C. vulgaris, C. protothecoides, and C. zofingiensis were reported as candidates for biodiesel production under photoautotrophic or heterotrophic culture conditions (Liu et al. 2008; Miao and Wu 2006; Hsieh and Wu 2009). Park et al. (2012) found that under mixotrophic conditions, oleic acid is comprised of 41-62 % fatty acid in many strains of microalgae, but in some Chlamydomonas isolates, oleic acid comprised only 9-16 % fatty acid, while palmitic and linoleic acid constituted 47-49 % of the total fatty acid content. Although C. vulgaris and C. minutissima are capable of producing high lipid contents, the triglyceride content is low, making them unsuitable for biodiesel production (Stephenson et al. 2010). The biodiesels pro­duced from some Chlorella species were acid methyl esters, linoleic acid methyl esters, and oleic acid methyl esters (Gao et al. 2009). Unsaturated fatty acid methyl esters comprised over 82 % of the total biodiesel content of Chlorella species (Xu et al. 2006; Cheng et al. 2009). Therefore, the properties of the biodiesel produced from Chlorella comply with ASTM 6751, the US Standard for biodiesel (Li et al. 2007). From various reports on the potentials of several strains of mic­roalgae for oil production, no strain can be selected as the best for biodiesel oil production, since both their oil contents and the composition of the oils vary with culture conditions. The choice, therefore, depends on the culture condition and the composition of the culture medium.

Heterokont algae are a monophyletic group with chloroplasts containing chloro­phyll a and c and the accessory pigment fucoxanthin which gives the group a golden-brown color. Marine, freshwater, and terrestrial heterokonts are known and range in the form of giant kelp (brown seaweeds), diatoms, Eustigmatophytes, and Chrysophytes. The later three groups have species that are high-lipid producers. Nannochloropsis (Eustigmatophytes) are primarily known from marine environ­ments but also occur in fresh and brackish water (Fawley and Fawley 2007). All of the species are small (diameter of about 2-3 pm) non-motile spheres with no distinct morphological features, and many are mixotrophic (Das et al. 2011). Nannochloropsis strains contain 30 % lipids under nutrient-replete growth condi­tions and over 60 % lipid content after nitrogen deprivation (Rodolfi et al. 2009; Huerlimann and de Nys 2010). The Chrysophyceae and Xanthophyceae are predominately freshwater organisms, although a substantial number of xantho — phytes are terrestrial.

Diatoms, widely studied as a feedstock for biodiesel production, are important members of planktonic and attached biofilm communities in both freshwater and marine environments (Round et al. 1990). Over 100,000 species are known which are estimated to contribute up to 45 % of total primary productivity in open oceans (Yool and Tyrrell 2003). Diatoms are distinguished by a unique silica cell wall composed of two separate valves and yellowish brown chloroplasts, surrounded by four membranes and containing the carotenoid pigment fucoxanthin as a photo­synthetic accessory pigment. Other xanthophylls are present as well as P-carotene and chlorophylls a and b. The main storage compounds are lipids (TAGs) and a P (1 ^ 3-linked carbohydrate chyrsolaminarin (Horner 2002). Several genera include species known for high lipid content including Nitzschia, Navicula, Amphiprora, Amphora, and Phaedodactylum (Griffiths and Harrison 2009). Diatoms lack flagella (except in sperm of some species), and their dense cell walls cause them to sink in the water column. Planktonic forms rely on turbulence to keep them in the photic zone, and many species regulate their buoyancy using intercellular lipids. Most diatoms are phototrophic, but a few groups are either obligate heterotrophs or are diurnally heterotrophic in the dark when supplied with a carbon source.

Silicon metabolism has relevance in the culture of diatoms as a feed source for biodiesel production. The silicon-laden cell wall is synthesized intercellularly by polymerizing silicic acid monomers (taken up by transporters from the media) in a specialized membranous compartment (Pickett-Heaps et al. 1990). Because silicate is a relatively expensive and an essential nutrient for diatoms, production costs can be raised significantly. However, silicon limitation prevents cell division and trig­gers rapid lipid biosynthesis which may allow for methods to control oil production in a two-stage production process. Nutrient limitation, including N and P that promote lipid hyper-accumulation in a variety of microalgae, has also been shown to promote lipid accumulation in diatoms (McGinnis et al. 1997). However, several studies suggest that Si deficiency stimulates lipid biosynthesis more rapidly and can result in up to 70 % (dry weight) lipid content (Adams et al. 2013).

Metabolic models build a characteristic description of the cell’s phenotypic state and give insights into systems’ emergent properties with respect to metabolic functions, adaptability, robustness, and optimality. Moreover, a metabolic model serves as a basis to investigate questions of major biotechnological importance, such as the effects of directed modifications of enzymatic activities to improve a desired property of cellular systems (Alper et al. 2005). Reconstruction of genome — scale metabolic models has led to a better systems level understanding of microbial metabolism, bridging the genotype-phenotype gap. The steady increase in the number of new genome-scale metabolic models over the past decade is clear evi­dence of their utility in investigating biological systems for many applications, including those with applicability for pharmaceutical, chemical, and environmental industries (Feist and Palsson 2008).

Soon after the release of Chlamydomonas’s genome sequence in 2007, a number of groups began the reconstruction of metabolic network models for this alga, resulting in the reconstruction of its central metabolic network in 2009 (Boyle and Morgan 2009; Manichaikul et al. 2009). Two years later, genome-scale recon­structed networks of Chlamydomonas were released by two groups independently. Chang et al. (2011) published a genome-scale metabolic network model for C. reinhardtii, iRC1080, describing and accounting for *2000 reactions, *1000 metabolites and over 1000 associated gene products. Dal’Molin et al. (2011) described a slightly smaller genome-scale reconstruction (AlgaGEM), which encompassed *1700 reactions, *900 genes, and *1900 metabolites. Both are constraint-based models that can predict genome-scale reaction fluxes under steady state growth conditions, as well as a wide range of other metabolic outcomes (see Sects. 10.4 and 10.5 for more details on steady state models).

The iRC1080 model allows for quantitative growth prediction for a given light source. This was accomplished by setting up new reactions that treat light as metabolites. More precisely, reactions for the absorption of light by photosystem I and II (as well as other light driven reactions such as vitamin D3 synthesis and photoisomerase) were defined with their wavelength specificities and stoichiome­tries. The introduced light reactions can accept different values corresponding to different light intensities the cell is exposed to. In summary, the absorption of photons drives photosynthesis and other reactions according to specified absorption coefficient, stoichiometry of the absorbed photon, and wavelengths. The described “light reactions” of the model were experimentally validated by photobioreactor growth studies under different light sources and intensities, i. e., photon fluxes, demonstrating the general agreement of actual biomass and oxygen yields with those predicted by the model (Chang et al. 2011).

The aforementioned network models (i. e., iRC1080 and AlgaGEM) can greatly facilitate future developments of network reconstructions for other species of green algae by providing a framework that can be modified according to the alga’s species-specific metabolic properties. We note that nongreen algal groups, such as diatoms which are evolutionarily distant to green algae, are likely to have distinct metabolic processes relative to green algae, differing in metabolic wiring and presence or absence of various subsystems in the network. The reconstruction of metabolic networks for these organisms is likely to require a significant adjustment of the existing green algae models, if these were to be used as the framework.

Post-dewatering, the microalgae biomass can be used directly as a source of animal feed or human food. The cultural and economic development of society has resulted in changes in human lifestyles with developed countries’ diets highly caloric, rich in

saturated fats and sugars, with lower consumption of complex carbohydrates and dietary fibre. This has brought about a greater interest in new foods that can contribute to improve nutritional health and well-being (Plaza et al. 2008). Microalgae are certainly candidates for producing high protein (Spirulina), high carbohydrate (Chlorella) and high essential oil similar to fish oil (Diatoms). Fur­thermore, microalgae biomass can be converted to renewable fuels. The three different pathways that can be used to extract and convert microalgae wet biomass (20 % solid) into bioenergy are summarised in Fig. 1.2. To date, hydrothermal liquefaction seems to be the most energetically positive method for biofuel pro­duction from microalgae (de Boer et al. 2012). However, extensive research and development is still required to determine the most energetically favourable and economically feasible process for extracting and converting the algal biomass for renewable bioenergy.