Category Archives: Biomass and Biofuels from Microalgae

Sequential Heterotrophic-Photoautotrophic Cultures

As previously discussed, mixotrophic cultures have very high potentials for oil production by many strains of photosynthetic organisms. However, optimization of mixotrophic cultures is difficult as the conditions that favor photoautotrophic metabolic activities may not favor heterotrophic metabolic activities. The relative contribution of these two metabolic activities depends on such factors as light intensity, the nature, and concentration of the organic carbon source, and carbon dioxide concentration (Ogbonna et al. 2002a, b). For instance, it has been observed that the assimilation of glucose in a certain strain of Chlorella is suppressed by light, even at low light intensities (Haass and Tanner 1974; Kamiya and Kovallik 1987); therefore, only photoautotrophic metabolic activities are observed under mixotrophic conditions. Sequential heterotrophic-photoautotrophic culture system can be used to overcome such limitations. In this system, the cells are first culti­vated to high densities in heterotrophic cultures, and the condition is then changed to photoautotrophic. This system ensures that the advantages of heterotrophic cultures (such as high cell densities) and those of the photoautotrophic cultures (such as light-induced synthesis of metabolites) are realized. The effectiveness of this culture system has been demonstrated for Chlorella (Ogbonna et al. 1997), Euglena (Ogbonna et al. 1999), and Haematococcus (Hata et al. 2001). Sequential heterotrophic-mixotrophic cultivation, in which light illumination is started before the organic carbon source is completely utilized, has also been investigated for biodiesel oil production (Mitra et al. 2012).

Aeration Rate

Aeration is a key parameter in mass transfer of CO2. Flue treatment productivity in particular will be decreased in low aeration rates, and adequate CO2 concentrations will be required. However, simply increasing CO2 aeration rates does not neces­sarily lead to a higher CO2 fixation efficiency (Li et al. 2011). For example, Fig. 7.5 shows that increasing aeration rates from 0.1 vvm (volume of gas per volume of culture per min) to 0.5 vvm in a S. obliquus WUST4 culture medium resulted in a decreasing CO2 removal efficiency from 67 to 20 % (Li et al. 2011). A comparable result was obtained for C. vulgaris (Fig. 7.6), with the capability of CO2 fixation and O2 evolution decreasing with increasing feed gas flow rates (Fan et al. 2007). Therefore, in low aeration rates, gas retention time increases leading to an increased interface between CO2 and microalgal cells (Fan et al. 2007). One factor may be the influence of bubble coalescence; as it increases with increased flow rates, larger bubbles rise to the surface at a faster rate than smaller bubbles and the bubble surface area per unit of gas volume declines. This leads to decrease in CO2 absorption (Chiu et al. 2009). However, this is far from consistent across the literature, as the opposite can result in which increasing aeration rates improves

Fig. 7.6 Effect of gas flow rates on CO2 fixation and O2 evolution, (T = 25 °C, cell number = 5 x 107 cells mL-1, luminous intensity = 5400 lux, red inner light source, PVDF-1 membrane length = 30 cm, and membrane number = 30). (Reproduced from Fan et al. (2007) with permission)

CO2 removal rates (Ong et al. 2010). For example, the effect of aeration rates from 0.25 to 0.5 vvm on CO2 fixation rate of Chlorella sp. MT-7 and MT-15 is signif­icantly higher than CO2 fixation rate at 0.5 vvm as compared with 0.25 vvm (Table 7.6) (Ong et al. 2010). Furthermore, the effect of different aeration rates (0.001, 0.002, and 0.005 ms-1) and CO2 fixation rates (1.5 g d-1) on the dry weight of C. vulgaris and D. tertiolecta was studied (Hulatt and Thomas 2011). The maximum biomass concentration for D. tertiolecta was obtained at the 0.005 ms-1 gas flow rate and at 12 % CO2 (1.5 g d-1), and 0.005 ms-1 and at 12 % CO2 (1.12 g d-1) for C. vulgaris (Table 7.6) (Hulatt and Thomas 2011). This indicates that improved mass transfer occurs at higher gas flow rates. Therefore, coming to an overall conclusion on the effect of flow gas rates on CO2 fixation rates is compli­cated due to opposite results being reported (see Table 7.6). While it is true that normally decreasing aeration rates lead to CO2 fixation efficiency increase, the opposite results have been obtained. This might arise as a consequence of the various production parameters [biomass concentration, light regime, nutrients, and types of PBRs (Hulatt and Thomas 2011)], and how the individual microalgae species affect each system. Nonetheless, increasing or decreasing aeration rate effectively determines whether CO2 fixation rates will increase or decrease in a microalgal system.

7.3 Conclusion

To achieve economical bioremediation of CO2 emitted from power stations using microalgae requires much research in order to maximize its efficiency and at the same time improve the microalgal biomass productivity at larger scales. Further­more, various microalgae and cyanobacteria species exhibit very different CO2 bioremediation rates and potentials for large-scale production. Results presented in this chapter demonstrated that the most attractive species for environmental CO2 mitigation include S. obliquss, D. tertiolecta, C. vulgaris, Phormidium sp., A. microscopic negeli, and C. littorale. The CO2 removal rate by the aforementioned species will require customization and optimization to meet each system-specific requirements. This chapter has reported on the initial cell concentrations, initial input CO2 concentrations, and aeration rates impact on CO2 bioremediation. In general, increasing initial cell concentrations and decreasing aeration rates lead to increasing CO2 fixation efficiency. It is to be noted that lowering aeration rates lead to a higher CO2 biofixation efficiency because of improved CO2 mass transfer between microalgal cells and the culture medium. Moreover, the input CO2 con­centration influences removal efficiency of CO2, however, providing high levels of CO2 into culture mediums leads to acidification. In contrast, the consumption of CO2 by microalgae through photosynthesis results in pH increase that may impact growth rates of some microalgae species.

Nutrient Removal from Wastewater

Several studies have demonstrated the potential of microalgae for the removal of nitrogen and phosphorus elements from wastewater effluents, with cells taking them up as their nutrient sources. Some of those studies are listed in Table 2.1, while some are further described in the following text. It should be noted that the direct comparison of the nutrient removal efficiencies from various experiments is inherently difficult, because of variations in the initial nutrient concentration, duration of the experiment, pH of the working solution, selected algal species, and the type of immobilization matrix.

The most common algal species used for the removal of nitrates and phosphates are Chlorella, Scenedesmus, and Spirulina. Various open and closed bioreactors have been used for the removal of nutrients by algae, ranging from tubular photobioreactors to corrugated raceways and high-rate algae ponds (Borowitzka 1999; Cromar et al. 1996; Olguin et al. 2003). Increased nutrient removal efficiencies with immobilized algae are usually related, with the dual effect of the enhanced photosynthetic rate of the cells and the ionic exchange between the nutrient ions and the immobilization matrix. Gels which are anionic in nature, such as carrageenan, are usually associated with the adsorption of cations (such as ammonium (NH4+)), while cationic gels such as chitosan yield adsorption of anions (phosphate (PO-3), nitrate (NO3-), nitrite (NO2-)) with higher efficiencies (Mallick and Rai 1994). Moreover, calcium ions of the alginate or chitosan gels are particularly efficient for the precipitation of PO-3 ions from wastewaters (Lau et al. 1997).

Immobilization of C. vulgaris cells within sodium alginate beads showed higher nutrient removal efficiencies from sewage wastewater compared to their externally immobilized counterparts on polyurethane foam (Travieso et al. 1996). de-Bashan et al. (2002b) obtained higher ammonium and phosphate removal efficiencies after co-immobilization of C. vulgaris microalgae with plant growth-promoting bacte­rium Azospirillum brasilense in alginate beads, relative to immobilized C. vulgaris cells alone. Tam and Wong (2000) obtained 78 % ammonium and 94 % phosphate removal efficiencies with immobilized C. vulgaris, entrapped in calcium alginate beads, compared to the 40 % ammonium and 59 % phosphate removal with free cells. Lau et al. (1997) also observed significantly higher ammonium (95 %) and phosphate (99 %) removal efficiencies for C. vulgaris cells immobilized in alginate beads relative to their free counterparts, resulting in only 50 % nitrogen and 50 % phosphate removal. In contrast, free cells of Nannochloropsis sp. cells yielded higher total phosphorus removal with respect to their immobilized cells within calcium alginate beads (Jimenez-Perez et al. 2004).

Pretreatment of the cells by starving them in a saline solution for three days was found to increase the cellular growth and phosphate removal efficiencies of the independently co-immobilized Chlorella sorokiniana & A. brasilense and C. vul­garis & A. brasilense pairs entrapped in alginate beads (Hernandez et al. 2006). Kaya et al. (1995) observed higher nutrient removal rates using S. bicellularis cells when they were immobilized on flat-surface alginate screens compared to their encapsulated form inside alginate beads.

Canizares et al. (1993) used immobilized Spirulina maxima cells in kappa — carrageenan gel beads for nutrient removal from swine waste. This immobilized system achieved around 90 % total phosphorus and ammonium-nitrogen removal, while it also allowed processing swine waste at higher concentrations. Chevalier

Table 2.1 Examples of studies on nutrient removal using immobilized algae

Immobilization matrix

Algal species

Targeted

pollutant

Reference

Alginate beads

Chlorella vulgaris

Ammonium,

phosphate

Tam and Wong (2000)

Nannochloropsis sp.; Scenedesmus intermedius

Total

phosphorous, total nitrogen

Jimenez — Perez et al. (2004)

Chlorella vulgaris and Azospirillum brasilense (co­immobilization)

Ammonium,

phosphate

de-Bashan et al. (2002b)

Chlorella sorokiniana and A. brasilense (co­immobilization)

Phosphate

Hernandez et al. (2006)

Carrageenan beads

Spirulina maxima

Total

phosphorus,

ammonium

Canizares et al. (1993)

Scenedesmus acutus; Scenedesmus obliquus

Ammonium,

phosphate

Chevalier and de la Node (1985)

Agar beads

Chlorella vulgaris; cyanobacterium Anabaena doliolum

Phosphate, nitrate, nitrite

Mallick and Rai (1994)

Alginate beads

Carrageenan beads

Chitosan beads

Chitosan beads

Scenedesmus sp.

Phosphate,

nitrate

Fierro et al. (2008)

Flat-surface alginate screens

Scenedesmus bicellularis

Ammonium,

phosphate

Kaya et al. (1995)

Alginate beads

Filter paper

Trentepohlia aurea

Ammonium, nitrate, nitrite

Abe et al. (2003)

Twin-layer system composed of nitrocellulose membrane, and glass fibers

Chlorella vulgaris, Scenedesmus rubescens

Phosphate,

ammonium,

nitrate

Shi et al. (2007)

Polyvinyl foams

Scenedesmus obliquus

Nitrate

Urrutia et al. (1995)

Polyurethane foams

Alginate beads

Chlorella vulgaris, Chlorella kessleri, Scenedesmus quadricauda

Ammonium,

phosphate

Travieso et al. (1996)

Carrageenan beads

Polystyrene foams

Polyurethane foams

Chitosan nanofibers

Chlorella vulgaris

Nitrate

Eroglu et al. (2012)

Graphene nanosheets

Wahid et al. (2013b)

Graphene oxide nanosheets

Wahid et al. (2013a)

and de la Notie (1985) investigated Scenedesmus acutus and Scenedesmus obliquus cells individually immobilized in kappa-carrageenan beads for nutrient removal from a secondary effluent. Immobilized cells showed similar cellular growth and ammonium or phosphate uptake rates compared to their free-living cell counter­parts. They observed around 90 % ammonium removal within the first 4 h, while all traces of phosphate were removed within 2 h (Chevalier and de la Notie 1985).

C. vulgaris and Anabaena doliolum cells immobilized in chitosan have higher phosphate, nitrate, and nitrite removal efficiencies than when they were immobi­lized within agar, alginate, or carrageenan (Mallick and Rai 1994). In addition, the phosphate removal capacity of the immobilization process was increased when phosphate-deprived cells were initially entrapped within chitosan. Fierro et al. (2008) investigated the nitrate and phosphate removal efficiencies of individually entrapped Scenedesmus sp. cells within chitosan beads. Immobilized cells achieved approximately 94 % phosphate and 70 % nitrate removal within the first 12 h after incubation, whereas by themselves chitosan beads removed 60 % phosphate and 20 % nitrate by the end of the experiment. The reason for yielding a significant phosphate removal rate (60 %) by chitosan beads alone was explained by the increased pH values, which eventually triggered the release of some calcium ions from chitosan polymer, resulting in the precipitation of phosphate ions (Fierro et al. 2008; Tam and Wong 2000).

Other immobilization matrices have also been proposed as alternatives to the gel beads. Immobilized cells of Trentepohlia aurea microalgal cells on a filter paper formed a biofilm layer that reduced the concentration of ammonium, nitrate, and nitrite ions, for around 40 days (Abe et al. 2003). Shi et al. (2007) proposed a twin- layer system, where the microalgal cells are attached on an ultrathin and micropo­rous “substrate layer” composed of a nitrocellulose membrane, which is surrounded by a “source layer” of macroporous glass fiber providing the growth medium (Shi et al. 2007). They observed phosphate, ammonium, and nitrate removal when C. vulgaris and Scenedesmus rubescens microalgal cells were entrapped in this twin — layer system.

In a recent study, C. vulgaris cells immobilized on electrospun chitosan nano­fiber mats yielded an efficient nitrate removal rate (87 %) as a result of the dual action of nitrate removal by the microalgal cells and electrostatic binding of the nitrate ions on chitosan nanofibers (Eroglu et al. 2012). In other studies from the authors’ laboratories, the resulting microalgal composites with multilayer graphene (Wahid et al. 2013b) or graphene oxide sheets (Wahid et al. 2013a) also achieved significant nitrate uptake rates, without being toxic for the microalgal cells.

Biological Contamination of Microalgal Cultures

Contamination of cultures by “weed” microalgae or predators is emerging as a major issue in large-scale cultivation of microalgae (Kazamia et al. 2012; Shurin et al. 2013). Although little is known about dispersal strategies of microorganisms, it is likely that wastewater contains spores of weed microalgae and predators. Contamination will therefore be more difficult to avoid when wastewater is used as a source of nutrients than when a pure culture medium is used. The risk of contamination can be limited by sterilizing the wastewater by micro — or ultrafiltration or by chemical disinfection. The cost of disinfection, however, will probably be too high when microalgae are pro­duced for low-value products such as fuel or animal feed (Wang et al. 2013). Wastewater will likely contain spores or dispersal stages of herbivores of microalgae such as microcrustaceans, rotifers, or ciliates. The microcrustacean Daphnia or the water flea is often an important herbivore in microalgae-based wastewater treatment systems. If Daphnia invades the system, it can reduce microalgal biomass by two orders of magnitude within a few days (Cauchie et al. 2000). Due to its large size, Daphnia can be relatively easily removed by simple screening using a nylon mesh (Borowitzka et al. 1985). Smaller herbivores like rotifers or ciliates can also decimate microalgal biomass within a few days once they have invaded the culture (Schltiter et al. 1987; Moreno-Garrido and Canavate 2001). However, these smaller herbivores cannot be so easily controlled by simple screening.

Some authors have proposed to use a wild consortium of microalgae rather than monospecific cultures for biofuel production. Today, most microalgae-based wastewater treatment systems use such wild consortia rather than monospecific microalgal cultures. Consortia of microalgae may be more resistant to the impact of small herbivores than monospecific microalgal cultures. If a small herbivore invades the culture, small microalgae will be consumed by large microalgal species that cannot be ingested by the herbivore may take over the culture and maintain a high productivity (Shurin et al. 2013). Consortia of microalgae are not only more resistant to herbivores, but may also be more efficient converting nutrients into biomass than monospecific cultures (Ptacnik et al. 2008; Kazamia et al. 2012; Shurin et al. 2013). However, the use of consortia rather than pure algal cultures may pose a problem for the valorization of the resultant biomass. First, if the consortia contain toxic species such as cyanobacteria, the biomass cannot be used for food or animal feed. Second, it is more difficult to control the biochemical composition of the biomass in mixed consortia than in pure cultures. Some species in the consortium may produce carbohydrates, for instance, while others produce lipids. The consortia that occur in wastewater treatment systems are often domi­nated by a few freshwater microalgal species, very often chlorophytes (Chlorella, Scenedesmus, Micractinium, Pediastrum) (Pittman et al. 2011). Some control over community composition is possible by recycling part of the harvested biomass. In a long-term study in a HRAP, recycling of the harvested biomass resulted in a 90 % dominance of the community by the large species Pediastrum, which improved the harvestability of the biomass (Park et al. 2011b).

Molecular Genetic Techniques for Algal Bioengineering

Kenan Jijakli, Rasha Abdrabu, Basel Khraiwesh, David R. Nelson, Joseph Koussa and Kourosh Salehi-Ashtiani

Abstract The uniquely diverse metabolism of algae can make this group of organisms a prime target for biotechnological purposes and applications. To fully reap their biotechnological potential, molecular genetic techniques for manipulating algae must gain track and become more reliable. To this end, this chapter describes the currently available molecular genetic techniques and resources, as well as a number of relevant computational tools that can facilitate genetic manipulation of algae. Genetic transformation is perhaps the most elemental of such techniques and has become a well-established approach in algal-based genetic experiments. The utility of genetic transformations and other molecular genetic techniques is guided by phenotypic insights resulting from forward and reverse genetic analysis. As such, genetic transformations can form the building blocks for more complex genic manipulations. Herein, we describe currently available engineered homologous recombination or recombineering approaches, which allow for substitutions, inser­tions, and deletions of larger DNA segments, as well as manipulation of endogenous DNA. In addition, as reagent resources in the form of cloned open reading frames (ORFs) of transcription factors (TFs) and metabolic enzymes become more readily available, algal genetic manipulations can greatly increase the range of obtainable phenotypes for biotechnological applications. Such resources and a few case studies are highlighted in the context of candidate genes for algal bioengineering. On a final note, tools for computer-aided design (CAD) to prototype molecular genetic tech­niques and protocols are described. Such tools could greatly increase the reliability and efficiency of genetic molecular techniques for algal bioengineering.

Kenan Jijakli and Rasha Abdrabu contributed equally to this work.

K. Jijakli

Division of Engineering, New York University Abu Dhabi,

P. O. Box 129188, Abu Dhabi, United Arab Emirates

R. Abdrabu • B. Khraiwesh • D. R. Nelson • J. Koussa • K. Salehi-Ashtiani (H) Division of Science and Math, Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi, P. O. Box 129188, Abu Dhabi, United Arab Emirates e-mail: ksa3@nyu. edu

© Springer International Publishing Switzerland 2015

N. R. Moheimani et al. (eds.), Biomass and Biofuels from Microalgae,

Biofuel and Biorefinery Technologies 2, DOI 10.1007/978-3-319-16640-7_9

9.1 Introduction

Microalgae have been described as nature’s very own power cells and can provide alternatives to petroleum-based fuels without competing with food crops (Dismukes et al. 2008; Singh et al. 2011). The heterogeneity and diversity that algae evolved make the molecular mechanisms that different algae have adopted, along with manipulating those mechanisms of tremendous interest. Currently, research is being conducted to develop methods for genetic modification to introduce desirable traits into algae and to develop synthetic biology approaches to re-engineer algal cells (Ferry et al. 2011; Gimpel et al. 2013; Rabinovitch-Deere et al. 2013). The crux of this research is to advance the molecular biology techniques utilized for algae and to ease the modification of the molecular systems of the species of interest.

One powerful example is the alga Chlamydomonas reinhardtii. As a single — celled alga containing a single large chloroplast, C. reinhardtii represents typical soil green algae. Moreover, Chlamydomonas combines powerful genetics with the availability of unique genetic and genomic resources. All three genomes (nuclear, plastid, and mitochondrial) have been fully sequenced (Merchant et al. 2007); large mutant collections have been established; and all three genomes are amenable to genetic manipulation by transformation (Hippler et al. 1998; Neupert et al. 2009). Most tools required for systematic functional genomics studies are available in Chlamydomonas, including high-frequency transformation protocols (Kindle 1990), efficient methods for chemical and insertional mutagenesis (Dent et al. 2005), and workable protocols for RNA interference (RNAi) (Arif et al. 2013; Molnar et al. 2007; Zhao et al. 2007). Overall, this represents a great advance in the molecular techniques and methods, especially with their applications to algae.

In the absence of cell differentiation, some algae such as Chlamydomonas can provide a much simpler system for genetic manipulations compared with higher plants. Manipulation of microalgae by metabolic and genetic methods would both permit (1) selection of beneficial pathways redirecting cellular functions toward the synthesis of preferred products and (2) introduction of non-algal genes for the generation of algal recombinant proteins. The selection of favorable pathways may include increased resistance to environmental or stress changes on the culturing and life cycle of the algae, expedited biomass production, and excretion of valuable products. The potential of such system remains to be optimized as an alternative protein expression system.

In light of all these potentials, and particularly during the past two or three decades, algal biotechnology grew steadily into an important global industry with new entrepreneurs realizing the potential of algae. However, creating profitable industries out of microalgae still remains challenging, and perhaps the development of new molecular techniques might expedite microalgae’s full industrial develop­ment, especially that some microalgal classes have highly complex genetic compositions rendering their modification arduous: Microalgal genome sizes range from 12.6 Mbp in Ostreococcus tauri, a Chlorophyta member, to 168 Mbp in the

Haptophyta Emiliania huxleyi and up to an impressive 10,000 Mbp for the Dinophyta Karenia brevis (Cadoret et al. 2012). Currently, a genome size of 10 Gbp precludes full genome sequencing, and as such, a lesser extent of knowl­edge would be available rendering the modification of such organisms a highly demanding task.

Use of Mixed Cultures

Depending on the type of reactor and the substrate used, it can be difficult to maintain monocultures of high-lipid-producing strains. For example, mixotrophic cultivation in open ponds, using wastewater as the nutrient source, will result in a significant reduction in the cost of production. However, under such conditions, monocultures of oleaginous strains are likely to be outcompeted by faster growing species of microalgae or cyanobacteria. Therefore, it is important to explore the use of naturally occurring mixed cultures in wastewaters. Lipid content of mixed cul­tures in municipal wastewater was reported to be 11.3 %, and as high as 29 % when grown with anaerobic digester effluent (Woertz et al. 2009). Griffiths (2009) reported a fatty acid methyl ester content of as high as 23.4 % after in situ transesterification of a mixed culture grown in municipal wastewater.

Mixed cultures of selected strains of microalgae, as well as mixed cultures of microalgae and yeasts have also been investigated. Mixed cultures can be designed for efficient absorption of light (combining strains with different light absorption spectrum) or better nutrient utilization (combining strains with different nutrient preference). Competition for nutrients can lead to a limitation of nutrients, such as P and N, which can induce oil accumulation. Mixtures of Chlamydomonas and Chlorella, Scenedesmus and Chlorella, or a combination of three strains Chla­mydomonas, Chlorella, and Scenedesmus were investigated for efficient oil pro­duction (Bhatnagar et al. 2011). In a mixed culture of Chlorella and yeast under mixotrophic cultivation with molasses as the carbon source, the biomass, lipid content per cell, and lipid productivities were higher than the values obtained in a monoculture of either of the strains (Leesing et al. 2012).

Ecology of Microalgae and Preferred Strains for Use in Wastewater Treatment

The extensive work carried out by the DOE/NREL sponsored Aquatic Species Program to explore large-scale algae production for biodiesel provided a number of recommendations for further work. Key among these was to isolate native, local strains for mass cultivation to ensure adaptation to local seasonal conditions. Currently, there are widespread efforts in the field to screen large numbers of algal strains for lipid production from widely differing natural habitats. Most of these are unlikely to thrive in the specific conditions of algae production ponds (water quality, nutrient sources, mixing, large seasonal light and temperature changes, grazing zooplankton, etc.), as discovered during the Aquatic Species Program. It is well known from limnology studies (McCormick and Cairns 1994) and municipal ponding operations (Benemann et al. 1977; Murry and Benemann 1980) that certain algae strains dominate the population at different seasons and in response to nutrient and other environmental parameters. An ecologically sound approach is to identify seasonally dominant strains in outdoor ponds at a specific site and with regard to local environmental factors and the desired objectives (i. e., high-lipid strains for biodiesel, bioremediation, value-added co-products, etc.).

Algal strains that have been identified for their high oil content and suitability for mass production include the following: Botryococcus braunii, Dunaliella tertio — lecta, Euglena gracilis, Isochrysis albana, Nannochloris sp., Neochloris oleo — abundans, Phaeodactylum tricornutum, Chrysotila carterae, Prymnesium parvum, Scenedesmus dimorphus, Tetraselmis chui, and Tetraselmis suecica (Oilgae 2009 and Chap. 1 of this book). Unfortunately, many of these species are adapted to waters with far higher salinity and grow very slowly in freshwater, while others tolerate wide ranges in salinity. Genera commonly found in wastewater ponds include the following: Chlorella (Ponnuswamy et al. 2013), Chlorococcum, Mi­crocystis and Phormidium (Mahapatra and Ramachandra 2013) Chlorella, Euglena and Selenastrum (Ojala et al. 2013) and Scenedesmus, Ankistrodesmus, Micrac — tinium, Oocystis, Phytoconis, Chlamydomonas, Oscilitoria, and Synechocystis (De Pauw and Van Vaerenbergh 1981). In his work on dairy wastewater treatment, Woertz et al. (2009) worked with batch cultures that were dominated by Scene — desmus, Micractinium, Chlorella, and Actinastrum. Because these strains thrive in wastewater operations, they are prime candidates for bioremediation-biofuel production.

Three final points of consideration for selecting the ideal algal strains would be the ease of harvesting, the ease of cell lysis, and/or lipid extraction and the ability to grow under autotrophic and heterotrophic conditions. While the cyanobacterium Microcystis proliferates in wastewater ponds, its small size (2-5 microns) would make it difficult to harvest. Various strains of Chlorella have been studied for their production of oil, but one of the main impediments in dealing with this genus is its resistance to cell lysis (Gerken et al. 2013; Zheng et al. 2011). Given the consid­erations of algae’s ability to grow in wastewater, be relatively easy to harvest and lyse, and contain a significant amount of oil, the candidate genera would include Euglenia, Scenedesmus, Selenastrum, Chlamydomonas, and Actinastrum. Species of the genus Chlorella can be added to this list with the understanding that future technologies will overcome the challenge of breaking open the cell walls. A con­sortium of two to five of these types of algae along with epiphytic wastewater bacteria could be the essential components of an algae-based wastewater treatment system that yields a high-quality effluent and a significant amount of triglycerides that can be converted into biodiesel or chemicals of industrial significance.

Reconstruction of Genome-scale Metabolic Network Models

How are metabolic network models reconstructed? A metabolic network consists of metabolites, biochemical reactions, and the relevant genomic evidence for the described enzymatic reactions, or gene-protein-reactions (GPRs) associations. The structural framework of a genome-scale metabolic model begins with compilation of relevant gene annotations and ends with refinement of the reconstructed meta­bolic network. This reconstruction process passes through four blocks (Fig. 10.3) (Orth et al. 2010; Thiele and Palsson 2010) as described in the sections below.

10.4.1 Draft Reconstruction

In this step (Fig. 10.3a), stoichiometric reactions that can describe cellular metab­olism using various sources of information are compiled. These data may be col­lected from different knowledgebases, including BiGG (http://bigg. ucsd. edu) (Schellenberger et al. 2010), KEGG (http://www. genome. jp/kegg/), MetaCyc (http://www. metacyc. org), and peer reviewed literature. Through genomic and bioinformatics approaches, the functional annotations of open reading frames (ORFs) provide genomic evidence for the presence of specific biochemical reac­tions, associating genes, or multiple genes with specific reactions in the network, for generating the gene-protein-reaction associations. Gene products are associated with specific reactions through assigned enzyme commission (EC) numbers. This process may include sequence-based searches of the ORFs against well-curated databases such as UniProt (http://www. uniprot. org) (Apweiler et al. 2004) or through profile-based scans (e. g., InterPro, http://www. ebi. ac. uk/interpro/) (Jones et al. 2014) to assign enzymatic function and EC numbers to the ORFs.

Model Refinement

Fig. 10.3 Metabolic model reconstruction and refinement. a Information from one or more knowledgebases is extracted to define reactions and pathways to reconstruct a draft model; b the draft network model is transformed into a stoichiometric matrix that maps the metabolites and the associated reactions; c the obtained mathematical representation of metabolism is constrained with key flux parameters and can be optimized for an objective function. The obtained optimal solution is then validated by experimental data; differences between the two are reconciled by refining the initial model through filling gaps, adding and removing metabolites, obtaining additional experimental evidence

With enzymatic functions assigned, metabolic reactions can be defined which in turn allows reconstruction of a draft metabolic network. The draft network also accounts for metabolites that contribute to biochemical reactions inside the cell.

Novel Methodologies

1.3.1 Conversion of Solar Energy to Biomass and Electricity

Photosynthesis is the driving mechanism behind microalgae biomass production but only requires a small fraction of the incident solar energy, primarily in the blue and red portions of the solar spectrum. In conventional cultivation of microalgae, the remainder of the incident solar energy simply heats the algae ponds, causing the water in them to evaporate and increase salinity which is a significant problem in biomass production. With microalgae cultivation often occurring in hot, semi-arid locations, this incidental heating is essentially a waste of the solar energy. Instead, it would be advantageous to be able to capture this unused portion of the solar spectrum and convert it to electricity for use at the cultivation site (Moheimani and Parlevliet 2013).

Figure 1.3 illustrates how the solar spectrum can be divided between the growth of microalgae and the production of electricity by a photovoltaic device (solar cell). Irradiance falling on the Earth’s surface is well defined in the standard ASTM G-173-03 (ASTM 2008). This is the AM1.5 solar spectrum as shown in Fig. 1.3. Of this spectrum, only a fraction is used by photosynthesis by a microalgae culture. Some 48.7 % of the incident solar energy is considered to be photosynthetically active radiation (PAR) in the region between 400 and 700 nm (Zhu et al. 2008). However, it is clear from the absorption spectra of Nannochloropsis that some parts of the spectrum are absorbed more strongly than others. As such, the growth and performance of photosynthetic organisms are strongly linked to the quality and quantity of available light (Lindstrom 1984; Smith 1983) with only some parts of the spectrum being used in photosynthesis. In comparison, highly efficient crys­talline silicon solar cells can absorb light strongly across the solar spectrum as shown by the spectral response of a PERL cell (Zhao et al. 1996) shown in Fig. 1.3. This suggests that although these consumers of solar energy (microalgae and solar cells) would appear to compete for the same resource, if the irradiance could be split between the two, the full utilisation of the solar spectrum would be possible. The shaded regions in Fig. 1.3 illustrate the portions of the solar spectrum that can be delivered to electrical generation and to microalgae cultivation without reducing the productivity of the microalgae. This would allow the production of biomass and electricity from the one facility.

The concept of the coproduction of electricity and agricultural production has been previously used in photovoltaic greenhouses. These are a building integrated photovoltaic system whereby solar modules are integrated into the structure of the building (Panda et al. 2011). Photovoltaic greenhouses use photovoltaic modules in the parts of the greenhouse whereby any reduction in overall PAR would not alter the growth of the plants, while the use of semi-transparent or opaque elements on the greenhouse can reduce the PAR and result in decreased productivity (Perez — Alonso et al. 2012). This would be due to a reduction in the irradiance the plants required for photosynthesis. To overcome this issue, we propose the use of a

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Fig. 1.3 Splitting the solar spectrum for the coproduction of biomass and electricity

semi-transparent solar module that is specifically designed to transmit the irradiance required by the microalgae and convert the remainder to electricity via a photo­voltaic system. This solar module or filter can be located above the microalgae ponds (Moheimani and Parlevliet 2013).

There are a number of advantages to this system. By reducing the total irradiance incident upon the microalgae pond, the temperature of the culture would be reduced which would result in lower evaporation and a more stable salt content in the pond. As the microalgae are still receiving the portion of the spectrum required for photosynthesis, there would be no reduction in productivity. The electricity gen­eration by the photovoltaic aspects of the system can be used on site to power motors and electronic systems to reduce the running costs of a facility. Alterna­tively, the electricity can be used to power additional lighting to increase the period of illumination on the microalgae or to increase the irradiance in specific parts of the solar spectrum. Using additional lighting powered by the otherwise wasted portions of the solar spectrum can increase the productivity of the microalgae. The style of system we have proposed (Moheimani and Parlevliet 2013) can improve the via­bility of microalgae growth for industrial purposes.

Sequential Photoautotrophic-Mixotrophic Cultures

A major disadvantage of mixotrophic cultures is contamination, especially when open systems are used. Many species of bacteria have much higher growth rates than photosynthetic microorganisms. Thus, they can easily outcompete and out­grow the photosynthetic cells. Although many antibacterial substances can be used to inhibit bacteria growth, their effectiveness depends on the nature of the micro­algae. To minimize the risks of contamination, the cells can be cultivated photo- autotrophically to a relatively high cell concentration before switching to mixotrophic culture by the addition of an organic carbon source. Thus, the high concentration of microalgae before the addition of the organic carbon source will enable the cells to outcompete the initial low concentration of the contaminant. Furthermore, a shorter period of the mixotrophic culture can be applied so that cultivation is terminated as soon as concentration of the contaminants reaches a critical level. There are many reports on sequential photoautotrophic-mixotrophic cultures; for example, Fernandez Sevilla et al. (2004) employed it for cultivation of P. tricornutum UTEX 640, using both bubble column and airlift photobioreactors. The photoautotrophic phase lasted until a cell concentration of 3.5 g/L was reached; it was then switched to mixotrophic condition by the addition of glycerol under limited nitrogen, during which the cell concentration increased to 25.4 g/L. Yen and Chang (2013) similarly reported higher biomass concentration in sequential pho — toautotrophic-mixotrophic cultures; however, the linoleic acid content (18:1) was the same when compared with photoautotrophic cultures. Das et al. (2011) studied intracellular lipid accumulation by Nannochloropsis sp. in sequential photoauto — trophic-mixotrophic cultures. Photoautotrophic culture was used for 7 days fol­lowed by 3 days of mixotrophic culture, using either glycerol, glucose, or sucrose as the organic carbon source, resulting in a 72 % increase in lipid productivity when compared with the photoautotrophic culture.

4.2 Conclusions

Despite efforts to improve the productivity of photoautotrophic cultures, biodiesel oil productivity is still very low because of the low innate light conversation effi­ciencies of the microalgae. Large-scale photobioreactors with high light supply efficiencies remain technically challenging from a design perspective, and thus, the cost of microalgae biodiesel remains very high. The photoautotrophic microalgae cultures have several productivity limits, thus making it difficult to further reduce the cost of biodiesel oil production in photoautotrophic cultures. One solution is to exploit the ability of some strains of microalgae to metabolize organic carbon sources both with and without light. Depending on the strain, facilities available, technical know-how, and other culture conditions, the various culture systems discussed in this chapter present a viable alternative to photoautotrophic cultures in biodiesel production. For example, culture systems employing heterotrophic metabolism can be used to improve both the productivity and quality of oils pro­duced by microalgae by increasing the relative composition of triglyceride oils with high oleic acid contents, which are a better substrate for the production of biodiesel.