The advent of computer-aided design in biology holds the promise of greatly increasing the efficiency of biological manipulation and experimentation while easing the process of design and optimization. The hope is that CAD will allow the design and analysis of plasmids, vectors, protocols, and synthetic pathways with a minimal need for laboratory experiments.

Classically, tools used in genetic engineering perform singular specific tasks such as codon optimization, primer design, and ribosome binding site (RBS) design to optimize DNA constructs. A more inclusive set of computational tools combines multiple DNA components’ design and optimization operations via a single toolset. One example of such a tool is ApE (http://biologylabs. utah. edu/jorgensen/wayned/ ape/). ApE is a plasmid editor that, among many things, clearly highlights the different relevant features of a plasmid (restriction sites, ORF, Dam/Dcm methyl — ation sites, etc.), shows the protein translation, creates plasmid graphics and maps, performs a virtual restriction digest, selects sites matching a given criteria, and performs primer design based on an inputted criteria. Other inclusive tools include Gene Designer 2.0 (https://www. dna20.com/resources/genedesigner), which per­forms gene, operon, vector, and primer design along with codon optimization. It also performs protein translation and restriction site modification using a graphical — based interface. A similar toolkit to Gene Designer 2.0 is Gene Design. Gene Design is, however, available via a Web interface (http://54.235.254.95/gd/). Finally, Gene Composer is yet another interesting tool worth mentioning (http:// www. genecomposer. net). Gene Composer is a CAD-based software that allows alignment generation and constructs design, gene optimization, and assembly (Medema et al. 2012; Zabawinski et al. 2001).

As for molecular and synthetic biology centric computational tools, there are already a few software suits and CAD packages that include tools to aid in biological design. One such tool is Genome Compiler (http://www. genomecompiler. com), an advanced genomic design software package. Besides facilitating, viewing, and editing DNA as constructs, protein translation, and circular views, Genome Compiler allows viewing and editing a DNA construct on a functional basis. Additionally, searching and importing DNA constructs via the NCBI database is supported.

A similar CAD toolkit to Gene Compiler is GenoCAD (http://www. genocad. org). GenoCAD, however, has the added benefit of DNA construct simulation. This allows for quick performance testing of designed constructs. Other CAD tools that are focused on DNA constructs and biological parts design include TeselaGen (https:// www. teselagen. com), Clotho (http://www. clothocad. org), and SynBioSS (http:// synbioss. sourceforge. net). Both TeselaGen and Clotho offer genetic function centric DNA editing and construction tools. They also aid in the creation of DNA constructs and parts’ databases and the utilization of existing databases. Likewise, SynBioSS allows for the design and simulation of DNA constructs and biological parts, but does that through reconstructing reaction networks from a series of genetic parts that are user defined and strung together. Another CAD toolkit that allows for DNA construct simulation, in addition to DNA design and editing, is TinkerCell (http://www. tinkercell. com). TinkerCell is built with the ambition of having optimized CAD-based biological designs feed into laboratory automation tools, and thus easing the design, experimentation, and synthesis aspects of molecular and synthetic biol­ogy. Currently, TinkerCell’s analysis capability includes a plethora of deterministic and stochastic simulation and analysis options (Medema et al. 2012).

The CAD tools discussed thus far offer powerful design capabilities, but the designer must keep in mind the limitations inherent with each tool. Biological interactions are not yet fully understood, and the models and simulations of such interactions, as provided by the CAD tools, are limited by the assumptions rooted in those tools.

9.4 Conclusion

Genetic molecular techniques have allowed for tremendous progress in the fields of biology and bioengineering. The ability to manipulate, replicate, and modify DNA, RNA, proteins, and organisms via transformation techniques, cloning, and gene­editing tools has allowed for powerful biological insights and applications devel­opment. As is already apparent, the future of molecular techniques lies in devel­oping more robust editing tools, simplifying high-throughput techniques and adopting more automatable techniques. Also, the development of multiplexing techniques, techniques that are able to perform multiple manipulations at once, will allow for great progress in discovery.

Acknowledgments Major support for this work was provided by New York University Abu Dhabi Institute grant G1205, NYU Abu Dhabi Faculty Research Funds AD060, and NYU Abu Dhabi Research Enhancement Fund AD060; K. J. was supported through NYU Abu Dhabi Global Academic Fellows program. The authors thank Khalid Sam for his help in creating the figures used in this chapter.

Biodiesel is a diesel fuel consisting of mono-alkyl esters of long-chain fatty acids that are generally made by the transesterification of lipids in animal fat or vegetable oils such as soybean, sunflower, rapeseed, and oil palm (Hankamer et al. 2007; Li et al. 2007; Ma and Hanna 1999). As an alternative, microalgae have become popular for the renewable generation of hydrocarbon-based biofuels with high biofuel yields relative to those from plants (Eroglu and Melis 2009; Li et al. 2007).

Immobilization of the hydrocarbon-rich microalgae, Botryococcus braunii and Botryococcus protuberans, in alginate beads yielded a significant increase in the chlorophyll, carotenoids, cellular growth, and lipid contents of the cells during their stationary growth phase (Singh 2003). Bailliez et al. (1985) also observed enhanced hydrocarbon production for the B. braunii cells immobilized in calcium alginate gel as a result of enhanced photosynthetic activity.

In a study by de-Bashan et al. (2002a), C. vulgaris and C. sorokiniana micro­algal cells were individually co-immobilized with A. brasilense growth-promoting bacterium in alginate beads. They found that the presence of the growth-promoting bacterium within the immobilization matrix significantly enhanced the metabolism of Chlorella strains and yielded higher lipid and fatty acid production.

Li et al. (2007) used immobilization technology for the transesterification of algal oils, using immobilized lipase enzyme from Candidia sp. Initially, they grew Chlorella protothecoides cells in large-scale photobioreactors at three different sizes (5, 750, 11,000 L) yielding high lipid contents in the range 44-49 % per dry cell weight. Then, immobilized lipase enzyme from Candidia sp. was used to catalyze the transesterification of the lipids from C. protothecoides, yielding biodiesel production rates of 7.02, 6.12, and 6.24 g L-1 from 5, 750, and 11,000 L biore­actors, respectively. They also highlighted that the quality of this Chlorella biodiesel was comparable to that for conventional diesel fuels (Li et al. 2007).

For microalgae biofuel production, a major cost factor is the provision of water and nutrients (Davis and Aden 2011; Borowitzka and Moheimani 2013), which can both be provided by wastewater. Microalgae ponding systems were developed in the 1950s for municipal sewage treatment (see Oswald 2003 for a review of the early work), and this approach continues to serve as a starting point for the development of cost-efficient algae biomass for fuels production. At least 70 % of the cost of wastewater treatment can be attributed to secondary and tertiary treat­ment. Much of this is due to the energy costs of oxygen transfer in biological secondary treatment and chemical requirements in tertiary treatment.

Dr. William Oswald at the University of California-Berkeley and his colleagues over the following 50 years (Oswald 2003) developed the fundamental engineering design parameters and described the basic biological processes in bioremediation in high-rate ponds. Microscopic algae convert about 2 % of total solar energy to algal biomass. The photosynthetically generated oxygen is consumed by bacterial pop­ulations that decompose organic wastes to simple nutrients including CO2. Although algae-based secondary and tertiary treatment is economically feasible, at least in warm regions with ample land, few municipal algae ponds attempt to control species composition or even harvest the algal biomass (Benemann and Oswald 1996). Two of the persistent problems noted in the early years of inves­tigation were maintaining a stable algal population in the treatment ponds and harvesting the algae in an efficient and economic manner. Open pond cultures are subject to all the variations that occur in natural ponds and lakes. At any given time, there can be a major shift in algal dominance such as a transition from green algae to cyanobacteria (potentially toxic) or there can be a sudden collapse of the com­munity structure due to algae bloom crashes and predation by zooplankton. The sudden death of phytoplankton communities, or algal blooms, is thought to result from several factors including insufficient light for photosynthesis, limiting nutri­ents, phycoviruses (Brussaard 2004), the aging of the blooms, and perhaps by photoinhibition in gas-vaculate cyanobacteria.

Successful open-air algal monoculture is currently limited to a small number of species that can tolerate extremes of pH or salinity that preclude invasion by competing microbes and undesired predators conquering unenclosed outdoor ponds. Enclosed bioreactors mitigate some of the problems of maintaining mono­cultures and predation issues, but capital and labor costs limit their use to pro­duction of high-value products. Other major hurdles to economically feasible algae — based biofuels include the use of high oil strains adapted to the local environment, development of resource-specific production and management systems, and, at least in the short term, coupling algae culture with mitigation of environmental problems and co-production of high-value compounds.

Minimal nutritional requirements for algal growth can be estimated from the approximate molecular formula of algal biomass: C(048) H(183) N(011) P(0.0i) (Grobbelaar 2004). The chemical composition of municipal and dairy wastewaters typically has less N than P relative to algal biomass (Fulton 2009). Although CO2 limits algal growth in high-rate oxidation ponds (Benemann and Oswald 1996), when CO2 is supplemented, N typically limits algal growth on municipal (Bene — mann et al. 1977) and agricultural (Lundquist et al. 2011) wastewaters. N limitation has long been known to be a trigger for lipid synthesis in some algal species. In a variety of microalgae, as the nitrogen or phosphorus levels drop limiting growth, there is a rapid increase in oil (lipid) content (Takagi et al. 2000; Li et al. 2008). The mechanisms involved in the “N-trigger” have remained elusive. Although recently comparative proteomics and transcriptome analysis of the haptophyte Tisochrysis lutea (formerly known as Isochrysis galbana) have revealed a wide variety of proteins and transcripts involved in various pathways including lipid, carbohydrate, amino acid, energy, and pigment metabolisms, photosynthesis, protein translation, stress responses, and cell division in strains are subjected to N limitation (Garnier et al. 2014). Most of the oleaginous algae are non-motile. They are thought to produce lipids as a buoyancy compensator to position them in the ideal part of the water column during the light/dark cycle of photosynthesis. As a survival mecha­nism to counter the depletion of nutrient supplies, they may produce high levels of lipid to bring them to the surface where the wind may blow them to a more nutrient — rich environment. This insight favors the concept of two-phase semi-continuous cultures for wastewater treatment to promote lipid biosynthesis. As the rapidly growing algae take up all the available nitrogen and phosphorus, biosynthetic pathways for growth are nutrient limited and oil content rises.

Figure 6.1 is a flow diagram of a hypothetical algae-based wastewater treatment systems with multiple benefits that take into account the drawbacks described in previous algae-culturing systems. The treatment process is based on a sequence of events designed to yield a high-quality effluent and a consistent supply of algae biomass:

1. Filtered primary wastewater is added to the first of three shallow raceways. There are three raceways: one is filling, one is in the algae growth stage, and the third raceway is in the harvesting stage. A fourth raceway may be needed to account for variations in productivity.

2. The filtered primary effluent is rich in biochemical oxygen demand (BOD), ammonium, and organically bound phosphorus. This effluent is mixed with a high concentration of a pure strain or strains of algae that are grown in a bank of photobioreactors. The algae in the photobioreactors are added to the pond when they are at or near the top of their log-phase growth. The dilution of the concentrated algae into the pond is adjusted to the lower end of early log-phase growth. These ideal strains were selected for their ability to grow rapidly on wastewater, adapted to local waters, high lipid content, and harvestability. In the daylight, the algae produce large quantities of oxygen that facilitate the oxidation of the various organic compounds that contribute to the BOD. During the dark cycle of photosynthesis, the algae can take up a variety of organic compounds including the organic compounds that were degraded during the light cycle of photosynthesis.

Possible Extraction of

Commercially Important

Feedstock for Polymer

be composted to produce a fertilizer and the M Pr°duction or ioplastics

high-nutrient liquid can be used

Fig. 6.1 Flow diagram of an algae-based wastewater treatment system. The boxes with the dashed border indicate an end product or benefit

3. Due to the log-phase growth, in 72-120 h, the algae biomass has increased fourfold to eightfold; the nutrients have been assimilated by the algae, and the BOD has dropped to the level associated with tertiary treatment. The short residence time in the open ponds lowers the likelihood of contamination by another strain of algae or predation by zooplankton mainly by competitive exclusion (Lang 1974; Hillebrand 2011).

4. The algae can be harvested by one of many physical separation processes (for more information of dewatering, see Chaps. 12—14), and the tertiary effluent can be disinfected and prepared for some form of water reuse. At this point, the algae can be ground up to yield a green crude or lysed in order to separate the lipid content from the aqueous and solid fractions.

5. The green crude can be processed into a variety of fuels in the same way the crude oil is processed. If the cells were lysed and just the lipid fraction was isolated, it would then be converted to biodiesel by one of a variety of physical, chemical, or enzymatic processes.

6. The algae biomass could potentially contain a number of valuable phyto­chemicals that can be extracted and purified for use in the chemical or personal care products industry. The biomass could also be used as a feedstock for the production of synthetic polymers.

7. If the previous option is not feasible, the cracked algae cells can be put into a digester to produce methane. Unlike activated sludge, this organic matter has a uniform composition, and since the cells are already lysed, the degradation of the organic matter and subsequent production of methane should be more efficient than digesters filled with activated sludge.

8. The energy demands on this process should be considerably less than tradi­tional secondary and tertiary treatment such that the energy produced by cogeneration could be sold on the grid.

9. The CO2 that is produced by burning the methane is usually released into the air. In order to raise algae in high concentrations, it is necessary to add CO2 to the water. By bubbling in CO2 into the rapidly growing algal cultures, it is possible to capture the majority of CO2 produced by the cogeneration system (for a recent review, see Raeesossadati et al. 2014 and Chap. 7).

10. In the event that carbon “cap and trade” rules are implemented, this process provides a way for substantially shrinking the carbon footprint of a wastewater treatment plant.

11. The solid residue from the digester can be composted to produce a high-quality fertilizer, and the liquid fraction of the digester (mixed liquor) can be sterilized and used as a nutrient source for the bank of photobioreactors.

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.

12.5.1 Metal Salts

Metal salts such as alum (aluminum sulfate) or ferric chloride are among the most widely used flocculants in wastewater treatment. When dissolved in water, alumi­num or ferric iron forms metal hydroxides that are positively charged and can induce flocculation by charge neutralization. At higher doses, the hydroxides form precipitates that cause flocculation through a sweeping mechanism (Wyatt et al.

2012) . Metal salts work well for harvesting microalgae, but the doses required for effective flocculation are very high (usually >100 mg L :). Even higher doses are required in seawater compared to freshwater (Sukenik et al. 1988). Another dis­advantage is that the use of metal salts results in contamination of the harvested biomass with metals.

The process for conversion of the algal feedstock into biofuel dictates the type or types of harvesting methods that can be deployed. For instance, if the value proposition requires the use of residual biomass for animal feeds or valuable co­products, many of the simple settling, flocculation, and flotation methods may not be applicable since they require long time periods and addition of chemicals that could be detrimental to the biofuel and/or co-products.

The different techniques have different costs that must be absorbed by the final product portfolio (biofuel plus associated co-products). The overall cost hinges on the application (i. e., the process deployed by the company producing the biofuel). An in-depth study on harvesting technologies was conducted by the National Alliance for Advanced Biofuels and Bioproducts (NAABB) in 2011. Estimates for capital and operating costs associated with different harvesting techniques are provided in Table 14.2. These numbers were derived either from the NAABB report, literature, or by an engineering analysis and estimate by the authors.

Adjustments were made from the NAABB data to reflect energy and chemical costs on a per volume basis, assuming 0.5 g L 1 dry biomass concentration, which is a reasonable average for phototrophic cultures. In practice, costs for some methods, such as centrifugation, are impacted primarily by volume. Others, such as the chemicals required for chemical harvesting, are dependent on the mass of solids

in the stream. In this way, performance of the growth systems influences the types of harvesting systems that could be desired for the application.

Capital costs were scaled from the NAABB report to 100 m3 h 1 throughput using the 6/10ths rule of thumb for scaling capital costs and are presented as a ±40 % range. A caveat should be noted, many of these technologies are not yet at industrial scale, meaning that the actual industrialized costs could be significantly different than these projections. Particularly, the assumptions for chitosan floccu­lation and electrolytic harvesting stand out as requiring additional data to determine a more accurate scaled-up cost estimate.

Some OpEx components, such as labor and maintenance, were not included in the calculation. Electricity was assumed to cost $0.08/kWh. Hollow fiber members were estimated at a cost of $250/m2 with a 3-year life span, and the nickel alloy membranes were taken at $50/m2 with a 2-year life span. The details of the assumptions for the chemical dosing, cost of chemicals, and supporting data can be found in the NAABB final report (NAABB 2014).

Algal biofuel and bioproduct companies are currently pre-commercial or just recently in commercial-scale production of products based on algae and relatively closed with production data. While it is difficult to ascertain the exact process many of these companies are using for their harvesting technology, one can speculate from their Web sites, patent applications, and discussions with colleagues the probable harvesting methods being applied. Aurora Algae, Inc. (West Perth, Aus­tralia) is growing algae in open ponds photoautotrophically and harvesting using a DAF system supplied with flocculant (http://www. aurorainc. com/technology/ facilities/karratha-facility/). Looking at the Sapphire Energy Green Crude Farm information, it appears they use a DAF system in their process (http://www. sapphireenergy. com/GCFvideo). Heliae (Arizona, USA) uses a spiral plate centri­fuge system from Evodos for their harvesting for the production of astaxanthin (http://www. heliae. com/technology/?page=harvesting). Cellana (Hawaii, USA) uses selection of algae that settle rapidly combined with continuous centrifugation for their harvesting, and they have shifted away from biofuels to higher value products (http://cellana. com/technology/core-technology/). While Cyanotech (Hawaii, USA) is not in biofuels, they have developed a very efficient system for continuous centrifugation and washing over a moving screen in production of Arthrospira. Bioprocessalgae Inc. (Iowa, USA) grows their algae in enclosed systems and biofilms in order to harvest algae by washing down the biofilms and settling (http://www. bioprocessalgae. com/). Algenol (Florida, USA) has developed one of the systems that mostly bypasses the harvest of biomass and, instead, focuses on recovery of product (ethanol) through condensation on the bioreactor cover and processing of the water/ethanol product in their “direct-to-ethanol” process (http:// www. algenolbiofuels. com/direct-to-ethanol/direct-to-ethanol). Joule Unlimited (Massachusetts, USA) is a bit hard to decipher but appears to produce biofuels (e. g., ethanol) and bioproducts in enclosed PBRs and to collect the products directly rather than extraction from biomass (http://www. jouleunlimited. com/).

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.