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).

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.

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.

After flocculation, the flocculated microalgae can be separated from the culture medium by sedimentation. The efficiency of the entire flocculation-sedimentation process depends on the flocculation efficiency as well as the settling rate of the flocs and the volume of sludge that is formed. Most academic research on microalgae flocculation so far has focused mainly on the flocculation efficiency and thus evaluated mainly whether a novel flocculation technology is capable of inducing significant flocculation of the microalgae in suspension. When applying floccula­tion in large-scale systems, however, the technology should also be capable of generating flocs that settle rapidly and produce a small sludge volume. Properties such as floc size, settling rate, and sludge volume can differ substantially between different microalgae species and different flocculation methods (Fig. 12.5). Some flocculants like chitosan generate large flocs with a high sedimentation rate while other methods like alum produce small flocs that settle more slowly (Vandamme et al. 2014b). Flocculation by cationic starch generates a small sludge volume and allow for a 50-fold concentration of the biomass, while flocculation by chitosan generates a large sludge volume and allows only for a 20-fold concentration. The flocculation conditions or dosage can also influence flocculation process. For example, when applying autoflocculation by magnesium hydroxide in seawater medium, overdosing of base may result in massive precipitation of magnesium hydroxide and will result in a very large sludge volume (§irin et al. 2011; Besson and Guiraud 2013; Garcia-Perez et al. 2014).

Fig. 12.5 Illustration of differences in the size and shape of flocs, and volume of sludge produced by different flocculation methods: a aluminum sulfates, b electro-coagulation — flocculation using aluminum anodes, c cationic starch, d chitosan, and e pH-induced autoflocculation. The same culture (Chlorella vulgaris, 0.5 dry weight L-1, Wright’s cryptophyte medium) was used for each flocculation method (Vandamme et al. 2014a, b)

Subsequent to flocculation, sedimentation is generally used to separate the flocs from the culture medium. Sedimentation in a batch tank or a flow-through settling basin is relatively inefficient because these tanks are relatively deep and require a long settling times. As a result, a large area is required for the settling tanks. In inclined settlers or lamella separators, the settling depth is reduced and the surface area for settling is increased, resulting in a much smaller settling tank footprint (Smith and Davis 2013; Wang et al. 2014). Flocs can also be separated from the culture medium by means of flotation (suspended air flotation or dissolved air flotation). Flotation generally results in a smaller sludge volume than sedimentation (Kwon et al. 2014). Membrane filtration can also be used to separate concentrate flocs into a slurry that can be further dewatered (Gerardo et al. 2014).

After pre-concentration of the biomass by means of flocculation followed by sedimentation or flotation, the algal slurry that is obtained should be further dewatered using a mechanical method. This will require transport of the slurry from a settling or flotation system to a centrifugation or filtration system. Because transport and processing of microalgal slurries generally require pumping, changes in viscosity, and rheological behavior as a result of up-concentration of the biomass should be taken into account. As long as biomass concentrations remain below 2 %, microalgal slurries display a Newtonian behavior. When biomass concentrations exceed 4 to 8 %, however, shear thinning may occur in slurries of some species of microalgae (Wileman et al. 2012). Few studies have investigated the rheological properties of microalgal slurries that are produced by flocculation. Sirin et al. (2013) noted that pH-induced flocculation had no significant effect on the viscosity of the microalgal slurry that was obtained.

Solar cells manufactured from doped crystalline silicon solar cells are among the most widely recognized varieties of solar cells. Crystalline silicon solar cells made up about 86 % of the market in 2011 (Fraunhofer 2012). Currently, state-of-the-art single crystal silicon solar cells are reaching a conversion efficiency of up to 24.7 % (Beardall et al. 2009). A similar 24 % efficient (when measured under AM1.5 at 25 °C) passivated emitter, rear locally diffused (PERL) solar cell has been reported with a conversion efficiency of up to 46.3 % (Zhao et al. 1996). This was under monochromatic light of 1040 nm (Zhao et al. 1996). The broad spectral response of this solar cell can be seen in Fig. 15.3.

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

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.

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.

Basel Khraiwesh, Kenan Jijakli, Joseph Swift,

Amphun Chaiboonchoe, Rasha Abdrabu, Pei-Wen Chao, Laising Yen and Kourosh Salehi-Ashtiani

Abstract Synthetic Biology is an interdisciplinary approach combining biotech­nology, evolutionary biology, molecular biology, systems biology and biophysics. While the exact definition of Synthetic Biology might still be debatable, its focus on design and construction of biological devices that perform useful functions is clear and of great utility to engineering algae. This relies on the re-engineering of bio­logical circuits and optimization of certain metabolic pathways to reprogram algae and introduce new functions in them via the use of genetic modules. Genetic editing tools are primary enabling techniques in Synthetic Biology and this chapter dis­cusses common techniques that show promise for algal gene editing. The genetic editing tools discussed in this chapter include RNA interference (RNAi) and arti­ficial microRNAs, RNA scaffolds, transcription activator-like effector nucleases (TALENs), RNA guided Cas9 endonucleases (CRISPR), and multiplex automated genome engineering (MAGE). DNA and whole genome synthesis is another enabling technology in Synthetic Biology and might present an alternative approach to drastically and readily modify algae. Clear and powerful examples of the potential of whole genome synthesis for algal engineering are presented. Also, the development of relevant computational tools, and genetic part registries has stim­ulated further advancements in the field and their utility in algal research and engineering is described. For now, the majority of synthetic biology efforts are

B. Khraiwesh • A. Chaiboonchoe • R. Abdrabu • K. Salehi-Ashtiani (H)

Division of Science and Math, and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi, 129188, Abu Dhabi, United Arab Emirates e-mail: ksa3@nyu. edu

K. Jijakli

Division of Engineering, New York University Abu Dhabi, 129188, Abu Dhabi United Arab Emirates

J. Swift

Department of Biology, New York University, 12 Waverly Place, New York 10003, USA P.-W. Chao • L. Yen

Department of Pathology and Immunology, Department of Molecular and Cellular Biology, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA

© 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_8 focused on microbes as many pressing problems, such as sustainability in food and energy production rely on modification of microorganisms. Synthetic modifications of algal strains to enhance desired physiological properties could lead to improvements in their utility.

8.1 Introduction

More basic research is needed before algal biotechnology can reach a capacity to compete with other systems (Stephen and Joshi 2010). Because many physiologi­cal, morphological, biochemical, or molecular characteristics of algae are quite different from higher plants or animals, algae can meet several requirements that other systems cannot (Hallmann 2007). The demand for improved systems of production of nutraceuticals and cost-effective protein expression systems (both industrial and pharmaceutical applications) lend themselves to explore the potential useful capacities of algae (Hallmann 2007).

Synthetic biology is the design and construction of biological devices and systems for a specific purpose (Ferry et al. 2012). This is an area of biological research and technology that combines biology and engineering, thus often over­lapping with bioengineering and biomedical engineering (Serrano 2007). It encompasses a variety of different approaches, methodologies, and disciplines with a focus on bioengineering and biotechnology. Through the innovative re-engineering of biological circuits and optimization of certain metabolic path­ways, biological modules can be designed to reprogram organisms to produce products, or exhibit desired metabolic behaviors (Khalil and Collins 2010). Synthetic biology can enable model transferability to address a multitude of industrial needs and projects (Anderson et al. 2012). Researchers in this field are realistically optimistic that synthetic biology can provide solutions to a multitude of worldwide problems from health to energy (Purnick and Weiss 2009). The success of synthetic biology as a promising approach is demonstrated by a number of successful attempts in constructing microbial strains to lower production cost of pharmaceutical products (Khalil and Collins 2010). Similarly, there are ongoing works to introduce desirable traits into algae, and to re-engineer algal cells (Ferry et al. 2012; Gimpel et al. 2013; Rabinovitch-Deere et al. 2013).

Although sometimes referred to as genetic engineering, synthetic biology differs in terms of scale, scope, techniques of manipulation, and application (Serrano

2007) . Genetic engineering focuses on the alteration of a single characteristic of an organism through transgenic hybrids or genetic chimeras that carry altered genes, or genes from other organisms. In contrast, synthetic biology seeks to reconfigure, design, and construct new pathways, whole processes, or novel systems for the purpose of achieving some desired biosynthetic activity or phenotype (Alper and Stephanopoulos 2009; Khalil and Collins 2010).

Microalgae exhibit enormous biodiversity, and have the potential for producing large quantities of biomass containing high concentrations of lipids (Gimpel et al.

2013) . Synthetic, systems, and post-genomics biology are terms that are increas­ingly encountered in the biofuels and biotechnology research space with all such approaches likely to be deployed to enable algal biofuels to become economically competitive with fossil fuels. In order to create a viable technology, the field of synthetic biology has been moving towards modular genetic systems. Modularity encompasses the reliance on standardized genetic parts and circuitry models—just like the field of electronics and electric circuits relies on standard collections of resistors, transistors, and capacitors (Fig. 8.1).

RNA-mediated silencing and targeted genome editing tools, including artificial microRNA (Khraiwesh et al. 2011), RNA scaffolds (Delebecque et al. 2012), TALENs, and RNA guided Cas9 endonucleases (Gaj et al. 2013), have allowed synthetic biology to develop gene circuits designed to perform specific functions, often by combining components from multiple organisms to generate novel func­tionality. These new research endeavors will undoubtedly increase our knowledge and usage of these important primary-producing organisms.

Characteristics such as high photon conversion efficiency, fast growth rate, high growth density, high oil/carbon content, ease of harvesting and high pathogen/ predator resistance all represent aspects of importance for the development of high efficiency microalgal production strains. So far, however, there have not been any reports of a single species that is able to meet each of these criteria. The importance of microalgae bioprospecting and breeding, apart from establishing a solid basis for high efficiency strain development, lies in the identification of novel biological mechanisms and algal systems which can be exploited by genetic engineering.

Ideally, these will form libraries of traits, which in combination with tools to conduct species-specific engineering will enable strain customisation. In this con­text, it is of note that algae possess three genetic systems: the nuclear, the mito­chondrial and the plastid genome, each of which may be genetically manipulated.

The green alga Chlamydomonas reinhardtii is arguably the most widely used model alga, at least in terms of fundamental biology; its physiology is well described, multiple mutants exist, all three of its genomes have been sequenced (Maul et al. 2002; Merchant et al. 2007; Popescu and Lee 2007), and a range of molecular tools have been developed to facilitate its genetic engineering. It contains one chloroplast with a 203 kb circular genome encoding about 100 genes (Maul et al. 2002). The chloroplast genome of C. reinhardtii is AT-rich and highly polyploid with a copy number of about 50-80 copies per chloroplast (Maul et al. 2002). With this high genome copy number, the chloroplast has been the choice of heterologous protein production since protein levels of over 40 % of total soluble protein (TSP) can be achieved (Surzycki et al. 2009). The nuclear genome of C. reinhardtii has a high GC content and frequent repeat regions. Protein accumulation is often lower compared to expression in the chloroplast, and transgenes can often be silenced. However, transgene expression from the nucleus offers several advantages such as inducible expression, post-translational modifications and het­erologous protein-targeting to various compartments within the cell and secretion. The mitochondrial genome of C. reinhardtii is also polyploid with around 50-100 genome copies organised in about 20-30 nucleoids (Hiramatsu et al. 2006). It consists of a 15.8 kb linear DNA molecule which has been fully sequenced, and telomeres corresponding to inverted repeats of ~500 bp are located at each end, with 40-bp single-stranded extensions (Vahrenholz et al. 1993). Compared with the mitochondrial genome typically found in vascular plants, it is extremely compact with 14 genes encoding in total only eight proteins (Remacle et al. 2006) and three tRNAs. The low tRNA content suggests that cytosolic tRNAs are imported, a process known to take place in plant and human mitochondria, making it an interesting potential model system for more detailed process analysis (Remacle et al. 2012).

14.2.2.1 Screening/Macrofiltration

A more rapid and low-cost method uses screens or filters with wide gauge pores to retain the algae and allow the medium to flow through the screen without excessive clogging. Dead-end type filters and filter presses are examples of macrofiltration technology. This harvesting method is applicable for a limited number of algae that are of large size and filamentous, properties that allow them to not form imper­meable cakes, clog the screens, or flow through the pores. Large pore screens can be used to reliably capture a large percentage of the available biomass (Shelef et al. 1984). The process is widely used for larger, filamentous algae like Spirulina with rotating screens and back flushing, but is unlikely to be economical for non-food applications (Packer 2009). Unfortunately, most currently utilized microalgal pro­duction strains are non-filamentous and cannot be harvested in this manner; their small cell size necessitates the use of microfiltration or alternative separation methods. The addition of filtering aides, such as diatomaceous earth, sand or cel­lulose, can improve the effectiveness of macrofiltration methods, but not without adding significant cost and downstream processing complications.