There are two main types of microalgae cultivation systems: open ponds and closed photobioreactors (Moheimani 2012; Moheimani et al. 2011).

1.2.1.1 Closed Photobioreactors

Closed algal cultures (photobioreactors) are not exposed to the atmosphere and are covered with a transparent material or contained within transparent tubing. Pho­tobioreactors have the distinct advantage of preventing evaporation (Dodd 1986; Moheimani et al. 2011). Culturing microalgae in these kinds of systems have the added benefit of reducing the contamination risks, limiting the CO2 losses, creating reproducible cultivation conditions, and flexibility in technical design (Jeffery and Wright 1999). Closed and semi-closed photobioreactors are mainly used for pro­ducing high-value algal products (Becker 1994). In closed photobioreactors, the main challenge is being less economical than open ponds (Borowitzka 1996; Moheimani and McHenry 2013; Moheimani et al. 2013c; Pulz and Scheibenbogen 1998). A number of researchers have endeavoured to overcome a number of the limitations in closed including:

• reducing the light path (Borowitzka 1996; Janssen et al. 2002; Miron et al. 1999)

• solving shear (turbulence) complexity (Barbosa et al. 2003; Borowitzka 1996; Miron et al. 2003)

• reducing oxygen concentration (Acien Fernandez et al. 2001; Kim and Lee 2001; Rubio et al. 1999; Weissman et al. 1988), and

• temperature control system (Becker 1994; Borowitzka 1996; Carlozzi and Sacchi 2001; Morita et al. 2001; Rubio et al. 1999; Zhang et al. 1999).

Currently, the main disadvantages of closed systems are the high cost of con­struction, operation both for energy (pumping and cooling) and maintenance [such as cleaning and sterilization (Borowitzka 1996)], and scaling up difficulties (Grima et al. 2000; Janssen et al. 2002; Miron et al. 1999). However, if these difficulties can be overcome, these controlled closed systems may allow commercial mass pro­duction of an increased number of microalgal species at a wider number of locations.

Heterotrophic cultures use organic carbons as both sources of energy and carbon. There are many advantages of heterotrophic cultures over photoautotrophic cul­tures. These include the following: (i) the use conventional heterotrophic bioreac­tors that are simpler and easier to scale up, since the elimination of light requirements means that smaller reactor surface-to-volume ratios can be used; (ii) greater control of the cultivation process, since the cultures can be done indoors; and (iii) higher cell densities, which reduces the cost of harvesting the cells. The basic components of media for heterotrophic cultures are similar to those of the photoautotrophic media, with the addition of organic carbon sources. In addition, the growth rate and oil accumulation of heterotrophic cultures are affected by the C: N ratio in the medium.

Generally, the biomass concentrations obtained in heterotrophic cultures are much higher than those in photoautotrophic cultures (Ogbonna et al. 1998). Although the biomass concentration in most photoautotrophic cultures is less than 5 g/L, much higher concentrations of 15.5 g/L for Chlorella protothecoides (Xu et al. 2006), 28.8 g/L for Traselmis suecica (Azma et al. 2011), and even 53 g/L for

Chlorella zofingiensis (Sun et al. 2008) have been reported. Fed-batch cultures can be used to obtain even higher biomass concentrations. Furthermore, heterotrophi — cally grown microalgae usually accumulate more lipids than those cultivated photoautotrophically, as demonstrated for Chlorella species (Miao and Wu 2006; Xu et al. 2006; Agwa et al. 2013; Liu et al. 2008; Hsieh and Wu 2009). In the case of Chlorella vulgaris, for example, Wu et al. (2012) reported an increase from 15 % under photoautotrophic condition to more than 50 % under heterotrophic condition. Compared with photoautotrophic cultures, Jimenez et al. (2009) reported an 8 times increase in oil content of C. protothecoides under heterotrophic condition, and a 9 times increase in lipid yield was achieved in heterotrophic cultures fed with 30 g/ L of glucose (Liu et al. 2011).

The high biomass concentration and high lipid contents obtained in heterotro­phic cultures result in very high lipid productivities. A lipid productivity of 179 mg/ L/d in photoautotrophic culture is regarded as high (Chiu et al. 2008), but much higher productivities of 528.5 mg/L/d (Morales-Sanchez et al. 2013), 932 mg/L/d (Xu et al. 2006), 1.38 g/L/d (Liu et al. 2011), 2.43 g/L/d (Chen and Walker 2011),

3.0 g/L/d (Chen and Walker 2011), and 3.7 g/L/d (Xiong et al. 2008) have been reported for heterotrophic cultures. It is usually technically difficult to construct large-scale photoautotrophic photobioreactors; however, for heterotrophic cultures, conventional bioreactors can be used for large-scale processes. For example, a heterotrophic culture was scaled up from 5 to 750 L, and then 11,000 L, and the oil contents remained fairly stable at 46.1, 48.7, and 44.3 % of cell dry weight, respectively (Li et al. 2007).

It has also been reported that the quality of oil produced under heterotrophic cultures is more suitable for biodiesel production than those produced under pho­toautotrophic cultures with the same strains of microorganisms. Liu et al. (2011) reported that heterotrophic cells accumulated predominantly neutral lipids that accounted for 79.5 % of the total lipids, with 88.7 % triacylglycerol, while oleic acid accounted for 35.2 % of the total fatty acid. In contrast, photoautotrophic cells contained mainly the membrane lipids, glycolipids, and phospholipids. Further­more, C. saccharophila, C. vulgaris, N. laevis, Cylindrotheca fusiformis, Navicula incerta, and Tetraselmis suecica accumulate more lipids under heterotrophic than under photoautotrophic conditions, mainly in the form of triglycerides (Day et al. 1991; Tan and Johns 1991, 1996; Gladue and Maxey 1994). Conversely, photo­autotrophic cultures form more highly unsaturated fatty acids (polar lipids) (Tan and Johns 1991, 1996; Wen and Chen 2000a, b). Miao and Wu (2004) further noted that the oil obtained from heterotrophically grown cells possesses properties similar to those of fossil diesel in terms of oxygen content, heating value, density, and viscosity.

However, heterotrophic cultures have some limitations. Only a limited number of microalgae are capable of growing under heterotrophic conditions, and addition of an organic carbon source can significantly increase the cost of production. Furthermore, the presence of an organic carbon source further increases the risk of contamination, and depending on the species, the cell growth rate and lipid pro­ductivity in heterotrophic culture may be lower than the values obtained in mixotrophic culture. For example, Day and Tsavalos (1996) found that heterotro­phic culture of Tetraselmis with glucose yielded only about one-sixth of cellular lipid compared with the value obtained in mixotrophic culture.

Various parameters such as initial cell concentration, input CO2 concentration, aeration rate, photobioreactor design, light intensity and temperature should be taken into account for biofixation of CO2 from flue gases by microalgae. These factors are important when seeking to achieve high-productivity microalgae bio­remediation of CO2 from input gases. In this section, the effect of initial cell concentrations, input CO2 concentration, aeration rate and data analysis from these factors on microalgal CO2 bioremediation is discussed.

A gene deletion experiment and its effect on cellular behavior can be simulated in a manner similar to the linear optimization of growth (or for any other objective function). The results can be used to guide the design of metabolic engineering strategies. Gene-reaction associations in the model describe the relationship between genes and their corresponding reactions; therefore, reactions can be removed from the model on the basis of individual gene deletions. The possible results from a simulation of a single gene deletion are unchanged maximal growth (nonessential), reduced maximal growth, or no growth (“sick” or lethal effect). The gene deletion analyses can be carried out genome-wide with the outcome tabulated to provide a comprehensive overview of gene essentiality for the system.

In certain cases, mutations in two genes produce a phenotype that is surprising in the light of each mutation’s individual effect. This phenomenon, which defines genetic interaction, can reveal functional relationships between genes and path­ways. For example, double mutants with surprisingly slow growth define syner­gistic interactions that can identify compensatory pathways or protein complexes (Harcombe et al. 2013). Like single gene deletions, double gene deletions can be simulated to encompass all possible double gene deletions in the network. This provides a powerful tool to simulate otherwise prohibitively difficult wet-bench genetic interaction experiments.

Ela Eroglu, Steven M. Smith and Colin L. Raston

Abstract Immobilized cells entrapped within a polymer matrix or attached onto the surface of a solid support have advantages over their free-cell counterpart, with easier harvesting of the biomass, enhanced wastewater treatment, and enriched bioproduct generation. Immobilized microalgae have been used for a diverse number of bio­processes including gaining access to high-value products (biohydrogen, biodiesel, and photopigments), removal of nutrients (nitrate, phosphate, and ammonium ions), heavy metal ion removal, biosensors, and stock culture management. Wastewater treatment processes appear to be one of the most promising applications for immo­bilized microalgae, which mostly involve heavy metal and nutrient removal from liquid effluents. This chapter outlines the current applications of immobilized mic­roalgae with an emphasis on alternative immobilization approaches. Advances in immobilization processes and possible research directions are also highlighted.

2.1 Introduction

Algal bioprocesses are advantageous in integrating wastewater treatment processes with valuable biomass production. Algal biomass can be further exploited for various purposes such as biofuel generation in the form of biodiesel, biohydrogen, or biogas;

E. Eroglu (H) • S. M. Smith

School of Chemistry and Biochemistry, The University of Western Australia, Crawley,

WA 6009, Australia e-mail: elaeroglu@gmail. com

E. Eroglu • S. M. Smith

ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Crawley, WA 6009, Australia

C. L. Raston

Centre for NanoScale Science and Technology, School of Chemical and Physical Sciences, Flinders University, Bedford Park, SA 5042, Australia

© 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_2 food additives; slow-release fertilizers or soil conditioners; cosmetics; pharmaceu­ticals; and several other valuable chemicals (Johnson and An 1991; Mallick 2002; Mulbry et al. 2005). Microalgal cells have several other advantages in not requiring many resources to generate their biomass, providing an economical operation at lower costs, with the dissolved oxygen released by the algae being useful to elevate the oxygen levels of water effluents, and can be utilized for the reduction of carbon dioxide emissions using CO2 for their biomass and/or energy production.

Harvesting and dewatering of algal biomass from its liquid environment is one of the major challenges of algal bioprocesses. Several studies have focused on harvesting of microalgae using a wide range of technologies from sand filtration to high-speed centrifugation (Mallick 2002; Oswald 1988). Some of the most recent technologies for algal dewatering are further discussed in Chaps. 12, 13, and 14 of this book. Immobilization of algal cells has been proposed mainly to overcome the burdens of difficult harvesting and dewatering stages, in addition to providing the retention of the high-value-added algal biomass for further processes (de la Noue and de Pauw 1988; Mallick 2002).

Immobilization of various cells in either polymeric or biopolymeric matrices has several advantages over their free-cell counterparts, since immobilized cells occupy less space, are easier to handle, and can be used repeatedly for product generation (Mallick 2002). Immobilization of cells has also been proposed to increase the biosorption capacity and bioactivity of the biomass (Akhtar et al. 2004; de-Bashan and Bashan 2010). It allows bioprocesses with higher cell densities and also easy harvesting of biomass from its liquid environment (Mallick 2002). Immobilization processes have several other advantages as being resistant to harsh environments such as salinity, metal toxicity, and pH; protecting the aging cultures against the harmful effects of photoinhibition; yielding higher biomass concentrations; recov­ering the cells in a less-destructive way; and enhancing the cost-effectiveness of the process by reusing the regenerated biomass (Bailliez et al. 1986; Hall-Stoodley et al. 2004; Liu et al. 2009). Given the use of large-scale bioreactors represents a significant challenge associated with algal biomass recovery, immobilization sys­tems are becoming attractive alternatives for scale-up processing (Christenson and Sims 2011; Hoffmann 1998).

Various immobilization processes are in use, such as adsorption, confinement in liquid-liquid emulsions, capturing with semipermeable membranes, covalent cou­pling, and entrapment within polymers (de-Bashan and Bashan 2010; Mallick

2002) . Among others, the most common immobilization processes are the entrap­ment of the cells within polymeric matrices and self-adhesive attachment of cells onto the surfaces of solid-supports (Godlewska-Zylkiewicz 2003). Both synthetic and natural polymers can be applied as the immobilization matrix (de-Bashan and Bashan 2010).

Important criteria for successful entrapment are to set the algal cells free within their partition, while pores inside the gel matrix allow the diffusion of substrates and the metabolic products toward and from the cells (Mallick 2002). Nevertheless, entrapment systems still hold some drawbacks in reducing the mass transfer kinetics of the uptake of metal ions (Aksu et al. 2002). However, these can be avoided by careful choice of the immobilization method and the nature of the matrix, which will be further discussed in detail.

Key applications suggested for immobilized algal cells include removal of nutrients from aqueous solutions (Chevalier and de la Notie 1985), biodiesel pro­duction (Bailliez et al. 1985; Li et al. 2007), biosorption of heavy metals from wastewaters (de-Bashan and Bashan 2010), photoproduction of hydrogen and photopigments (Bailliez et al. 1986; Laurinavichene et al. 2008), providing an alternative technique to the common cryopreservation processes (Chen 2001; Faafeng et al. 1994; Hertzberg and Jensen 1989), and also toxicity testing (Boze­man et al. 1989). These processes will also be discussed in detail in the following sections of this review article.

When nutrient concentrations are reported for wastewater, total concentrations of nutrients are often reported. Dissolved inorganic nutrients, such nitrate and ammo­nium N and phosphate P are directly available to microalgae. Part of the total N and P in wastewater, however, may be associated with organic molecules (dissolved organic nutrients) or with particulate matter (either organic or inorganic). These nutrients are not necessarily available to microalgae. Relatively few studies have investigated the bioavailability of nutrients to microalgae in different types of wastewater.

The bioavailability of P is highly variable and may vary between 3.4 and 81 % in different types of wastewaters or surface waters (Ekholm and Krogerus 2003). The bioavailability of total P depends on the dominant P-forms that are present (Van Moorleghem et al. 2013; e. g., Li and Brett 2013). Polyphosphates and organic phosphate monoesters, for instance, have a high bioavailability. Microalgae are capable of producing phosphatase enzymes to dissociate phosphate ester bonds (e. g., Huang and Hong 1999). Phytic acid, on the contrary, has a very low bio­availability and is a major phosphate reserve in plant seeds. As it cannot be digested by livestock, it is present in high concentrations in manure from grain-fed livestock (Jongbloed and Lenis 1998). To increase the bioavailability of P from phytic acid to livestock, phytase enzymes are sometimes added to the animal feed, and this may result in a higher bioavailability of P in the manure. If the wastewater contains a lot of sediments, phosphate may be chemically bound to iron (Fe) or aluminum (Al), and this phosphate has a low bioavailability. Phosphate that is precipitated with calcium (Ca) as apatite minerals also has a low bioavailability to microalgae, despite being often detected by colorimetric phosphate analyses. Wastewater from animal manure has a high content of humic substances and these can also bind phosphate. It is assumed that phosphate is bound to oxidized Al or Fe ions that are stabilized by humic acid complexes (Gerke 2010). These humic acid-metal phos­phate complexes also have a low bioavailability to microalgae.

Less information is available about the bioavailability of organic N forms. About half of the N in animal manure, wastewater may be present as organic N (Cai et al.

2013) . Free amino acids, nucleotides, as well as urea are organic N forms that are highly bioavailable to microalgae. Peptides or proteins have a slightly lower bio­availability, and humic substances contain N and exhibit even less bioavailability (Bronk et al. 2007). Humic substances have a C:N ratio of 18-30:1 for humic acids and 45-55:1 for fulvic acids (See and Bronk 2005). Humic substances can absorb amino acids and ammonium ions, which can represent about half of N in humic substances. Part of the organic N present in wastewater is slowly made available by bacteria that live in symbiosis with microalgae (Pehlivanoglu and Sedlak 2004).

MAGE is a genetic engineering method that relies on recombineering to produce frequent and large scale genetic changes to cells. Simply, it is a cyclical and scalable recombineering system that allows multiple genetic changes in a high throughput manner. A given cycle in MAGE requires cell growth, the introduction of recombineering substrate, and provision of providing synthesized DNA con­structs a continuous and interlinked process. Target cells are continuously grown and drawn out of a cell growth chamber into an exchange chamber where the substrates required for recombineering are added under the right conditions. The induced cells are then mixed with the recombinant and synthetic DNA constructs in another chamber and then moved to an electroporator to induce the uptake of the synthetic DNA constructs. Those modified cells are then reintroduced into the growth chamber and grown. This whole process occurs continuously; and move­ment to each stage is facilitated by microfluidics, or any suitable pumping assembly (Isaacs et al. 2011).

Just like recombineering, MAGE can be used to create a mismatch, deletion or insertional genetic changes. The efficiency of each genetic change is dependent on the size of the homology flanking each introduced DNA construct, the size of the desired change, and the number of cycles. This efficiency is also correlated with the Gibbs free energy from the hybridization that occurs between the DNA strands. Knowledge of the efficiency of each genetic change and the parameters that affect it can be used to tune the MAGE process to produce colonies or strains with desired genetic traits (Isaacs et al. 2011). The MAGE system was initially demonstrated by modifying a 1-Deoxy-D-xylulose 5-phosphate (DXP) biosynthesis pathway in Escherichia Coli. The DXP pathway was modified to increase production of isoprenoid lycopene. New genetic modifications were introduced in greater than 30 % of the cell population every 2-2.5 h under optimum conditions. After just five cycles of MAGE, the average genetic change across the entire cell population was

3.1 bp, and increased to 5.6 bp afters 15 cycles. Ultimately, 24 genes were opti­mized simultaneously and about 15 billion genetic variants were produced at an average rate of 430 million bp changes per MAGE cycle in a total of 35 cycles. This translated to an up to 390 % increase in isoprenoid lycopene production, clearly demonstrating MAGE’s potential (Isaacs et al. 2011). Development of a system similar to MAGE in algae is dependent on the design of a suitable genetic engi­neering method; recombineering strategies are not established in algae.

In some studies, more than one culture was immobilized to achieve a multifunctional immobilization matrix. For example, Adlercreutz et al. (1982) co-immobilized mixed cultures of algae (Chlorella pyrenoidosa) and bacteria (Gluconobacter oxy — dans) inside calcium alginate beads for the continuous production of dihydroxyac — etone. They did not observe any significant loss of activity within the first six days of this bioprocess. They used the algal cells as an in situ oxygen supplier, which was directly used by the bacteria during the conversion of glycerol to dihydroxyacetone (Adlercreutz et al. 1982). Co-immobilization of microalga S. obliquus with Bacillus subtilis bacteria in carrageenan beads was studied inside air-lift reactors, for enhancing the production of alpha-amylase enzyme (Chevalier and de la Node 1988). Microalgal cells were again used as an in situ oxygen generator for the bacterial cells, which were mainly responsible for the synthesis of alpha-amylase enzyme. Co-immobilization overcame the existing oxygen diffusion problems and yielded higher alpha-amylase activity by a factor of around 20 %. They also observed higher growth rates for the algal cells when co-immobilized with bacteria, compared to the immobilization with algal cells alone (Chevalier and de la Node 1988).

Immobilization of Dunaliella tertiolecta in alginate (Grizeau and Navarro 1986) and Dunaliella salina in agar-agar (Thakur and Kumar 1999) increased the amount of glycerol production. Immobilized algae were also used for the generation of keto acids from amino acids (Wikstrom et al. 1982).

Luan et al. (2006) achieved successful removal (90 %) of a highly toxic tribu — tyltin using alginate-immobilized C. vulgaris cells. They observed that less than 10 % of the tributyltin was accumulated inside the cells, while the remainder was adsorbed by both the immobilization matrix and the cell walls.

He et al. (2014) recently constructed an algal fuel cell with immobilized C. vulgaris cells in sodium alginate placed inside a cathode chamber of the fuel cell. The aim was to achieve a complete process that combines biomass production, electricity generation, and wastewater treatment all at the same time. They observed a significant chemical oxygen demand (COD) removal efficiency of 92.1 %.

The morphology, pigments, metabolic capabilities, and cell wall structure of the eukaryotic microalgae are quite diverse because they represent a multitude of phylogenetically distinct groups of organisms. Recent molecular evidence suggests that the “algae” fit into very different evolutionary lineages including those related to plants, fungi, or animals (Lucentinii 2005) via one or more serial endosymbiotic events (Moestrup 2001a, b). Three classes of primary interest for biofuels pro­duction include the golden-brown algae (Chrysophycea), prymnesiophytes (Prymnesiophyceae), and the eustigmatophytes (Eustigmatophyceae) (Sheehan et al. 1987) and for bioremediation, the Chlorophycea.

6.5.2.1 Chlorophyta

The green algae are a large group estimated to have over 13,000 species, (Guiry

2012) from which the higher plants emerged. Green algae, in common with land plants, have chloroplasts that contain chlorophyll a and b, as well as the accessory pigments P-carotene and xanthophylls, and store carbon as starch and lipids. Their distribution is ubiquitous with marine, freshwater, and terrestrial species and groups adapted to extremes including deserts, arctic zones, hypersaline waters, and deep — sea thermal vents (Lewis and Lewis 2005; De Wever et al. 2009). Several strains are known to have rapid growth rates and high lipid content and have been a focus of biofuel research. Chorella spp., used since the 1950s as a food supplement, have short doubling times and can be cultured to produce between 30 and 55 % lipid content (Becker 1994; Miaoa and Wu 2006). Species of Scenedesmus, Ettlia, Nannochloris, and Monoraphidium grow rapidly (doubling times from 7 to 12 h under nutrient-replete conditions) and have lipid contents ranging from 30 to over 60 % (Griffiths and Harrison 2009). A number of genera in the order of Chloro — coccales, including Actinastrum, Scenedesmus, Chlorella, Closerium, and Gol — enkinia, tend to dominate algae communities in eutrophic waters (Rawson 1956) and algae wastewater ponds (Martinez et al. 2000; Benemann and Oswald 1996). Many are either heterotrophic or mixotrophic.

Kourosh Salehi-Ashtiani, Joseph Koussa, Bushra Saeed Dohai, Amphun Chaiboonchoe, Hong Cai, Kelly A. D. Dougherty, David R. Nelson, Kenan Jijakli and Basel Khraiwesh

Abstract Genomic sequencing is the first step in a systems level study of an algal species, and sequencing studies have grown steadily in recent years. Completed sequences can be tied to algal phenotypes at a systems level through constructing genome-scale metabolic network models. Those models allow the prediction of algal phenotypes and genetic or metabolic modifications, and are constructed by tying the genes to reactions using enzyme databases, then representing those reactions in a concise mathematical form by means of stoichiometric matrices. This is followed by experimental validation using gene deletion or proteomics and metabolomics studies that may result in adding reactions to the model and filling phenotypic gaps. In this chapter, we offer a summary of completed and ongoing algal genomic projects before proceeding to holistically describing the process of constructing genome-scale metabolic models. Relevant examples of algal metabolic models are presented and discussed. The analysis of an alga’s emergent properties from metabolic models is also demonstrated using flux balance analysis (FBA) and related constraint-based approaches to optimize a given metabolic phenotype, or sets of phenotypes such as algal biomass. We also summarize readily available optimization tools rooted in constraint-based modeling that allow for optimizing bioproduction and algal strains. Examples include tools used to develop knockout strategies, identify optimal bioproduction strains, analyze gene deletions, and

Joseph Koussa and Bushra Saeed Dohai contributed equally to this work.

K. Salehi-Ashtiani (H) • J. Koussa • B. S. Dohai • A. Chaiboonchoe • H. Cai K. A.D. Dougherty • D. R. Nelson • B. Khraiwesh

Division of Science and Math, and 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

K. Jijakli

Division of Engineering, New York University Abu Dhabi, P. O. Box 129188,

Abu Dhabi, United Arab Emirates © 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_10

explore functional relationships within sets in a metabolic model. All in all, this systems level approach can lead to a better understanding and prediction of algal metabolism leading to more robust and cheaper applications.

10.1 Introduction

Algal research gained its first round of momentum, beyond the scientific commu­nity, in 1978 with the launch of the Aquatic Species Program to explore alternative transport fuel sources (Sheehan et al. 1998). As many laboratories started focusing their investigations entirely on algal systems, the amount of data available increased at a near exponential pace. In particular, the development of next-generation sequencing platforms has rapidly and dramatically advanced and increased the amount of available data on algal genomes. New high-throughput phenotypic platforms have made metabolic characterizations broader and more rapid. As in other fields of biological research, integration of disparate data types, as well as contextualization of data remains a central challenge that is addressed by the field of systems biology. Systems level understanding of metabolism is needed for pre­diction of biomass and bioproduct optimization strategies, for algae, or for any other organism because of the wide expanse and the high degree of interconnec­tivity in metabolic networks.

Evolutionarily, the term algae describe a polyphyletic group of organisms and is contentious in definite meaning (Proschold and Leliaert 2007). Currently, in some classifications, it includes superphyla in several separate lineages: stramenopiles which include brown, golden, and yellow algae and diatoms; rhodophyta or the red algae; photosynthetic alveolates, such as dinoflagelates; and the viridiplantae which include green algae (Barton et al. 2007). The phylogenetic classification green algae describe the presumed plant predecessors with photosynthetic capabilities, and a characteristic green color (Besche et al. 2009; Harris 2001). Both green algae and diatoms have shown great potentials as sustainable sources of biofuel, biomass, and bioproducts; however, Chlamydomonas reinhardtii, due to its position as a well studied representative green alga (Harris 2001), has received special interest as a model organism for genomic and metabolic studies.

The interest in algal exploration has steadily increased as commercial and large — scale production of lipid-producing algae has provided a practical importance for the research, with particular demand on more integrated goals, i. e., optimizing algae for biofuel production, optimizing growth of strains of interest and achieving economical viability all at the same time (Koussa et al. 2014). The metabolic optimization of algal organisms, which requires extensive characterization of algal metabolic circuitry, calls for an integrative “systems level” approach. This begins with genome sequencing, which then provides the “parts list” for reconstruction of a metabolic network, and finally ends with the ability to make model-based pre­dictions (Fig. 10.1). In this chapter, we first review microalgal sequencing efforts

investigations

Fig. 10.1 Schematic diagram representing the relationships between the reductionist, systems, and synthetic biology approaches. Through reductionism, a “top-down” approach leads to characterization of few or individual target genes. The collective knowledge gained through individual studies provides the framework for developing large-scale methodologies, building knowledgebases, and executing “bottom-up” omics studies, with the results integrated to describe the emergent properties of the system. Investigations of the models, constraints, and simulations provide predictions, which are implemented through an engineering approach, using biological parts or devices, for synthesis of a desired biological outcome that are in progress or have been completed, we then outline metabolic network reconstruction and constraint-based analyses, and to conclude, we briefly describe some computational tools that are used in metabolic modeling, which can aid the design of engineering experiments and optimization of cellular metabolic outputs.