Computational Models of Algae Metabolism for Industrial Applications Jacob E. Koskimaki, 1 Anna S. Blazier, 1 Andres F. Clarens, 2 and Jason A. Papin 1 1 Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 2 Department of Civil and Environmental Engineering, University of Virginia, Charlottesville, VA Abstract Many algae species exhibit phenotypes that are of great in- terest for industrial applications. Systems biology approaches including large-scale metabolic reconstructions can guide and enhance production of biobased commodities such as fuel and human therapeutic proteins. Here we review the existing computational models of metabolism in algal species, as well as the databases and tools that are available to improve the sustainability of algae-to-energy production at industrial scales. As this field has progressed with genome sequencing, computational techniques, novel annotation tools and data- bases, systems biology will continue to evolve to advance the appeal of algae to a wide array of industries. Introduction M icroalgae fossil records extend back more than three billion years, and over that period algae have become a vital and ubiquitous component of the Earth’s biosphere. 1 Their importance is pri- marily due to their high photosynthetic productivity, which creates the foundation for many ecosystems. 2 With more than 40,000 algal species currently identified, microalgae have evolved to succeed in most environments on earth. 3 Many mi- croalgal species are autotrophic, meaning they convert sunlight and CO 2 into biomass, and include types of green algae (Chlorophyta), yellow-green algae (Xanthophyta), golden algae (Chrysophyta), red algae (Rhodophyta), diatoms, and dinofla- gellates. 4 This diversity of species contributes to much of the current interest in using microalgae for a variety of commercial purposes, ranging from nutraceutical applications to biofuel production. Here we focus our discussion on eukaryotic species of algae and exclude cyanobacteria, or blue-green prokaryotic algae, due to their distinct cellular properties. Industrial production strategies have generally sought to capitalize or improve production of one or more of the following outputs from algae: long-chained polyunsaturated fatty acids, food colorants, animal feed and supplementation, cosmetics, hydrogen, wastewater treatment, CO 2 fixation, human thera- peutic proteins, and biofuel production. 5–13 Thus, algal species have the potential to become ‘‘cellular factories’’ upon genetic re-engineering to improve production and yield of desired commodities. Despite the promise of commercial applications for micro- algae, algal biotechnology remains in its infancy. Terrestrial crops and animals have been bred over thousands of years and selected for the creation of high-yield species for agricultural needs. It stands to reason that efforts to cultivate algae on a large scale will not be based solely on wild-type species but will require some degree of genetic modification before algae can make economically viable contributions to any sector. 14 How- ever, our current limited understanding of how to perform ge- netic engineering of a species for the creation of target strains with economically favorable characteristics has hindered the industrialization of algal biotechnology. Academic and commercial interest in the properties of algae metabolism has increased substantially in recent years (Figs. 1A-B). Advances in genome sequencing have allowed for the sequencing of at least 20 microalgal genomes, including the smallest free-living eukaryote Ostreococcus tauri, the unicel- lular green algae Chlamydomonas reinhardtii, and the plank- tonic species Botryococcus braunii, with applications across a range of industries (Fig. 1C). 9,15–76 Key species of interest include B. braunii, C. reinhardtii, Dunaliella salina, and Phaedactylum tricornutum, although to date only a select number of engineered strains and products have been commercialized. Furthermore, within the broader field of systems biology, advances in whole genome sequencing have paved the way for the reconstruction of organism-specific metabolic networks, with genome-scale networks having been created for several prokaryotes as well as for an increasing number of more complex, eukaryotic species. 77 Metabolic network models are systems-level, genomic computational tools that can assist in the reengineering of key metabolic pathways for industrial-level production of desired products such as lipids and biofuels. 78 Additional applications include bioremediation, wastewater treatment, and carbon dioxide (CO 2 ) fixation. For example, a genome-scale metabolic network for the model eukaryote Sac- charomyces cerevisiae was probed using in silico engineering strategies to increase ethanol yields on glucose and production of fumaric acid, a potential petroleum substitute. 79,80 Similarly, metabolic network reconstructions of algae hold promise to aid in the identification of genetic modifications for the creation of optimized strains with commercial potential for desired com- modity production. To date, metabolic network reconstructions DOI: 10.1089/ind.2013.0012 ª MARY ANN LIEBERT, INC. VOL. 9 NO. 4 AUGUST 2013 INDUSTRIAL BIOTECHNOLOGY 185
Transcript
Computational Models of Algae Metabolism for Industrial Applications
Jacob E. Koskimaki,1 Anna S. Blazier,1 Andres F. Clarens,2
and Jason A. Papin1
1Department of Biomedical Engineering, University of Virginia,Charlottesville, VA
2Department of Civil and Environmental Engineering,University of Virginia, Charlottesville, VA
AbstractMany algae species exhibit phenotypes that are of great in-terest for industrial applications. Systems biology approachesincluding large-scale metabolic reconstructions can guide andenhance production of biobased commodities such as fuel andhuman therapeutic proteins. Here we review the existingcomputational models of metabolism in algal species, as wellas the databases and tools that are available to improve thesustainability of algae-to-energy production at industrialscales. As this field has progressed with genome sequencing,computational techniques, novel annotation tools and data-bases, systems biology will continue to evolve to advance theappeal of algae to a wide array of industries.
Introduction
Microalgae fossil records extend back more thanthree billion years, and over that period algaehave become a vital and ubiquitous component ofthe Earth’s biosphere.1 Their importance is pri-
marily due to their high photosynthetic productivity, whichcreates the foundation for many ecosystems.2 With more than40,000 algal species currently identified, microalgae haveevolved to succeed in most environments on earth.3 Many mi-croalgal species are autotrophic, meaning they convert sunlightand CO2 into biomass, and include types of green algae(Chlorophyta), yellow-green algae (Xanthophyta), golden algae(Chrysophyta), red algae (Rhodophyta), diatoms, and dinofla-gellates.4 This diversity of species contributes to much of thecurrent interest in using microalgae for a variety of commercialpurposes, ranging from nutraceutical applications to biofuelproduction. Here we focus our discussion on eukaryotic speciesof algae and exclude cyanobacteria, or blue-green prokaryoticalgae, due to their distinct cellular properties.
Industrial production strategies have generally sought tocapitalize or improve production of one or more of the followingoutputs from algae: long-chained polyunsaturated fatty acids,food colorants, animal feed and supplementation, cosmetics,
hydrogen, wastewater treatment, CO2 fixation, human thera-peutic proteins, and biofuel production.5–13 Thus, algal specieshave the potential to become ‘‘cellular factories’’ upon geneticre-engineering to improve production and yield of desiredcommodities.
Despite the promise of commercial applications for micro-algae, algal biotechnology remains in its infancy. Terrestrialcrops and animals have been bred over thousands of years andselected for the creation of high-yield species for agriculturalneeds. It stands to reason that efforts to cultivate algae on a largescale will not be based solely on wild-type species but willrequire some degree of genetic modification before algae canmake economically viable contributions to any sector.14 How-ever, our current limited understanding of how to perform ge-netic engineering of a species for the creation of target strainswith economically favorable characteristics has hindered theindustrialization of algal biotechnology.
Academic and commercial interest in the properties of algaemetabolism has increased substantially in recent years (Figs.1A-B). Advances in genome sequencing have allowed for thesequencing of at least 20 microalgal genomes, including thesmallest free-living eukaryote Ostreococcus tauri, the unicel-lular green algae Chlamydomonas reinhardtii, and the plank-tonic species Botryococcus braunii, with applications across arange of industries (Fig. 1C).9,15–76 Key species of interestinclude B. braunii, C. reinhardtii, Dunaliella salina, andPhaedactylum tricornutum, although to date only a selectnumber of engineered strains and products have beencommercialized.
Furthermore, within the broader field of systems biology,advances in whole genome sequencing have paved the way forthe reconstruction of organism-specific metabolic networks,with genome-scale networks having been created for severalprokaryotes as well as for an increasing number of morecomplex, eukaryotic species.77 Metabolic network models aresystems-level, genomic computational tools that can assist in thereengineering of key metabolic pathways for industrial-levelproduction of desired products such as lipids and biofuels.78
Additional applications include bioremediation, wastewatertreatment, and carbon dioxide (CO2) fixation. For example, agenome-scale metabolic network for the model eukaryote Sac-charomyces cerevisiae was probed using in silico engineeringstrategies to increase ethanol yields on glucose and productionof fumaric acid, a potential petroleum substitute.79,80 Similarly,metabolic network reconstructions of algae hold promise to aidin the identification of genetic modifications for the creation ofoptimized strains with commercial potential for desired com-modity production. To date, metabolic network reconstructions
DOI: 10.1089/ind.2013.0012 ª M A R Y A N N L I E B E R T , I N C . � VOL. 9 NO. 4 � AUGUST 2013 INDUSTRIAL BIOTECHNOLOGY 185
have been generated for four different algal species: C. re-inhardtii, Ostreococcus lucimarinus, O. tauri, and B. braunii.
In this review, we highlight the current state of algae meta-bolic network modeling and optimization tools with an em-phasis on the application of in silico metabolic engineeringreconstructions. We use this information to provide an analysisof how systems-based approaches can inform and be informedby large-scale supply chain constraints in the algae-to-energylandscape, and how this might influence commercial develop-ment strategies.
Metabolic Engineering Reconstruction ProcessMetabolic network reconstructions are mathematically orga-
nized structures assembled from various data sources includingbiochemical, genomic, and cell phenotypic data. Assemblednetworks account for known reactions, enzymes, and genes thatare part of the metabolic pathways of an organism. Generating ametabolic network reconstruction is an iterative process in-volving several rounds of manual curation and experimentalvalidation. The first step in creating a genome-scale metabolicreconstruction is to obtain the most recent version available ofthe target organism’s sequenced genome and annotation toidentify open reading frames (ORFs) and assign them functions
by comparing them to genes associated with proteins of knownfunctions using available online tools such as Uniprot andChlamyCyc.81,82
Once these molecular functions have been assigned, reactionsare then associated with the annotated genes, taking into accountstoichiometry and reversibility, using databases like KEGG andPredAlgo.83–85 Additionally, to create the most biologicallyaccurate model with accurate network connectivity, cellularlocalization must be taken into account, especially for complexeukaryotic organisms such as algae. Unlike the other steps of thereconstruction process, which rely heavily on the comparison ofthe functionality of genes across different species and organ-isms, compartmentation of reactions requires organism-specificknowledge; however, there is often little to no evidence forreaction localization.86 Thus, in the event that the localization ofa particular reaction is unknown, researchers typically infer thecompartment based on related reactions in homologous species,relying on transporter reactions to carry metabolites from onecompartment to another. In the case of the well-studied plantspecies Arabidopsis thaliana and its reconstructed networkAraGEM, compartmentation of reactions was completed man-ually based on the literature, and in many cases, reactions werelocalized by default to the cytosol to avoid relying on
Fig. 1. Snapshot of algae species with sequenced genomes in research and industrial applications. Number of publications cited in PubMedper year from 2000 through 2012 (A). Search terms included ‘‘algae and metabolism’’; ‘‘Chlamydomonas and metabolism’’; and‘‘Ostreococcus and metabolism.’’ Number of patents issued in the United States per year with algae or alga in title or abstract, according tothe United States Patent and Trademark Office (www.uspto.gov) (B). For sequenced algal genomes, the number of publications for theindicated application cited on PubMed (C). References for the indicated species are listed as follows: B. braunii,18–31 C. reinhardtii,9,32–51
D. salina,52–58 Micromonaspusilla CCMP1545,59 O. lucimarinus,60 O. tauri,60,61 Volvox carteri,60,62 Phaeodactylum tricornutum,63–69
Thalassiosira pseudonana,70,71 Chondrus crispus,72 Cyanidioschyzon merolae,60,73 and Galdieria sulphuraria.74–76
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uncharacterized transport reactions.86,87 Certainly, as biologicalknowledge increases, so too will the quality of available networkreconstructions and associated reaction localization.
There are several freely available tools that semi-automate thedraft reconstruction process, such as Pathway Tools and ModelSEED; however, extensive manual curation is still required.88–90
Table 1 highlights key resources publicly available for the re-construction of metabolic networks, including several algae-specific annotation tools.81,82,85,88–104 Ultimately, this extensiveand iterative manual curation process results in an organism-specific, genome-scale metabolic reconstruction that summa-rizes the metabolic pathways of a target organism by doc-umenting information such as the gene-protein-reaction (GPR)relationships, reaction stoichiometry, reaction reversibility, andcellular compartment. We provide an overview of the process togenerate an in silico metabolic network for algae commodityproduction, including computational tools for optimized strainselection, in Fig. 2.
In Silico Methods for Strain OptimizationOnce the metabolic network reconstruction has been manu-
ally curated, constraint-based modeling approaches can then beperformed to characterize aspects of the organism’s metabolism.One commonly used approach, flux balance analysis (FBA), canbe used to predict the fluxes of metabolites through the meta-bolic network when optimized toward a biologically relevantobjective, such as cell growth or the production of a metaboliteof interest. FBA simulations can be performed using softwaretools like the COBRA Toolbox, a freely available softwarepackage that runs in MATLAB, and the BioMet Toolbox, anonline resource for integrating high-throughput data for systemsanalysis.105,106
To perform FBA, the metabolic network reconstruction mustfirst be converted into a mathematical framework in the formof a stoichiometric matrix, S, where the rows of S correspondto the metabolites present in the reconstruction and the col-umns of S correspond to the reactions present in the recon-struction (Fig. 2). As such, each element of the matrix denotesthe stoichiometric coefficient of a particular metabolite in agiven reaction. Subsequently, a system of linear equations isestablished by multiplying the stoichiometric matrix by acolumn vector, v, which accounts for the fluxes through thereactions in the system, and setting this product equal to therate of change of concentrations of all the species in the sys-tem. This vector of concentrations is equal to zero understeady-state conditions. The assumption that the metabolicnetwork operates at steady-state is well justified under manyconditions of interest.107
Because the resulting system of equations is underdetermined(ie, there are typically more reactions than metabolites in a re-construction), FBA converts this system to a linear program-ming problem by optimizing for a particular flux called theobjective function. Examples of objective functions includebiomass production as an estimate for cell growth yield or theproduction of a metabolite of commercial interest. Additionalconstraints, such as enzyme capacities, maximum uptake, se-cretion rates and thermodynamic constraints, can be placed onthe system to further narrow the solution space of feasible flux
distributions.108 Using these constraints and the specified ob-jective function, FBA then calculates a distribution of fluxesthrough the metabolic network corresponding to the optimalvalue of the objective.
Since its introduction, several extensions have been made toFBA that have potential applications for in silico metabolicengineering. For example, in silico gene knockout simulationscan be performed by taking advantage of the GPR relationshipdefined in the reconstruction to predict the effect that a modelgene deletion or a combination of gene deletions will have oneither biomass production or on the yield of a metabolite ofinterest.109 However, given the size of genome-scale metabolicnetwork reconstructions, the amount of data generated from insilico gene deletion combination simulations has proven to bechallenging to manage.110 To combat the immense volume ofcombinatorial data, several frameworks have been proposed toguide in silico gene knockout studies in a systematic and bio-logically relevant manner. OptKnock, for instance, is structuredas a two-part optimization problem that evaluates the effect ofgene knockouts to maximize both the overproduction of a me-tabolite of interest as well as the biomass.111 Several extensionsto OptKnock such as OptGene, OptStrain, OptORF, and OptReghave been proposed to help further guide in silico metabolicengineering gene deletion simulations.112–115 OptReg, for in-stance, extends OptKnock by allowing for gene up- and/ordown-regulation, enabling the development of a broader rangeof potential metabolic engineering strategies.
Additionally, several FBA-based algorithms have been de-veloped to depict more clearly the objective function that bestcharacterizes the metabolism of mutant organisms. Studies havesuggested that, unlike the wild-type phenotype, mutant organ-isms that have not been exposed to long-term evolutionarypressure do not operate in an optimized manner in regards tobiomass production. Rather, in response to a genetic perturba-tion, the mutant tries to limit the difference in flux distributionbetween itself and the wild-type phenotype. In an effort tocapture this altered objective function, algorithms like Mini-mization Of Metabolic Adjustment (MOMA) and RegulatoryOn/Off Minimization (ROOM) have been developed to mini-mize the predicted redistribution of metabolic fluxes in mutantphenotypes after a gene knockout by applying a minimal dis-tance metric that limits the overall change in flux distributioncompared to the wild type.116,117
Further aiding in strain design, several methods like fluxvariability analysis (FVA) and flux coupling analysis (FCA)have been developed to study the structural and topologicalproperties of metabolic networks. FVA identifies the range ofpossible fluxes for each reaction in a network given a certainobjective function value.118 FCA identifies pairs of metabolicfluxes in the network that are coupled together directionally,partially, or fully, as well as blocked reactions that are unable tocarry a flux under certain conditions.118–120
Finally, genome-scale metabolic network reconstructionsare an excellent platform for the integration of high-throughputdata, allowing for the creation of context-specific computa-tional models that have the potential to aid in identifyingmutant strains suitable for the overproduction of a metaboliteof commercial interest. As such, several algorithms have been
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developed for the integration of a variety of ‘‘omics’’ data,with special interest given to the integration of expression datadue to the wealth of transcriptomic data currently available.One such method, Gene Inactivity Moderated by Metabolismand Expression (GIMME), employs a user-defined threshold toenable the methodical removal of reactions below the thresh-old.121 Additional methods include the Integrative MetabolicAnalysis Tool (IMAT), which creates a functioning model forfluxes based on mRNA transcript levels without relying on auser-specified objective function, and Metabolic Adjustmentby Differential Expression (MADE), which uses the statisticalsignificance of changes in gene expression measurements toturn model genes on and off.122–123 Existing methods and in-tegration techniques are improving continually. The currentstate of such methods, including E-flux and ProbabilisticRegulation of Metabolism (PROM), has been reviewed byBlazier et al.124
The use of FBA, FVA, and in silico gene knockout simula-tions performed on algal metabolic network reconstructions isdiscussed in further detail in the following section. Other algo-rithms, such as the computationally efficient combinatorial geneknockout algorithms, and the methods for omics data integra-tion, have yet to be used with algae reconstructions, primarily
because genome-scale algae metabolic network reconstructiondevelopment and analysis is still in its infancy.125 Nevertheless,these algorithms have much potential to aid metabolic engi-neering strategies in genome-scale metabolic network recon-structions and provide biological insight into algal species.
Algal Metabolic Network ReconstructionsAlthough the genomes of at least 20 different algal species
have been sequenced (Fig. 1C; http://genome.jgi-psf.org),metabolic network modeling of algae remains a relativelyyoung field. As metabolic networks involve functional anno-tation of all known metabolic reactions, organisms with fullysequenced genomes remain the best candidates for compre-hensive network reconstructions. To date, genome-scale met-abolic network reconstructions have been generated for onlythree algal species: C. reinhardtii, O. lucimarinus, andO. tauri. Additionally, the metabolic pathways involved inliquid hydrogen production of B. braunii were reconstructedusing transcriptomic data in the absence of a fully sequencedgenome.17 A summary of the existing metabolic reconstruc-tions for these species is shown in Table 2, along with thecorresponding number of metabolites and reactions accountedfor in the models.17,126–131
Table 1. Genome and Pathway Resources Available for Metabolic Reconstructions
Prior to reconstructing the full genome-scale metabolic net-work of C. reinhardtii, several initial efforts focused on thereconstruction of the central metabolic network, with eachsuccessive iteration accounting for additional properties of thealgal species. The first published model for algae focused on thecentral metabolism of C. reinhardtii and included 458 intra-cellular metabolites and 484 metabolic reactions, accounting forfatty acid, amino acid, and nucleotide synthesis as well as gly-colysis and the citric acid cycle.126 This model accounted forreaction and metabolite localization in three compartments: thecytosol, the mitochondria, and the chloroplast. Using flux bal-ance analysis, the intracellular metabolic fluxes were predictedfor C. reinhardtii growth under three different conditions: au-totrophic, heterotrophic, and mixotrophic. Each of these threegrowth conditions was modeled by modifying the limits on CO2
and acetate uptake reactions as well as setting a constraint for theamount of light absorbed by the system.
Using an iterative methodology that combined bioinformaticsand experimental techniques, a second central metabolic net-work reconstruction, iAM303, was generated with 259 reactionsand 467 metabolites across several cellular compartments in-cluding the cytosol, mitochondria, chloroplast, glyoxysome, andflagellum.127 In order to validate the model, the results fromFBA after optimization of the network for either biomass oradenosine triphosphate (ATP) production were compared to
experimental values found in the literature. Additionally, in si-lico gene-knockout simulations and FVA were used to proposegenetic engineering strategies for increased hydrogen produc-tion. A non-compartmentalized model of the primary metabo-lism was also created by incorporating thermodynamicparameters with constraint-based modeling techniques to de-termine the extent of light-driven respiration.128 By constructinga two-step objective function, including both the growth rate ofcells and the photon uptake rate, this study allowed for simu-lation of biomass growth under low light conditions, revealinginteractions between respiratory activity and photosynthesis inC. reinhardtii.
More recently, two genome-scale metabolic network recon-structions have been generated for C. reinhardtii. One such re-construction, iRC1080, accounts for the function of 1,068metabolites and 2,190 reactions localized across 10 compart-ments, and was the first metabolic network reconstruction toaccount for photon absorption and growth simulations quanti-tatively under various light sources.129 Using the C. reinhardtiicentral metabolism reconstruction iAM303 as a starting point,Chang et al. added reactions on a pathway-by-pathway basisusing more than 250 publications. iRC1080 also significantlyexpanded the number of lipid metabolic pathways over pre-ceding metabolic reconstructions, enabling the application of insilico metabolic engineering strategies aimed at enhancing lipid
Fig. 2. Snapshot of the in silico metabolic engineering process of algal strains for industrial commodity production. Genome sequenc-ing allows for network formation and mathematical representation by a stoichiometric matrix. Computational tools may aid in the selectionof engineered strains for large industrial-scale commodity production.
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production. In order to validate the model, in silico geneknockout simulations were performed and subsequently com-pared to published data on mutant C. reinhardtii phenotypes.FBA and FVA were subsequently used to simulate adaptationsin metabolic response to changes in light influx for contributionof metabolic pathways to biomass production, including lipidsynthesis and protein production.132
AlgaGEM, another C. reinhardtii genome-scale metabolicnetwork reconstruction, accounts for the function of 1,869 me-tabolites and 1,725 metabolic reactions across several cellularcompartments such as the cytoplasm, the mitochondrion, theplastid, and the microbody. Compartmentalization was deter-mined from published literature or by comparing homologs to ametabolic model of the plant species A. thaliana. To identifygaps in the model, FBA was used to evaluate the model’s abilityto produce major biomass components under autotrophic, het-erotrophic, and mixotrophic conditions. AlgaGEM was subse-quently used to predict metabolic targets for the increasedproduction of H2 by performing in silico gene knockout simu-lations on the network.130
In addition to those curated for C. reinhardtii, genome-scalemetabolic network reconstructions have been generated for thegreen algae O. lucimarinus and O. tauri. Ostreococcus speciesare prevalent as microalgae and are ideal organisms to studybecause of their simplicity and phylogenetic relationship asearly-diverging green plants.131 Furthermore, O. tauri is be-coming popular as a model organism for metabolic studies as ithas the smallest eukaryotic genome and an unusually strongadaptation to nutrient stress, demonstrated in part by its ability torespond to low nitrogen conditions—suggesting a fast globalmetabolic response.133 To generate genome-scale metabolicnetwork reconstructions for both O. lucimarinus and O. tauri,
draft networks were first reconstructed for each species using theKEGG database. Subsequently, a sophisticated FBA-based gap-filling algorithm that took into account phylogenetic distanceamong species was applied to the two draft reconstructions,resulting in more complete and accurate networks. Additionally,this algorithm highlighted gaps in the annotation of genes forboth Ostreococcus species.
Finally, in the absence of a fully sequenced genome, a modelfor the organism B. braunii, a high oil-producing algal strain,was constructed using transcriptomic data and various databasesincluding KEGG, MetaCyc, and Reactome.17 The ability of B.braunii to produce large volumes of terpenoid liquid hydro-carbons, which are similar to crude fossil fuels, motivated thestudy of the genes and pathways involved in terpene production.Using a large-scale next-generation sequence read dataset, theDepartment of Energy Joint Genome Institute produced a met-abolic network through various annotation methods, includingthe B. braunii Showa web-based annotation tool, FrameDP, andthe NetStart 1.0 Server, to identify genes and pathways keyfor the production of terpene compounds and precursors. As aresult of this study, an annotated transcriptomic database forB. braunii was made publicly available online, paving the wayfor future in silico metabolic engineering analyses such as geneknockout simulations to optimize the production of terpenoidliquid hydrocarbons.
Simulations for the majority of C. reinhardtii models, in-cluding iAM303, iRC1080, and AlgaGEM, were completedwith the COBRA toolbox implemented in MATLAB, freelyavailable from the openCOBRA project (http://opencobra.sourceforge.net/).105 Each of the Ostreococcus models, onceconverted to SBML format, were then processed with theCOBRA toolbox v2.0.134
Table 2. Sequenced Genomes and Existing Metabolic Models for Algaea
aCompartments for each reconstruction are listed as follows: B. braunii was not compartmentalized; primary network of C. reinhardtii includes cytosol, mitochondria, and
chloroplast; iAM303 includes cytoplasm, endoplasmic reticulum, extracellular, Golgi apparatus, lysosome, mitochondria, nucleus, peroxisome, and plasma membrane;
C. reinhardtii thermodynamic model is not compartmentalized; iRC1080 includes mitochondria, glyoxysome, extracellular space, nucleus, Golgi apparatus, flagellum,
eyespot, thylakoid, chloroplast, and cytosol; AlgaGEM is divided into cytosol, mitochondria, plastid, and microbody (peroxisome). Both O. lucimarinus and O. tauri models
were not compartmentalized.
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Industrial Applications of BiotechnologyA key to making algae-to-energy systems viable and sus-
tainable in the long run will be to tie emerging metabolicnetwork models with the large-scale systems drivers that willdetermine the industrial relevance of these processes. Ulti-mately, the technical and economic viability of a scaled upalgae-based biotechnology sector, especially for products tobe produced in large quantities such as fuels, will dependlargely on the availability of land, water, nutrients, and CO2,as well as markets for byproducts, among other constraints.135
A great majority of existing algae biotechnology research hastaken for granted the supply chains and large-scale systemsthat will need to be put in place to support such an industry atrelevant scales.136 This is starting to change as life cycleanalysis and other tools from the emerging field of industrialecology are being used to try and understand more aboutancillary processes.137 By evaluating biological viability andindustrial sustainability in unison, in silico metabolic engi-neering and industrial ecology can be used to optimize pa-rameters in a way that maximizes the potential of industrialalgae processes.
A number of companies currently produce compounds fromalgae including ethanol, hydrogen food colorants, and biofuelprecursors.138 Several chemical companies, such as SyntheticGenomics (La Jolla, CA) and Sapphire Energy (San Diego, CA),have employed metabolic engineering strategies to improve fuelyields from algae.139 Solazyme (San Francisco, CA) has pro-duced and begun commercializing biobased fuels from hetero-trophic algae, although precise synthetic biology and metabolicengineering strategies have remained proprietary.140 Other algaeproduction companies and their methods for biofuel conversionhave been previously described.141 A large number of startupbiotechnology companies are also working to leverage compu-tational metabolic methods in the production of cosmetics,pharmaceuticals, and specialty chemicals; Rosetta Green (Re-hovot, Israel), for example, is working toward the successfulproduction of human growth hormone from algae.
ConclusionsEfforts to deploy metabolic optimization strategies are
limited primarily by the complexity inherent in understandingbiological systems. To overcome this complexity, efforts toleverage metabolic network models have tended to focus onsingle factor optimization. In the context of algae-biofuelproduction, for example, there has been a heavy focus on themaximization of lipid yields.14 Empirical data from years oflaboratory work suggest that very high lipid yields (>50%mass) are possible, but that these yields also result in lowergrowth rates.142 If the total lipid productivity is higher, eventhough the cell growth rate is lower, then such efforts tomaximize lipid production would be worthwhile. While tra-deoff analysis is relatively simple on a pathway-by-pathwaybasis, it becomes challenging for increasingly complex met-abolic systems. Metabolic network reconstructions along within silico analyses such as FVA can be used to aid in thetradeoff assessment for both simple and complex metabolicsystems.
A great opportunity exists to couple metabolic engineeringwith regional materials flow analysis. Material flow analysis is aquantitative tool for measuring material stocks and flows at thescale of regions or nations.143 Continuing on with the algae-biofuel example, this coupling would enable algae farmers toselect strains that are both regionally appropriate and capable ofachieving maximum profits under current market conditions.Knowing how water composition and average temperaturesaffect growth rates in a variety of strains could help an algaefarmer select a family of algae that would be the most viablestrains for their site. The developer could additionally refinestrain selection by considering which byproducts would be mostvaluable under prevailing market conditions. Obtaining thisinformation using traditional laboratory approaches would beimmensely expensive and limited because it would not allow forthe manipulation of algae cells for some generally desirabletraits; however, as genome-scale metabolic network recon-structions become more encompassing, they may be used toaddress such concerns as optimal strain selection.
Interestingly, the computational structure of most tools inindustrial ecology, including life cycle assessment and materialflow analysis, has parallels with metabolic network recon-structions. These models are generally comprised of large sys-tems of equations that describe material flows resulting fromdeliberate human activity in the economy or via indirect orundesirable pathways in the form of pollution.144 These systemsof equations can be manipulated depending on the systemboundaries or functional unit of interest.145 Over the past de-cade, methods for obtaining data that are temporally and spa-tially specific for material and energy flows have resulted inseveral large commercial databases that greatly facilitate thedevelopment of life cycle and material flow models.146 Thesimilarities between the numerical methods used in both prob-lems suggest that they could one day be integrated to yield algaestrain selection tools that span huge spatial scales.
There are certain phenotypes that will be desirable for allindustrially relevant algae. Most importantly, species thatmaximize available CO2 will be prized given how difficult itwill be to set up a CO2 supply chain to deliver CO2 to large-scale algae cultivation facilities. Most analyses have tended toassume that CO2 will be obtained from a large point source,such as a coal or natural gas-burning power plant.147 Coal-firedplants are ubiquitous and their flue gas contains high levels(*12%) of CO2. The flue gas tends to be dirty, however,containing significant particulate matter such as SO2 and NOx,which can impact the pH of pond water if it is not remediatedfirst. Flue gas from natural gas power plants is much cleanerbut it has lower levels of CO2 and, consequently, the costs ofseparating out the CO2 are higher. Given how involved it willlikely be to supply large industrial facilities with CO2, de-signing algae to use CO2 more efficiently would improve theefficiency and bottom line of the company.148 In silico geneknockout simulations could be performed on metabolic net-work reconstructions using algorithms like OptKnock andMOMA to aid in the design process of mutant algae that moreefficiently metabolize CO2.
Other important phenotypic characteristics include the algaespecies’ ability to tolerate high salinity growth media; produce
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compounds that would enable them to outcompete invasivespecies; remain neutrally buoyant in growth ponds; or be ca-pable of autoflocculation.149 These characteristics, which can beunderstood in part in the context of metabolic network models,will have important ramifications on the engineering processesnecessary to use algae for biofuels production. Employing thesemetabolic network models will help ensure that genetic modi-fications are being carried out in the most rational and efficientway possible, and that the most appropriate tools are being de-veloped for the burgeoning industry.
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Address correspondence to:Jason A. Papin, PhD
Department of Biomedical EngineeringUniversity of Virginia School of Medicine
