Flux balance analysis of primary metabolism in Chlamydomonas reinhardtii
© Boyle and Morgan; licensee BioMed Central Ltd. 2009
Received: 12 August 2008
Accepted: 07 January 2009
Published: 07 January 2009
Photosynthetic organisms convert atmospheric carbon dioxide into numerous metabolites along the pathways to make new biomass. Aquatic photosynthetic organisms, which fix almost half of global inorganic carbon, have great potential: as a carbon dioxide fixation method, for the economical production of chemicals, or as a source for lipids and starch which can then be converted to biofuels. To harness this potential through metabolic engineering and to maximize production, a more thorough understanding of photosynthetic metabolism must first be achieved. A model algal species, C. reinhardtii, was chosen and the metabolic network reconstructed. Intracellular fluxes were then calculated using flux balance analysis (FBA).
The metabolic network of primary metabolism for a green alga, C. reinhardtii, was reconstructed using genomic and biochemical information. The reconstructed network accounts for the intracellular localization of enzymes to three compartments and includes 484 metabolic reactions and 458 intracellular metabolites. Based on BLAST searches, one newly annotated enzyme (fructose-1,6-bisphosphatase) was added to the Chlamydomonas reinhardtii database. FBA was used to predict metabolic fluxes under three growth conditions, autotrophic, heterotrophic and mixotrophic growth. Biomass yields ranged from 28.9 g per mole C for autotrophic growth to 15 g per mole C for heterotrophic growth.
The flux balance analysis model of central and intermediary metabolism in C. reinhardtii is the first such model for algae and the first model to include three metabolically active compartments. In addition to providing estimates of intracellular fluxes, metabolic reconstruction and modelling efforts also provide a comprehensive method for annotation of genome databases. As a result of our reconstruction, one new enzyme was annotated in the database and several others were found to be missing; implying new pathways or non-conserved enzymes. The use of FBA to estimate intracellular fluxes also provides flux values that can be used as a starting point for rational engineering of C. reinhardtii. From these initial estimates, it is clear that aerobic heterotrophic growth on acetate has a low yield on carbon, while mixotrophically and autotrophically grown cells are significantly more carbon efficient.
Algae and other marine organisms are responsible for the fixation of almost half of the inorganic carbon from the atmosphere . With rising atmospheric carbon dioxide levels, knowledge of how photosynthetic organisms convert atmospheric carbon dioxide into metabolites and other important compounds is becoming increasingly important. Not only do these organisms fix carbon dioxide, but they also have the potential to be used for the production of inexpensive bulk chemicals because the major inputs into the system (light and CO2) are essentially free. However, to harness this potential through metabolic engineering, a deeper understanding of photosynthetic metabolism is required.
There are several widely accepted methods for modelling metabolism, ranging from highly detailed kinetic models to less complex stoichiometric models. One of the more increasingly used methods is flux balance analysis (FBA), which has the ability to predict fluxes using linear programming with the knowledge of reaction stoichiometry, biomass composition and additional constraints, such as limits on uptake/excretion rates and thermodynamic constraints. FBA has been used for a number of model organisms [2–7] to predict fluxes and viability of knockouts. FBA can also be used for rational strain design, both to predict theoretical yields and to identify bottlenecks or sinks in metabolism that need to be altered to achieve the theoretical yield [8, 9].
FBA has been previously used to model photosynthetic metabolism in a model cyanobacteria, Synechocystis . In an earlier related study, the metabolic network of another cyanobacterium, Arthrospira platensis, was reconstructed and fluxes computed . The goal of the current study was not only to model photosynthetic metabolism, but to model it in a higher eukaryote in order to have a model more representative of plant metabolism. Therefore, Chlamydomonas reinhardtii was chosen as a representative algal species for this study. C. reinhardtii has been used as a model organism to study numerous cellular functions from photosynthesis research to flagellar function and assembly  and most recently a metabolomics and proteomics approach to genome annotation . It has served as a bridge between higher plants and cyanobacteria in the field of photosynthetic research due to the relative simplicity of the cell structure and metabolism while being more comparable to higher plants. C. reinhardtii was the first algal species to have its genome sequenced  and this has provided researchers with an abundance of data on genes and their functions. Another advantage of C. reinhardtii is that its photosynthetic capability is dispensable; as it can grow heterotrophically on acetate. However, as an acetate flagellate, it can only grow on acetate and similar 2-carbon molecules in the dark. In the presence of light, C. reinhardtii can metabolize pentoses and hexoses (mixotrophic growth) as well as acetate  and supports autotrophic growth using carbon dioxide as the carbon source.
The major contribution of this work is the reconstruction of a compartmental metabolic network for primary metabolism in the green alga, C. reinhardtii. The metabolic network was reconstructed using the genomic database , biochemical texts [15–17], metabolic pathway databases [18, 19], and archival journal articles (See methods section for specific articles). Localization of enzymes in the cell was proposed using bioinformatic software [20, 21]. FBA was then used to predict flux distributions for three conditions: autotrophic, heterotrophic and mixotrophic growth.
Results and discussion
Missing enzymes from the C. reinhardtii database
F6P + ATP --> F16P + ADP
F16P + H2O --> F6P + Pi
F6P_c + ATP_c --> F16P_c + ADP_c
ATP_c + H2O_c --> ADP_c + Pi_c
ATP_m+ H2O_m --> ADP_m + Pi_m
Glycerate_c + ATP_c → 3PG_c + ADP_c
DAP synthase, KDPH Synthetase
PEP_c + E4P_c + H2O_c --> DDP_c
DHN + 2 H2O --> DHDN + PP + Pi
HMD + ATP --> DHT + AMP
4AD --> PYR + ABZ
Glyceraldehyde + ATP --> GAP + ADP
Hser + AcCoA --> OAH + CoA
SucCoA_c + Hser_c --> CoA_c + OSH_c
Indole-3-glycerol Phosphate Synthase
CPDRP_c --> I3GP_c + H2O_c + CO2_c
HOLP_c + H2O_c --> HOL_c + Pi_c
CMP_c + ATP_c --> CDP_c + ADP_c
ATP_c + Formate_c + THF_c --> ADP_c + Pi_c + FTHF_c
Localization of enzymes and metabolites
Biomass formation equation
Dry weight composition
Biomass formation equations
Biomass Formation Equation (moles/kg biomass)
Central metabolism flux maps
Flux maps for three growth conditions (auto-, hetero-, and mixotrophic) were calculated using the reconstructed network and FBA. During autotrophic growth, the cell fixes carbon dioxide by converting light into cellular energy (reducing equivalents and ATP). In this study we defined heterotrophic growth as aerobic growth on acetate in the dark; the cell using acetate for both carbon and energy sources. Another metabolic mode, mixotrophic growth, is the link between the two extremes. In our model, mixotrophic growth has three inputs: light, acetate and carbon dioxide.
Quantitative results for all three growth regimes as well as reaction lists can be found at http://cobweb.ecn.purdue.edu/~jamorgan.
Comparison of yields
Yield (g biomass/mole carbon)
Increases with increasing light flux from 13.5 to a maximum of 22.9
Comparison to a model photosynthetic microbe
Comparison of selected fluxes to Synechocystis
Autotrophic growth fluxes (moles/100 moles CO2/kg biomass)
Cyclic ETC (photons)
Non-cyclic ETC (photons)
O 2 released
A stoichiometric model of primary metabolism was constructed for C. reinhardtii from the genomic database, pathway databases and literature. The network includes all the major pathways in central metabolism (glycolysis, TCA cycle, oxidative and reductive pentose phosphate pathways) as well as amino acid, nucleotide, chlorophyll, lipid and starch synthesis. Metabolic network reconstruction is a valuable tool to identify gaps in existing knowledge [4, 5, 29–31]. As a result of the reconstruction process, one new gene was annotated and 16 other genes were identified to be missing, implying either non-conserved amino acids sequences or possibly new pathways. Despite being incomplete, it is the first model of a eukaryotic photosynthetic organism to include central and intermediary metabolism with three metabolically active compartments.
Intracellular fluxes were estimated using FBA for three growth conditions, autotrophic, heterotrophic, and mixotrophic. Yield on carbon and growth rate are factors that need to be considered in choosing the appropriate growth conditions for maximizing the production of desired metabolites. For example, a lower yield for heterotrophic growth is off-set by a faster growth rate, which may be ideal for the production of growth associated products. Along with this, the model provides a more complete picture of photosynthesis in a compartmented organism and can serve as a starting point for models of other photosynthetic algae and more complex models of higher photosynthetic organisms.
With renewed interest in biofuel production from algae  the reconstructed network of C. reinhardtii presented here can serve as a starting point for metabolic engineering of lipid or starch production in algae. Future work will use elementary mode analysis  to determine if multiple pathways that lead to the same optimum exist, which is highly likely due to the size and complexity of the network.
A stoichiometric model of the primary metabolism of C. reinhardtii was constructed using the genomic database , pathway databases [18, 34], biochemistry texts [15–17, 35] and archival journal articles. The reconstruction process began with a search of the genome database for reactions in the metabolic pathways to be modelled. This included the following pathways: glycolysis, gluconeogenesis, pentose phosphate pathway (oxidative and reductive), TCA cycle, photorespiration, glycolate cycle (recycles 2-phosphoglycolate to 3-phosphoglycerate in plants) and the biosynthesis of amino acids, chlorophyll, nucleotides, starch and lipids. The reversibility of reactions was also assigned during this initial search; if no information was available, reactions were assumed to be reversible. Starch and lipid metabolism were simplified by making a few assumptions. An average chain length of 50 was assumed for starch based on typical values that range from 3–1000 for amylase chains and 3–50 for amylopectin . Fatty acid synthesis reactions were added to represent the synthesis of hexadecanoic and octadecanoic acids as well as their corresponding unsaturated fatty acids (16:1, 16:2, 16:3, 16:4, 18:1, 18:2, 18:3, 18:4), which represent the majority of fatty acids present in Chlamydomonas reinhardtii . The synthesis of the major classes of lipids (MGDG, DGDG, SQDG, PG, PI, DGTS, and PE) were included based on average lipid composition for each head group and the localization of each lipid was based on the distribution of C16 and C18 in the C1 and C2 position on the head group . Although detailed lipid synthesis reactions were included in the network reconstruction, lipids were lumped into a single representative lipid to simplify the FBA simulations. An 'average' lipid made up of unsaturated C18 fatty acids and a glycerol head group was assumed based on the largest percentage of lipids being C18 as reported by Janero and Barrnett  and is shown in reaction 179 in additional file 2.
Despite large efforts to fully annotate genome databases, not all enzymatic functions are listed and therefore gaps exist in the pathways. Gaps in the network were first addressed by searching pathway databases for the missing enzymes and corresponding genes in other organisms whose genome is sequenced (A. thaliana, E. coli, and S. cerevisiae). The amino acid sequence of these known genes were then blasted against the C. reinhardtii database; in most cases, this resulted in a hit which had already been annotated but not linked to the KEGG portion of the genome database. There were a few genes that resulted in hits to proteins that were either listed as having a different function or were not annotated at all (see results). A few enzymes resulted in no hits in the database, but were assumed to be present in order to have a complete network.
Linear programming formulation
where sij is the stoichiometric coefficient of the ith metabolite in the jth reaction, vj is the flux of the jth reaction, Mi is the set of intracellular metabolites, Mr is the set of reactants other than substrate, and Mp is the set of products excreted.
A mixed integer linear program was formulated in the GAMS environment (GAMS Development Corporation, Washington, DC) and the optimum solution was found using the ILOG CPLEX 8.100 solver (ILOG, Inc. Mountain View, CA).
Unlike heterotrophic organisms that utilize the same substrate as the source of both carbon and energy, photoautotrophic organisms require two substrates, one for energy (light) and one for carbon (carbon dioxide). Due to the input of two substrates, FBA simulations can be run in either light or carbon limitation conditions. To simulate carbon limitation, the model is allowed unlimited light, which calculates an optimal biomass flux. No fermentation products were detected in the media during growth (see maximum uptake rates discussion) therefore, in the absence of carbon overflow products, the yield of biomass is fixed because the only outlet for carbon is biomass. For photoautotrophic metabolism, a more meaningful result is to find the flux distribution that maximizes biomass while minimizing energy usage. Therefore the optimization is done in two steps. The first step is to maximize biomass with no constraint on light and the second is to fix the biomass and minimize light. Flux distributions for the heterotrophic case are the result of a one-step optimization to maximize biomass.
Chlamydomonas reinhardtii strain CC-400 cw15 mt+ was acquired from the Chlamydomonas Genetics Center. Cells were cultivated at 25°C in 250 ml flasks with a working volume of 50 ml and an agitation rate of 200 RPM. Heterotrophic and mixotrophic cells were grown in TAP media  and autotrophic cells were grown in similar media without addition of acetic acid. Mixotrophic and autotrophic cultures were grown under constant illumination at an average fluence rate of 65 μE/m2/s. All cells were grown in the presence of atmospheric carbon dioxide levels. Growth was monitored spectrophotometrically by measuring absorbance at 750 nm.
Maximum uptake rates
Specific growth rates
Growth Rate (hr-1)
0.035 ± 0.002
0.059 ± 0.001
0.066 ± 0.007
Estimation of cell surface area
In order to convert the calculated total photons from the model to a flux (μE/m2/s), the surface area per kilogram biomass must be calculated. Based on experimental measurements, the dry weight of a typical C. reinhardtii was determined to be 0.2 pg. The length and width of cell were assumed to be 10 μm and 3 μm respectively  based on literature values. The geometry of the cell was assumed to be a prolate spheroid. The surface area per kilogram biomass was then calculated to be 389 m2kg-1.
Growth associated and non-growth associated maintenance requirements were also included in the model. Growth associated energy is included to account for partially unknown energy requirements for transport, biosynthesis and polymerization  while non-growth associated accounts for cellular maintenance operations such as DNA repair, cell wall maintenance, and pH control. Growth associated maintenance was found to be 29.89 mmol ATP/g biomass by fitting the model to the experimentally determined biomass yield by changing the ATP requirement. This value falls into the range of published values for growth associated maintenance values [2–7]. Autotrophic and mixotrophic maintenance requirements were assumed to be the same. Non-growth associated maintenance requirements range from 0.36 mmol ATP/g DW hr for Lactobacillus plantarum to 7.60 mmol ATP/g DW for E. coli. For C. reinhardtii, non-growth associated maintenance was assumed to be 1.50 mmol ATP/g DW [3, 5, 7].
The biomass composition was determined separately for each of the three growth regimes: autotrophic, mixotrophic and heterotrophic growth. Lipids were measured using the chloroform-methanol extraction method of Ishida et al. . The resulting water layer and pellet were then dried and resuspended in 0.2 N NaOH and diluted by a factor of 5. This solution was then assayed for protein content with the Pierce BCA protein assay kit (Pierce Biotechnology, Inc. Rockford, IL). The amino acid composition (Additional file 3) was estimated from Gas chromatography-mass spectrometry (GC-MS) analysis of hydrolyzed protein (data not shown). Chlorophyll a and b were measured  and subtracted from the total lipid measurement. DNA and RNA were assumed to be constant for all growth conditions; the DNA content was determined by Chiang et al to be 1.23 × 10-7 μg per cell and the RNA content was assumed to be 28-fold higher than the DNA content . The GC content of DNA was measured to be 62.1%  and the same GC content was assumed for RNA. Carbohydrate composition was calculated as the balance of the fraction dry weight. The elemental composition of lyophilized cells was also determined. In all cases except elemental composition, experiments were done in triplicate.
Basic Local Alignment Search Tool
Electron transport chain
Flux Balance Analysis
Gas chromatography mass spectrometry
High performance liquid chromatography
Kyoto Encyclopedia of Genes and Genomes
Nicotinamide adenine dinucleotide
Nicotinamide adenine dinucleotide phosphate
The authors would like to thank Avantika Shastri for her assistance in analyzing amino acid compositions and valuable discussion. This material is based upon work supported under a National Science Foundation Graduate Research Fellowship awarded to NRB and the NSF CAREER award to JAM (BES-0348458).
- Field CB, Behrenfeld MJ, Randerson JT, Falkowski P: Primary Production of the Biosphere: Integrating Terrestrial and Oceanic Components. Science. 1998, 281 (5374): 237-240. 10.1126/science.281.5374.237View ArticlePubMed
- Borodina I, Krabben P, Nielsen J: Genome-scale analysis of Streptomyces coelicolor A3(2) metabolism. Genome Res. 2005, 15 (6): 820-829. 10.1101/gr.3364705PubMed CentralView ArticlePubMed
- Famili I, Forster J, Nielsen J, Palsson BO: Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network. Proc Natl Acad Sci USA. 2003, 100 (23): 13134-13139. 10.1073/pnas.2235812100PubMed CentralView ArticlePubMed
- Heinemann M, Kümmel A, Ruinatscha R, Panke S: In silico genome-scale reconstruction and validation of the Staphylococcus aureus metabolic network. Biotech Bioeng. 2005, 92 (7): 850-864. 10.1002/bit.20663.View Article
- Oliveira A, Nielsen J, Forster J: Modeling Lactococcus lactis using a genome-scale flux model. BMC Microbiology. 2005, 5 (1): 39- 10.1186/1471-2180-5-39PubMed CentralView ArticlePubMed
- Reed J, Vo T, Schilling C, Palsson B: An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol. 2003, 4 (9): R54- 10.1186/gb-2003-4-9-r54PubMed CentralView ArticlePubMed
- Teusink B, Wiersma A, Molenaar D, Francke C, Vos WMd, Siezen RJ, Smid EJ: Analysis of Growth of Lactobacillus plantarum WCFS1 on a Complex Medium Using Genome-scale Metabolic Model. J Biol Chem. 2006, 281 (52): 40041-40048. 10.1074/jbc.M606263200View ArticlePubMed
- Burgard A, Maranas C: Probing the performance limits of the Escherichia coli metabolic network subject to gene additions or deletions. Biotech Bioeng. 2001, 74: 364-375. 10.1002/bit.1127.View Article
- Shastri AA, Morgan JA: Flux Balance Analysis of Photoautotrophic Metabolism. Biotechnol Prog. 2005, 21 (6): 1617-1626. 10.1021/bp050246dView ArticlePubMed
- Cogne G, Gros JB, Dussap CG: Identification of a metabolic network structure representative of arthrospira (spirulina) platensis metabolism. Biotech Bioeng. 2003, 84 (6): 667-676. 10.1002/bit.10808.View Article
- Rochaix J-D: Chlamydomonas reinahrdtii as the Photosynthetic Yeast. Annual Review of Genetics. 1995, 29: 209-230. 10.1146/annurev.ge.29.120195.001233View ArticlePubMed
- May P, Wienkoop S, Kempa S, Usadel B, Christian N, Rupprecht J, Weiss J, Recuenco-Munoz L, Ebenhoh O, Weckwerth W: Metabolomics- and Proteomics-Assisted Genome Annotation and Analysis of the Draft Metabolic Network of Chlamydomonas reinhardtii. Genetics. 2008, 179 (1): 157-166. 10.1534/genetics.108.088336PubMed CentralView ArticlePubMed
- Merchant SS, Prochnik SE, Vallon O, Harris EH, Karpowicz SJ, Witman GB, Terry A, Salamov A, Fritz-Laylin LK, Marechal-Drouard L: The Chlamydomonas Genome Reveals the Evolution of Key Animal and Plant Functions. Science. 2007, 318 (5848): 245-250. 10.1126/science.1143609PubMed CentralView ArticlePubMed
- Harris E: Chlamydomonas as a Model Organism. Annual Review of Plant Physiology. 2001, 52: 363-406. 10.1146/annurev.arplant.52.1.363.View Article
- Nelson DL, Cox MM: Lehninger Principles of Biochemistry. 2005, New York: W.H. Freeman and Company, Fourth
- Garrett RH, Grisham CM: Biochemistry. 1999, Fort Worth: Saunders College Publishing, Second
- Buchanan BB, Gruissem W, Jones RL, : Biochemistry & Molecular Biology of Plants. 2000, Rockville, Maryland: American Society of PLant Physiologists
- , : MetaCyc: A Multiorganism Database of Metabolic Pathways and Enzymes. Nucleic Acids Res. 2006, 34: D511-D516. 10.1093/nar/gkj128PubMed CentralView ArticlePubMed
- Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucl Acids Res. 1999, 27 (1): 29-34. 10.1093/nar/27.1.29PubMed CentralView ArticlePubMed
- Emanuelsson O, Nielsen H, Brunak S, Heijne Gv: Predicting Subcellular Localization of Proteins based on Their N-terminal Amino Acid Sequence. Journal of Molecular Biology. 2000, 300: 1005-1016. 10.1006/jmbi.2000.3903View ArticlePubMed
- Claros MG, Vincens P: Computational method to predict mitochondrially imported proteins and their targeting sequences. Eur J Biochem. 1996, 241: 779-786. 10.1111/j.1432-1033.1996.00779.xView ArticlePubMed
- Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol. 1990, 215 (3): 403-410.View ArticlePubMed
- Neidhardt FC, Ingraham JL, Schaechter M: Physiology of the Bacterial Cell: A Molecular Approach. 1990, Sunderland, Massachusetts: Sinauer Associates, Inc
- Burris JE: Effects of oxygen and inorganic carbon concentrations on the photosynthetic quotients of marine algae. Marine Biology. 1981, 65 (3): 215-219. 10.1007/BF00397114.View Article
- Kornberg HL: The role and control of the glyoxylate cycle in Escherichia col. Biochem J. 1966, 99: 1-11.PubMed CentralView ArticlePubMed
- Flavell RB, Woodward DO: Metabolic Role, Regulation of Synthesis, Cellular Localization, and Genetic Control of the Glyoxylate Cycle Enzymes in Neurospora crassa. J Bacteriol. 1971, 105 (1): 200-210.PubMed CentralPubMed
- Schwender J, Seemann M, Lichtenthaler HK, Rohmer M: Biosynthesis of isoprenoids (carotenoids, sterols, prenyl side-chains of chlorophylls and plastoquinone) via a novel pyruvate/glyceraldehyde 3-phosphate non-mevalonate pathway in the green alga Scenedesmus obliquus. Biochem J. 1996, 316 (1): 73-80.PubMed CentralView ArticlePubMed
- Eastmond PJ, Germain V, Lange PR, Bryce JH, Smith SM, Graham IA: Postgerminative growth and lipid catabolism in oilseeds lacking the glyoxylate cycle. Proc Natl Acad Sci USA. 2000, 97 (10): 5669-5674. 10.1073/pnas.97.10.5669PubMed CentralView ArticlePubMed
- Becker S, Palsson B: Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation. BMC Microbiology. 2005, 5 (1): 8- 10.1186/1471-2180-5-8PubMed CentralView ArticlePubMed
- Duarte NC, Herrgard MJ, Palsson BO: Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. Genome Res. 2004, 14: 1298-1309. 10.1101/gr.2250904PubMed CentralView ArticlePubMed
- Oh Y-K, Palsson BO, Park SM, Schilling CH, Mahadevan R: Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data. J Biol Chem. 2007, 282 (39): 28791-28799. 10.1074/jbc.M703759200View ArticlePubMed
- Chisti Y: Biodiesel from microalgae. Biotechnol Advances. 2007, 25 (3): 294-306. 10.1016/j.biotechadv.2007.02.001.View Article
- Schuster S, Dandekar T, Fell DA: Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. Trends Biotechnol. 1999, 17 (2): 53-60. 10.1016/S0167-7799(98)01290-6View ArticlePubMed
- Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, Katayama T, Araki M, Hirakawa M: From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 2006, 34: D354-357. 10.1093/nar/gkj102PubMed CentralView ArticlePubMed
- Michal G, : Biochemical Pathways: An Atlas of Biochemistry and Molecular Biology. 1999, New York: John Wiley & Sons, Inc
- Ball SG: Regulation of Starch Biosynthesis. The Molecular Biology of Chloroplasts and Mitochondria in Chlamydomonas. Edited by: Rochaix JD, Goldschmidt-Clermont M, Merchant S. 1998, 549-567. Dordrecht: Kluwer Academic Publishers
- Giroud C, Gerber A, Eichenberger W: Lipids of Chlamydomonas reinhardtii. Analysis of Molecular Species and Intracellular Site(s) of Biosynthesis. Plant Cell Physiol. 1988, 29 (4): 587-595.
- Janero DR, Barrnett R: Cellular and thylakoid-membrane glycolipids of Chlamydomonas reinhardtii 137+. J Lipid Res. 1981, 22 (7): 1119-1125.PubMed
- Allen JF: Photosynthesis of ATP – Electrons, Proton Pumps, Roters and Poise. Cell. 2002, 110: 273-276. 10.1016/S0092-8674(02)00870-XView ArticlePubMed
- Stock D, Leslie AGW, Walker JE: Molecular Architecture of the Rotary Motor in ATP Synthase. Science. 1999, 286: 1700-1705. 10.1126/science.286.5445.1700View ArticlePubMed
- Forster J, Famili I, Fu P, Palsson BO, Nielsen J: Genome-Scale Reconstruction of the Saccharomyces cerevisiae Metabolic Network. Genome Research. 2003, 13: 244-253. 10.1101/gr.234503PubMed CentralView ArticlePubMed
- Stephanopoulos GN, Aristidou AA, Nielsen J: Metabolic Engineering Principles and Methodologies. 1998, San Diego: Academic press
- Harris EH: The Chlamydomonas Sourcebook: A Comprehensive Guide to Biology and Laboratory Use. 1989, San Diego: Academic Press, Inc
- Metting FB, Smith J, Amthor J, Izaurralde RC: Science Needs and New Technology for Increasing Soil Carbon Sequestration. Climatic Change. 2001, 51 (1): 11-34. 10.1023/A:1017509224801.View Article
- Ishida T, Hasegawa N, Hayashi NR, Peerapornpisal Y, Ishii M, Igarashi Y, Kodama T: Growth Characteristics and Dense Culture of a Thermophilic Cyanobacterium, Chroococcidiopsis sp. Strain TS-821. Journal of Fermentation and Bioengineering. 1997, 83 (6): 571-576. 10.1016/S0922-338X(97)81139-7.View Article
- Chiang K-S, Sueoka N: Replication of Chloroplast DNA in Chlamydomonas reinhardi during Vegetative Cell Cycle: Its Mode and Regulation. Proc Natl Acad Sci USA. 1967, 57 (5): 1506-1513. 10.1073/pnas.57.5.1506PubMed CentralView ArticlePubMed
- Valle O, Lien T, Knutsen G: Fluorometric determination of DNA and RNA in Chlamydomonas using ethidium bromide. Journal of Biochemical and Biophysical Methods. 1981, 4: 271-277. 10.1016/0165-022X(81)90067-1.View ArticlePubMed
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.