Current Status and Applications of Genome-Scale Metabolic Models of Oleaginous Microorganisms

Zijian Hu , Jinyi Qian , Yuzhou Wang , Chao Ye

Food Bioengineering ›› 2024, Vol. 3 ›› Issue (4) : 492 -511.

PDF
Food Bioengineering ›› 2024, Vol. 3 ›› Issue (4) : 492 -511. DOI: 10.1002/fbe2.12113
REVIEW ARTICLE

Current Status and Applications of Genome-Scale Metabolic Models of Oleaginous Microorganisms

Author information +
History +
PDF

Abstract

Oleaginous microorganisms have the unique ability to accumulate lipids that can exceed 20% of their dry cell weight under certain conditions. Despite their potential for efficient lipid production, the metabolic pathways involved are not yet fully understood, largely due to the complexity of intracellular processes and the challenges in phenotypic prediction. This review synthesizes the latest research on the application of Genome-scale Metabolic Network Models (GSMMs) to study oleaginous microorganisms, including bacteria, cyanobacteria, yeast, microalgae, and fungi, and provides a comprehensive analysis of how GSMMs have been utilized to decipher the metabolic mechanisms behind lipid accumulation and to identify key genes involved in lipid synthesis. The review highlights the role of GSMMs in predicting cellular behavior, optimizing metabolic engineering strategies, and discusses the future directions and potential of GSMMs in enhancing lipid production in microorganisms. This comprehensive overview not only summarizes the current state of research but also identifies gaps and opportunities for further investigation in the field.

Keywords

cell phenotype / genome-scale metabolic network model / lipid production / metabolic engineering / oleaginous microorganisms

Cite this article

Download citation ▾
Zijian Hu, Jinyi Qian, Yuzhou Wang, Chao Ye. Current Status and Applications of Genome-Scale Metabolic Models of Oleaginous Microorganisms. Food Bioengineering, 2024, 3(4): 492-511 DOI:10.1002/fbe2.12113

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Abraham, T. W., and R. Höfer. 2012. “ 10.03 - Lipid-Based Polymer Building Blocks and Polymers.” In Polymer Science: A Comprehensive Reference, edited by K. Matyjaszewski and M. Möller, 15–58. Amsterdam: Elsevier.

[2]

Ageitos, Ageitos, J. A. Vallejo, P. Veiga-Crespo, and T. G. Villa. 2011. “Oily Yeasts as Oleaginous Cell Factories.” Applied Microbiology and Biotechnology 90, no. 4: 1219–1227.

[3]

Archanaa, Archanaa, S. Jose, A. Mukherjee, and G. K. Suraishkumar. 2019. “Sustainable Diesel Feedstock: A Comparison of Oleaginous Bacterial and Microalgal Model Systems.” Bioenergy Research 12, no. 1: 205–216.

[4]

Armbrust, Armbrust, J. A. Berges, C. Bowler, et al. 2004. “The Genome of the Diatom Thalassiosira Pseudonana:: Ecology, Evolution, and Metabolism.” Science 306, no. 5693: 79–86.

[5]

Aung, Aung, S. A. Henry, and L. P. Walker. 2013. “Revising the Representation of Fatty Acid, Glycerolipid, and Glycerophospholipid Metabolism in the Consensus Model of Yeast Metabolism.” Industrial Biotechnology (New Rochelle, N.Y.) 9, no. 4: 215–228.

[6]

Ayudhya, Ayudhya, K. Laoteng, Y. D. Song, A. Meechai, and W. Vongsangnak. 2019. “Metabolic Traits Specific for Lipid-Overproducing Strain of Mucor Circinelloides WJ11 Identified by Genome-Scale Modeling Approach.” PeerJ (Corta Madera, CA and London) 7: e7015.

[7]

Balagurunathan, Balagurunathan, V. K. Jain, C. J. Y. Tear, C. Y. Lim, and H. Zhao. 2017. “In Silico Design of Anaerobic Growth-Coupled Product Formation in Escherichia Coli: Experimental Validation Using a Simple Polyol, Glycerol.” Bioprocess and Biosystems Engineering 40, no. 3: 361–372.

[8]

Baroukh, Baroukh, R. Muñoz-Tamayo, J.-P. Steyer, and O. Bernard. 2013. “A New Framework for Metabolic Modeling Under Non-Balanced Growth. Application to Carbon Metabolism of Unicellular Microalgae.” IFAC Proceedings Volumes 46, no. 31: 107–112.

[9]

Beopoulos, Beopoulos, J. M. Nicaud, and C. Gaillardin. 2011. “An Overview of Lipid Metabolism in Yeasts and Its Impact on Biotechnological Processes.” Applied Microbiology and Biotechnology 90, no. 4: 1193–1206.

[10]

Bommareddy, Bommareddy, W. Sabra, G. Maheshwari, and A. P. Zeng. 2015. “Metabolic Network Analysis and Experimental Study of Lipid Production in Rhodosporidium Toruloides Grown on Single and Mixed Substrates.” Microbial Cell Factories 14: 36.

[11]

Boyle, N. R., and J. A. Morgan. 2009. “Flux Balance Analysis of Primary Metabolism in Chlamydomonas Reinhardtii.” BMC Systems Biology 3: 4.

[12]

Boyle, Boyle, N. Sengupta, and J. A. Morgan. 2017. “Metabolic Flux Analysis of Heterotrophic Growth in Chlamydomonas Reinhardtii.” PLoS One 12, no. 5: e0177292.

[13]

Broddrick, Broddrick, B. E. Rubin, D. G. Welkie, et al. 2016. “Unique Attributes of Cyanobacterial Metabolism Revealed by Improved Genome-Scale Metabolic Modeling and Essential Gene Analysis.” Proceedings of the National Academy of Sciences of the United States of America 113, no. 51: E8344–E8353.

[14]

Camacho-Rodríguez, Camacho-Rodríguez, A. M. González-Céspedes, M. C. Cerón-García, J. M. Fernández-Sevilla, F. G. Acién-Fernández, and E. Molina-Grima. 2014. “A Quantitative Study of Eicosapentaenoic Acid (EPA) Production by Nannochloropsis Gaditana for Aquaculture as a Function of Dilution Rate, Temperature and Average Irradiance.” Applied Microbiology and Biotechnology 98, no. 6: 2429–2440.

[15]

Castañeda, Castañeda, S. Nuñez, F. Garelli, C. Voget, and H. D. Battista. 2018. “Comprehensive Analysis of a Metabolic Model for Lipid Production in Rhodosporidium Toruloides.” Journal of Biotechnology 280: 11–18.

[16]

Chaiboonchoe, Chaiboonchoe, B. S. Dohai, H. Cai, D. R. Nelson, K. Jijakli, and K. Salehi-Ashtiani. 2014. “Microalgal Metabolic Network Model Refinement Through High-Throughput Functional Metabolic Profiling.” Frontiers in Bioengineering and Biotechnology 2: 68.

[17]

Chang, Chang, L. Ghamsari, and A. Manichaikul, et al. 2011. “Metabolic Network Reconstruction of Chlamydomonas Offers Insight Into Light-Driven Algal Metabolism.” Molecular Systems Biology 7: 518.

[18]

Chen, Chen, F. R. Li, and J. Nielsen. 2022. “Genome-Scale Modeling of Yeast Metabolism: Retrospectives and Perspectives.” FEMS Yeast Research 22, no. 1: foac003.

[19]

Chowdhury, Chowdhury, A. Chowdhury, and C. D. Maranas. 2015. “Using Gene Essentiality and Synthetic Lethality Information to Correct Yeast and CHO Cell Genome-Scale Models.” Metabolites 5, no. 4: 536–570.

[20]

Christian, Christian, P. May, S. Kempa, T. Handorf, and O. Ebenhoh. 2009. “An Integrative Approach Towards Completing Genome-Scale Metabolic Networks.” Molecular BioSystems 5, no. 12: 1889–1903.

[21]

Cogne, Cogne, M. Rugen, A. Bockmayr, et al. 2011. “A Model-Based Method for Investigating Bioenergetic Processes in Autotrophically Growing Eukaryotic Microalgae: Application to the Green Algae Chlamydomonas Reinhardtii.” Biotechnology Progress 27, no. 3: 631–640.

[22]

Czajka, Czajka, Y. C. Han, and J. Kim, et al. 2024. “Genome-Scale Model Development and Genomic Sequencing of the Oleaginous Clade Lipomyces.” Frontiers in Bioengineering and Biotechnology 12: 1356551.

[23]

Czajka, Czajka, T. Oyetunde, and Y. J. Tang. 2021. “Integrated Knowledge Mining, Genome-Scale Modeling, and Machine Learning for Predicting Yarrowia Lipolytica Bioproduction.” Metabolic Engineering 67: 227–236.

[24]

Dal’Molin, Dal’Molin, L. E. Quek, R. W. Palfreyman, and L. K. Nielsen. 2011. “Algagem—A Genome-Scale Metabolic Reconstruction of Algae Based on the Chlamydomonas Reinhardtii Genome.” BMC Genomics 12: S5.

[25]

Dinh, Dinh, P. F. Suthers, S. H. J. Chan, et al. 2019. “A Comprehensive Genome-Scale Model for Rhodosporidium Toruloides IFO0880 Accounting for Functional Genomics and Phenotypic Data.” Metabolic Engineering Communications 9: e00101.

[26]

Dobson, Dobson, K. Smallbone, and D. Jameson, et al. 2010. “Further Developments Towards a Genome-Scale Metabolic Model of Yeast.” BMC Systems Biology 4: 145.

[27]

Duarte, Duarte, M. J. Herrgard, and B. O. Palsson. 2004. “Reconstruction and Validation of Saccharomyces Cerevisiae iND750, a Fully Compartmentalized Genome-Scale Metabolic Model.” Genome Research 14, no. 7: 1298–1309.

[28]

Edwards, J. S., and B. O. Palsson. 2000. “The Escherichia coli MG1655 in Silico Metabolic Genotype: Its Definition, Characteristics, and Capabilities.” Proceedings of the National Academy of Sciences of the United States of America 97, no. 10: 5528–5533.

[29]

Espinosa-Gonzalez, Espinosa-Gonzalez, A. Parashar, and D. C. Bressler. 2014. “Heterotrophic Growth and Lipid Accumulation of Chlorella Protothecoides in Whey Permeate, a Dairy By-Product Stream, for Biofuel Production.” Bioresource Technology 155: 170–176.

[30]

Fabris, Fabris, M. Matthijs, S. Rombauts, W. Vyverman, A. Goossens, and G. J. E. Baart. 2012. “The Metabolic Blueprint of Phaeodactylum Tricornutum Reveals a Eukaryotic Entner-Doudoroff Glycolytic Pathway.” Plant Journal 70, no. 6: 1004–1014.

[31]

Feist, Feist, C. S. Henry, and J. L. Reed, et al. 2007. “A Genome-Scale Metabolic Reconstruction for Escherichia Coli K-12 MG1655 That Accounts for 1260 ORFs and Thermodynamic Information.” Molecular Systems Biology 3: 121.

[32]

Ferreira, Ferreira, P. G. Teixeira, M. Gossing, F. David, V. Siewers, and J. Nielsen. 2018. “Metabolic Engineering of Saccharomyces Cerevisiae for Overproduction of Triacylglycerols.” Metabolic Engineering Communications 6: 22–27.

[33]

Forster, Forster, I. Famili, P. Fu, B. O. Palsson, and J. Nielsen. 2003. “Genome-Scale Reconstruction of the Saccharomyces Cerevisiae Metabolic Network.” Genome Research 13, no. 2: 244–253.

[34]

Fu, P. C. 2009. “Genome-Scale Modeling of Synechocystis Sp Pcc 6803 and Prediction of Pathway Insertion.” Journal of Chemical Technology and Biotechnology 84, no. 4: 473–483.

[35]

Gajewski, Gajewski, R. Pavlovic, M. Fischer, E. Boles, and M. Grininger. 2017. “Engineering Fungal De Novo Fatty Acid Synthesis for Short Chain Fatty Acid Production.” Nature Communications 8: 14650.

[36]

Garay, Garay, I. R. Sitepu, T. Cajka, et al. 2016. “Eighteen New Oleaginous Yeast Species.” Journal of Industrial Microbiology & Biotechnology 43, no. 7: 887–900.

[37]

Goffeau, Goffeau, B. G. Barrell, H. Bussey, et al. 1996. “Life With 6000 Genes.” Science (New York, N.Y.) 274, no. 5287: 546–567.

[38]

González, González, M. R. Fernandez, D. Marco, et al. 2010. “Role of Saccharomyces Cerevisiae Oxidoreductases Bdh1p and Ara1p in the Metabolism of Acetoin and 2,3-Butanediol.” Applied and Environmental Microbiology 76, no. 3: 670–679.

[39]

Guarnieri, Guarnieri, A. Nag, S. H. Yang, and P. T. Pienkos. 2013. “Proteomic Analysis of Chlorella Vulgaris: Potential Targets for Enhanced Lipid Accumulation.” Journal of Proteomics 93: 245–253.

[40]

Guo, Guo, L. Q. Su, Q. Liu, Y. Zhu, Z. J. Dai, and Q. H. Wang. 2022. “Dissecting Carbon Metabolism of Yarrowia Lipolytica Type Strain W29 Using Genome-Scale Metabolic Modelling.” Computational and Structural Biotechnology Journal 20: 2503–2511.

[41]

Hamilton, J. J., and J. L. Reed. 2012. “Identification of Functional Differences in Metabolic Networks Using Comparative Genomics and Constraint-Based Models.” PLoS One 7, no. 4: e34670.

[42]

Heavner, Heavner, K. Smallbone, B. Barker, P. Mendes, and L. P. Walker. 2012. “Yeast 5-an Expanded Reconstruction of the Saccharomyces Cerevisiae Metabolic Network.” BMC Systems Biology 6: 55.

[43]

Heavner, Heavner, K. Smallbone, N. D. Price, and L. P. Walker. 2013. “Version 6 of the Consensus Yeast Metabolic Network Refines Biochemical Coverage and Improves Model Performance.” Database-The Journal of Biological Databases and Curation 2013: bat059.

[44]

Heirendt, Heirendt, S. Arreckx, T. Pfau, et al. 2019. “Creation and Analysis of Biochemical Constraint-Based Models Using the COBRA Toolbox v.3.0.” Nature Protocols 14, no. 3: 639–702.

[45]

Hendry, Hendry, C. B. Prasannan, A. Joshi, S. Dasgupta, and P. P. Wangikar. 2016. “Metabolic Model of Synechococcus sp PCC 7002: Prediction of Flux Distribution and Network Modification for Enhanced Biofuel Production.” Bioresource Technology 213: 190–197.

[46]

Hong, S. J., and C. G. Lee. 2007. “Evaluation of Central Metabolism Based on a Genomic Database of Synechocystis PCC6803.” Biotechnology and Bioprocess Engineering 12, no. 2: 165–173.

[47]

Imam, Imam, S. Schauble, J. Valenzuela, et al. 2015. “A Refined Genome-Scale Reconstruction of Chlamydomonas Metabolism Provides a Platform for Systems-Level Analyses.” Plant Journal 84, no. 6: 1239–1256.

[48]

Juneja, Juneja, F. W. R. Chaplen, and G. S. Murthy. 2016. “Genome Scale Metabolic Reconstruction of Chlorella Variabilis for Exploring Its Metabolic Potential for Biofuels.” Bioresource Technology 213: 103–110.

[49]

Kavscek, Kavscek, G. Bhutada, T. Madl, and K. Natter. 2015. “Optimization of Lipid Production With a Genome-Scale Model of Yarrowia Lipolytica.” BMC Systems Biology 9: 72.

[50]

Kerkhoven, Kerkhoven, K. R. Pomraning, S. E. Baker, and J. Nielsen. 2016. “Regulation of Amino-Acid Metabolism Controls Flux to Lipid Accumulation in Yarrowia lipolytica.” NPJ Systems Biology and Applications 2: 16005.

[51]

Kim, Kim, S. T. Coradetti, and Y. M. Kim, et al. 2021. “Multi-Omics Driven Metabolic Network Reconstruction and Analysis of Lignocellulosic Carbon Utilization in Rhodosporidium toruloides.” Frontiers in Bioengineering and Biotechnology 8: 612832.

[52]

Kim, Kim, M. Fabris, G. Baart, et al. 2016. “Flux Balance Analysis of Primary Metabolism in the Diatom Phaeodactylum Tricornutum.” Plant Journal 85, no. 1: 161–176.

[53]

Kliphuis, Kliphuis, A. J. Klok, D. E. Martens, P. P. Lamers, M. Janssen, and R. H. Wijffels. 2012. “Metabolic Modeling of Chlamydomonas Reinhardtii: Energy Requirements for Photoautotrophic Growth and Maintenance.” Journal of Applied Phycology 24, no. 2: 253–266.

[54]

Knies, Knies, P. Wittmuß, S. Appel, O. Sawodny, M. Ederer, and R. Feuer. 2015. “Modeling and Simulation of Optimal Resource Management during the Diurnal Cycle in Emiliania Huxleyi by Genome-Scale Reconstruction and an Extended Flux Balance Analysis Approach.” Metabolites 5, no. 4: 659–676.

[55]

Knoop, Knoop, M. Grundel, and Y. Zilliges, et al. 2013. “Flux Balance Analysis of Cyanobacterial Metabolism: The Metabolic Network of Synechocystis sp PCC 6803.” PLOS Computational Biology 9, no. 6: e1003081.

[56]

Knoop, Knoop, Y. Zilliges, W. Lockau, and R. Steuer. 2010. “The Metabolic Network of Synechocystis spSp. PCC 6803: Systemic Properties of Autotrophic Growth.” Plant Physiology 154, no. 1: 410–422.

[57]

Kroth, Kroth, A. Chiovitti, and A. Gruber, et al. 2008. “A Model for Carbohydrate Metabolism in the Diatom Phaeodactylum tricornutum Deduced From Comparative Whole Genome Analysis.” PLoS One 3, no. 1: e1426.

[58]

Krumholz, Krumholz, H. Yang, P. Weisenhorn, C. S. Henry, and I. G. L. Libourel. 2012. “Genome-Wide Metabolic Network Reconstruction of the Picoalga Ostreococcus.” Journal of Experimental Botany 63, no. 6: 2353–2362.

[59]

Kuepfer, Kuepfer, U. Sauer, and L. M. Blank. 2005. “Metabolic Functions of Duplicate Genes in Saccharomyces Cerevisiae.” Genome Research 15, no. 10: 1421–1430.

[60]

Levering, Levering, J. Broddrick, and C. L. Dupont, et al. 2016. “Genome-Scale Model Reveals Metabolic Basis of Biomass Partitioning in a Model Diatom.” PLoS One 11, no. 5: e0155038.

[61]

Levitan, Levitan, J. Dinamarca, G. Hochman, and P. G. Falkowski. 2014. “Diatoms: A Fossil Fuel of the Future.” Trends in Biotechnology 32, no. 3: 117–124.

[62]

Li, Li, W. Du, and D. H. Liu. 2008. “Perspectives of Microbial Oils for Biodiesel Production.” Applied Microbiology and Biotechnology 80, no. 5: 749–756.

[63]

Lim, Lim, H. Schuhmann, S. R. Thomas-Hall, et al. 2017. “RNA-Seq and Metabolic Flux Analysis of Tetraselmis sp. M8 During Nitrogen Starvation Reveals a Two-Stage Lipid Accumulation Mechanism.” Bioresource Technology 244, no. Pt 2: 1281–1293.

[64]

Liu, Liu, X. M. Zhang, C. Chen, Y. Yin, G. H. Zhao, and Y. G. Chen. 2023. “Physiological Responses of Methanosarcina barkeri Under Ammonia Stress at the Molecular Level: The Unignorable Lipid Reprogramming.” Environmental Science & Technology 57: 3917–3929.

[65]

Liu, Liu, C. Y. Chang, Q. Liao, X. Zhu, and J. S. Chang. 2013. “Photoheterotrophic Growth of Chlorella Vulgaris ESP6 on Organic Acids From Dark Hydrogen Fermentation Effluents.” Bioresource Technology 145: 331–336.

[66]

Liu, Liu, T. Li, W. Zhou, et al. 2020. “The Yeast Peroxisome: A Dynamic Storage Depot and Subcellular Factory for Squalene Overproduction.” Metabolic Engineering 57: 151–161.

[67]

Loira, Loira, T. Dulermo, J. M. Nicaud, and D. J. Sherman. 2012. “A Genome-Scale Metabolic Model of the Lipid-Accumulating Yeast Yarrowia Lipolytica.” BMC Systems Biology 6: 35.

[68]

Loira, Loira, S. Mendoza, and M. P. Cortes, et al. 2017. “Reconstruction of the Microalga Nannochloropsis Salina Genome-Scale Metabolic Model With Applications to Lipid Production.” BMC Systems Biology 11: 66.

[69]

Lopes, Lopes, N. Bonturi, E. J. Kerkhoven, E. A. Miranda, and P. J. Lahtvee. 2020. “C/N Ratio and Carbon Source-Dependent Lipid Production Profiling in Rhodotorula Toruloides.” Applied Microbiology and Biotechnology 104, no. 6: 2639–2649.

[70]

Lu, Lu, F. Li, B. J. Sánchez, et al. 2019. “A Consensus S. Cerevisiae Metabolic Model Yeast8 and Its Ecosystem for Comprehensively Probing Cellular Metabolism.” Nature Communications 10, no. 1: 3586.

[71]

Ma, Ma, B. Shi, Z. L. Ye, et al. 2019. “Lipid Engineering Combined With Systematic Metabolic Engineering of Saccharomyces Cerevisiae for High-Yield Production of Lycopene.” Metabolic Engineering 52: 134–142.

[72]

Maheswari, Maheswari, A. Montsant, J. Goll, et al. 2005. “The Diatom EST Database.” Nucleic Acids Research 33: D344–D347.

[73]

Manichaikul, Manichaikul, L. Ghamsari, E. F. Y. Hom, et al. 2009. “Metabolic Network Analysis Integrated With Transcript Verification for Sequenced Genomes.” Nature Methods 6, no. 8: 589–U53.

[74]

May, May, S. Wienkoop, S. Kempa, et al. 2008. “Metabolomics- and Proteomics-Assisted Genome Annotation and Analysis of the Draft Metabolic Network of Chlamydomonas Reinhardtii.” Genetics 179, no. 1: 157–166.

[75]

Mishra, Mishra, N. R. Lee, M. Lakshmanan, M. Kim, B. G. Kim, and D. Y. Lee. 2018. “Genome-Scale Model-Driven Strain Design for Dicarboxylic Acid Production in Yarrowia lipolytica.” BMC Systems Biology 12: 12.

[76]

Mishra, Mishra, G. Y. Park, M. Lakshmanan, et al. 2016. “Genome-Scale Metabolic Modeling and In Silico Analysis of Lipid Accumulating Yeast Candida tropicalis for Dicarboxylic Acid Production.” Biotechnology and Bioengineering 113, no. 9: 1993–2004.

[77]

Mo, Mo, B. O. Palsson, and M. J. Herrgard. 2009. “Connecting Extracellular Metabolomic Measurements to Intracellular Flux States in Yeast.” BMC Systems Biology 3: 37.

[78]

Monk, Monk, C. J. Lloyd, E. Brunk, et al. 2017. “iML1515, a knowledgebase That Computes Escherichia coli Traits.” Nature Biotechnology 35, no. 10: 904–908.

[79]

Montagud, Montagud, E. Navarro, P. F. de Cordoba, J. F. Urchueguia, and K. R. Patil. 2010. “Reconstruction and Analysis of Genome-Scale Metabolic Model of a Photosynthetic Bacterium.” BMC Systems Biology 4: 156.

[80]

Montagud, Montagud, A. Zelezniak, E. Navarro, P. de Cordoba, J. F. Urchueguia, and K. R. Patil. 2011. “Flux Coupling and Transcriptional Regulation Within the Metabolic Network of the Photosynthetic Bacterium Synechocystis sp PCC6803.” Biotechnology Journal 6, no. 3: 330–342.

[81]

Morifuji, Morifuji, S. Higashi, C. Oba, et al. 2015. “Milk Phospholipids Enhance Lymphatic Absorption of Dietary Sphingomyelin in Lymph-Cannulated Rats.” Lipids 50, no. 10: 987–996.

[82]

Mueller, Mueller, B. M. Berla, H. B. Pakrasi, and C. D. Maranas. 2013. “Rapid Construction of Metabolic Models for a Family of Cyanobacteria Using a Multiple Source Annotation Workflow.” BMC Systems Biology 7: 142.

[83]

Mund, Mund, Y. S. Liu, and S. L. Chen. 2022. “Advances in Metabolic Engineering of Cyanobacteria for Production of Biofuels.” Fuel 322: 124117.

[84]

Muthuraj, Muthuraj, B. Palabhanvi, S. Misra, V. Kumar, K. Sivalingavasu, and D. Das. 2013. “Flux Balance Analysis of Chlorella sp. FC2 IITG Under Photoautotrophic and Heterotrophic Growth Conditions.” Photosynthesis Research 118, no. 1–2: 167–179.

[85]

Navarro, Navarro, A. Montagud, P. F. de Cordoba, and J. F. Urchueguia. 2009. “Metabolic Flux Analysis of the Hydrogen Production Potential in Synechocystis sp PCC6803.” International Journal of Hydrogen Energy 34, no. 21: 8828–8838.

[86]

Nogales, Nogales, S. Gudmundsson, E. M. Knight, B. O. Palsson, and I. Thiele. 2012. “Detailing the Optimality of Photosynthesis in Cyanobacteria Through Systems Biology Analysis.” Proceedings of the National Academy of Sciences of the United States of America 109, no. 7: 2678–2683.

[87]

Nookaew, Nookaew, M. C. Jewett, and A. Meechai, et al. 2008. “The Genome-Scale Metabolic Model iIN800 of Saccharomyces Cerevisiae and Its Validation: A Scaffold to Query Lipid Metabolism.” BMC Systems Biology 2: 71.

[88]

Orth, Orth, T. M. Conrad, and J. Na, et al. 2011. “A Comprehensive Genome-Scale Reconstruction of Escherichia Coli Metabolism-2011.” Molecular Systems Biology 7: 535.

[89]

Österlund, Österlund, I. Nookaew, S. Bordel, and J. Nielsen. 2013. “Mapping Condition-Dependent Regulation of Metabolism in Yeast Through Genome-Scale Modeling.” BMC Systems Biology 7: 36.

[90]

Patnayak, S., and A. Sree. 2005. “Screening of Bacterial Associates of Marine Sponges for Single Cell Oil and Pufa.” Letters in Applied Microbiology 40, no. 5: 358–363.

[91]

Pengcheng, Pengcheng, H. Qiang, and S. S. J. P. O. Fong. 2012. “Reconstruction and In Silico Analysis of Metabolic Network for an Oleaginous Yeast.” Yarrowia Lipolytica 7, no. 12: e51535.

[92]

Pham, Pham, M. Reijnders, and M. Suarez-Diez, et al. 2021. “Genome-Scale Metabolic Modeling Underscores the Potential of Cutaneotrichosporon Oleaginosus ATCC 20509 as a Cell Factory for Biofuel Production.” Biotechnology for Biofuels 14, no. 1: 2.

[93]

Poontawee, Poontawee, W. Lorliam, P. Polburee, and S. Limtong. 2023. “Oleaginous Yeasts: Biodiversity and Cultivation.” Fungal Biology Reviews 44: 100295.

[94]

Prigent, Prigent, G. Collet, S. M. Dittami, et al. 2014. “The Genome-Scale Metabolic Network of Ectocarpus Siliculosus (EctoGEM): A Resource to Study Brown Algal Physiology and Beyond.” Plant Journal 80, no. 2: 367–381.

[95]

Qian, Qian, M. K. Kim, G. K. Kumaraswamy, A. Agarwal, D. S. Lun, and G. C. Dismukes. 2017. “Flux Balance Analysis of Photoautotrophic Metabolism: Uncovering New Biological Details of Subsystems Involved in Cyanobacterial Photosynthesis.” Biochimica Et Biophysica Acta-Bioenergetics 1858, no. 4: 276–287.

[96]

Quinn, Quinn, T. Yates, N. Douglas, et al. 2012. “Nannochloropsis production Metrics in a Scalable Outdoor Photobioreactor for Commercial Applications.” Bioresource Technology 117: 164–171.

[97]

Ranganathan, Ranganathan, T. W. Tee, A. Chowdhury, et al. 2012. “An Integrated Computational and Experimental Study for Overproducing Fatty Acids in Escherichia Coli.” Metabolic Engineering 14, no. 6: 687–704.

[98]

Ratledge, C., and J. P. Wynn. 2002. “The Biochemistry and Molecular Biology of Lipid Accumulation in Oleaginous Microorganisms.” Advances in Applied Microbiology 51: 1–51.

[99]

Ray, Ray, P. Kundu, and A. Ghosh. 2023. “Reconstruction of a Genome-Scale Metabolic Model of Scenedesmus obliquus and Its Application for Lipid Production Under Three Trophic Modes.” ACS Synthetic Biology 12, no. 11: 3463–3481.

[100]

Reed, Reed, T. D. Vo, C. H. Schilling, and B. O. Palsson. 2003. “An Expanded Genome-Scale Model of Escherichia coli K-12 (iJR904 GSM/GPR).” Genome Biology 4, no. 9: R54.

[101]

Rugen, Rugen, A. Bockmayr, J. Legrand, and G. Cogne. 2012. “Network Reduction in Metabolic Pathway Analysis: Elucidation of the Key Pathways Involved in the Photoautotrophic Growth of the Green Alga Chlamydomonas Reinhardtii.” Metabolic Engineering 14, no. 4: 458–467.

[102]

Saha, Saha, A. T. Verseput, B. M. Berla, T. J. Mueller, H. B. Pakrasi, and C. D. Maranas. 2012. “Reconstruction and Comparison of the Metabolic Potential of Cyanobacteria Cyanothece sp ATCC 51142 and Synechocystis sp PCC 6803.” PLoS One 7, no. 10: e48285.

[103]

Seaver, Seaver, F. Liu, and Q. Z. Zhang, et al. 2021. “The ModelSEED Biochemistry Database for the Integration of Metabolic Annotations and the Reconstruction, Comparison and Analysis of Metabolic Models for Plants, Fungi and Microbes.” Nucleic Acids Research 49, no. D1: D1555.

[104]

Semkiv, Semkiv, K. V. Dmytruk, C. A. Abbas, and A. A. Sibirny. 2017. “Metabolic Engineering for High Glycerol Production by the Anaerobic Cultures of Saccharomyces cerevisiae.” Applied Microbiology and Biotechnology 101, no. 11: 4403–4416.

[105]

Shah, Shah, A. Ahmad, S. Srivastava, and B. M. J. Ali. 2017. “Reconstruction and Analysis of a Genome-Scale Metabolic Model of Nannochloropsis gaditana.” Algal Research-Biomass Biofuels and Bioproducts 26: 354–364.

[106]

Shastri, A. A., and J. A. Morgan. 2005. “Flux Balance Analysis of Photoautotrophic Metabolism.” Biotechnology Progress 21, no. 6: 1617–1626.

[107]

Simensen, Simensen, A. Voigt, and E. Almaas. 2021. “High-Quality Genome-Scale Metabolic Model of Aurantiochytrium sp. T66.” Biotechnology and Bioengineering 118, no. 5: 2105–2117.

[108]

Sundararaghavan, Sundararaghavan, A. Mukherjee, S. Sahoo, and G. K. Suraishkumar. 2020. “Mechanism of the Oxidative Stress-Mediated Increase in Lipid Accumulation by the Bacterium, R. Opacus PD630: Experimental Analysis and Genome-Scale Metabolic Modeling.” Biotechnology and Bioengineering 117, no. 6: 1779–1788.

[109]

Tiukova, Tiukova, S. Prigent, J. Nielsen, M. Sandgren, and E. J. Kerkhoven. 2019. “Genome-Scale Model of Rhodotorula Toruloides Metabolism.” Biotechnology and Bioengineering 116, no. 12: 3396–3408.

[110]

Tsai, Tsai, T. Ohashi, C. C. Wu, et al. 2019. “Delta-9 Fatty Acid Desaturase Overexpression Enhanced Lipid Production and Oleic Acid Content in Rhodosporidium Toruloides for Preferable Yeast Lipid Production.” Journal of Bioscience and Bioengineering 127, no. 4: 430–440.

[111]

Ventorim, Ventorim, M. A. D. Ferreira, E. L. M. de Almeida, E. J. Kerkhoven, and W. B. da Silveira. 2022. “Genome-Scale Metabolic Model of Oleaginous Yeast Papiliotrema laurentii.” Biochemical Engineering Journal 180: 108353.

[112]

Vongsangnak, Vongsangnak, A. Kingkaw, J. H. Yang, Y. D. Song, and K. Laoteng. 2018. “Dissecting Metabolic Behavior of Lipid Over-Producing Strain of Mucor Circinelloides Through Genome-Scale Metabolic Network and Multi-Level Data Integration.” Gene 670: 87–97.

[113]

Vongsangnak, Vongsangnak, A. Klanchui, I. Tawornsamretkit, W. Tatiyaborwornchai, K. Laoteng, and A. Meechai. 2016. “Genome-Scale Metabolic Modeling of Mucor Circinelloides and Comparative Analysis With Other Oleaginous Species.” Gene 583, no. 2: 121–129.

[114]

Vongsangnak, Vongsangnak, R. Ruenwai, X. Tang, et al. 2013. “Genome-Scale Analysis of the Metabolic Networks of Oleaginous Zygomycete Fungi.” Gene 521, no. 1: 180–190.

[115]

Vu, Vu, E. A. Hill, L. A. Kucek, A. E. Konopka, A. S. Beliaev, and J. L. Reed. 2013. “Computational Evaluation of Synechococcus sp PCC 7002 Metabolism for Chemical Production.” Biotechnology Journal 8, no. 5: 619–630.

[116]

Vu, Vu, S. M. Stolyar, and G. E. Pinchuk, et al. 2012. “Genome-Scale Modeling of Light-Driven Reductant Partitioning and Carbon Fluxes in Diazotrophic Unicellular Cyanobacterium Cyanothece sp ATCC 51142.” PLOS Computational Biology 8, no. 4: e1002460.

[117]

Wang, Wang, Y. Q. Li, N. Wu, and C. Q. Lan. 2008. “CO2 Bio-Mitigation Using Microalgae.” Applied Microbiology and Biotechnology 79, no. 5: 707–718.

[118]

Wang, Wang, S. Marcisauskas, B. J. Sanchez, et al. 2018. “RAVEN 2.0: A Versatile Toolbox for Metabolic Network Reconstruction and a Case Study on Streptomyces Coelicolor.” PLoS Computational Biology 14, no. 10: e1006541.

[119]

Weaver, Weaver, I. M. Keseler, A. Mackie, I. T. Paulsen, and P. D. Karp. 2014. “A Genome-Scale Metabolic Flux Model of Escherichia coli K-12 Derived From the EcoCyc Database.” BMC Systems Biology 8: 79.

[120]

Wei, Wei, X. Jian, J. Chen, C. Zhang, and Q. Hua. 2017. “Reconstruction of Genome-Scale Metabolic Model of Yarrowia Lipolytica and Its Application in Overproduction of Triacylglycerol.” Bioresources and Bioprocessing 4, no. 1: 51.

[121]

Wu, Wu, W. Xiong, J. Dai, and Q. Wu. 2015. “Genome-Based Metabolic Mapping and 13C Flux Analysis Reveal Systematic Properties of an Oleaginous Microalga Chlorella Protothecoides.” Plant Physiology 167, no. 2: 586–599.

[122]

Xiong, Xiong, L. Liu, C. Wu, C. Yang, and Q. Wu. 2010. “13C-Tracer and Gas Chromatography-Mass Spectrometry Analyses Reveal Metabolic Flux Distribution in the Oleaginous Microalga Chlorella protothecoides.” Plant Physiology 154, no. 2: 1001–1011.

[123]

Xue, Xue, Z. Chi, Y. Zhang, et al. 2018. “Fatty Acids From Oleaginous Yeasts and Yeast-Like Fungi and Their Potential Applications.” Critical Reviews in Biotechnology 38, no. 7: 1049–1060.

[124]

Yang, Yang, Q. Hua, and K. Shimizu. 2000. “Energetics and Carbon Metabolism During Growth of Microalgal Cells Under Photoautotrophic, Mixotrophic and Cyclic Light-Autotrophic/Dark-Heterotrophic Conditions.” Biochemical Engineering Journal 6, no. 2: 87–102.

[125]

Yang, Yang, Q. Hua, and K. Shimizu. 2002. “Metabolic Flux Analysis in Synechocystis Using Isotope Distribution From C-13-Labeled Glucose.” Metabolic Engineering 4, no. 3: 202–216.

[126]

Yang, Yang, M. Yan, and B. Hu. 2014. “Endophytic Fungal Strains of Soybean for Lipid Production.” Bioenergy Research 7, no. 1: 353–361.

[127]

Ye, Ye, N. Xu, H. Chen, Y. Q. Chen, W. Chen, and L. Liu. 2015a. “Reconstruction and Analysis of a Genome-Scale Metabolic Model of the Oleaginous Fungus Mortierella Alpina.” BMC Systems Biology 9, no. 1: 1.

[128]

Ye, Ye, W. Qiao, X. Yu, et al. 2015b. “Reconstruction and Analysis of the Genome-Scale Metabolic Model of Schizochytrium limacinum SR21 for Docosahexaenoic Acid Production.” BMC Genomics 16, no. 1: 799.

[129]

Yoshikawa, Yoshikawa, Y. Kojima, T. Nakajima, C. Furusawa, T. Hirasawa, and H. Shimizu. 2011. “Reconstruction and Verification of a Genome-Scale Metabolic Model for Synechocystis sp PCC6803.” Applied Microbiology and Biotechnology 92, no. 2: 347–358.

[130]

Zhou, Zhou, Y. A. Wang, and J. L. Zhang, et al. 2021. “A Metabolic Model of Lipomyces starkeyi for Predicting Lipogenesis Potential From Diverse Low-Cost Substrates.” Biotechnology for Biofuels 14, no. 1: 148.

[131]

Zomorrodi, A. R., Computational Tools for Genome-Scale Synthetic Lethality Analysis and Metabolic Modeling of Microbial Communities, 2012.

[132]

Zomorrodi, A. R., and C. D. Maranas. 2010. “Improving the iMM904 S. cerevisiae Metabolic Model Using Essentiality and Synthetic Lethality Data.” BMC Systems Biology 4: 178.

[133]

Zuniga, Zuniga, J. Levering, M. R. Antoniewicz, M. T. Guarnieri, M. J. Betenbaugh, and K. Zengler. 2018. “Predicting Dynamic Metabolic Demands in the Photosynthetic Eukaryote Chlorella vulgaris.” Plant Physiology 176, no. 1: 450–462.

[134]

Zuniga, Zuniga, C. T. Li, T. Huelsman, et al. 2016. “Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes Under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions.” Plant Physiology 172, no. 1: 589–602.

RIGHTS & PERMISSIONS

2024 The Author(s). Food Bioengineering published by John Wiley & Sons Australia, Ltd. on behalf of State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology.

AI Summary AI Mindmap
PDF

271

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/