Advances in metabolic modeling of oleaginous microalgae

Tibocha-Bonilla, Juan D.; Zuñiga, Cristal; Godoy-Silva, Rubén D.; Zengler, Karsten

doi:10.1186/s13068-018-1244-3

Title: Advances in metabolic modeling of oleaginous microalgae

Journal Article · 05 September 2018 · Biotechnology for Biofuels

DOI: https://doi.org/10.1186/s13068-018-1244-3 · OSTI ID:1485595

Tibocha-Bonilla, Juan D. ^[1]; Zuñiga, Cristal ^[2]; Godoy-Silva, Rubén D. ^[3]; Zengler, Karsten ^[2]

Univ. Nacional de Colombia (Colombia); University of California, San Diego
Univ. of California, San Diego, La Jolla, CA (United States)
Univ. Nacional de Colombia (Colombia)

Production of biofuels and bioenergy precursors by phototrophic microorganisms, such as microalgae and cyanobacteria, is a promising alternative to conventional fuels obtained from non-renewable resources. Several species of microalgae have been investigated as potential candidates for the production of biofuels, for the most part due to their exceptional metabolic capability to accumulate large quantities of lipids. Constraint-based modeling, a systems biology approach that accurately predicts the metabolic phenotype of phototrophs, has been deployed to identify suitable culture conditions as well as to explore genetic enhancement strategies for bioproduction. Core metabolic models were employed to gain insight into the central carbon metabolism in photosynthetic microorganisms. More recently, comprehensive genome-scale models, including organelle-specifc information at high resolution, have been developed to gain new insight into the metabolism of phototrophic cell factories. As a result, we review the current state of the art of constraint-based modeling and computational method development and discuss how advanced models led to increased prediction accuracy and thus improved lipid production in microalgae.

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Research Organization:: Johns Hopkins Univ., Baltimore, MD (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: SC0012658

OSTI ID:: 1485595

Journal Information:: Biotechnology for Biofuels, Journal Name: Biotechnology for Biofuels Journal Issue: 1 Vol. 11; ISSN 1754-6834

Publisher:: BioMed CentralCopyright Statement

Country of Publication:: United States

Language:: English

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RNA-Seq and metabolic flux analysis of Tetraselmis sp. M8 during nitrogen starvation reveals a two-stage lipid accumulation mechanism Lim, David K. Y.; Schuhmann, Holger; Thomas-Hall, Skye R. Bioresource Technology, Vol. 244 https://doi.org/10.1016/j.biortech.2017.06.003	journal	November 2017
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Biodiesel from microalgae beats bioethanol Chisti, Yusuf Trends in Biotechnology, Vol. 26, Issue 3 https://doi.org/10.1016/j.tibtech.2007.12.002	journal	March 2008
Network reduction in metabolic pathway analysis: Elucidation of the key pathways involved in the photoautotrophic growth of the green alga Chlamydomonas reinhardtii Rügen, Marco; Bockmayr, Alexander; Legrand, Jack Metabolic Engineering, Vol. 14, Issue 4 https://doi.org/10.1016/j.ymben.2012.01.009	journal	July 2012
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Placing microalgae on the biofuels priority list: a review of the technological challenges Greenwell, H. C.; Laurens, L. M. L.; Shields, R. J. Journal of The Royal Society Interface, Vol. 7, Issue 46 https://doi.org/10.1098/rsif.2009.0322	journal	November 2009
The Metabolic Network of Synechocystis sp. PCC 6803: Systemic Properties of Autotrophic Growth Knoop, Henning; Zilliges, Yvonne; Lockau, Wolfgang Plant Physiology, Vol. 154, Issue 1 https://doi.org/10.1104/pp.110.157198	journal	July 2010
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Genome-Based Metabolic Mapping and ¹³ C Flux Analysis Reveal Systematic Properties of an Oleaginous Microalga Chlorella protothecoides Wu, Chao; Xiong, Wei; Dai, Junbiao Plant Physiology, Vol. 167, Issue 2 https://doi.org/10.1104/pp.114.250688	journal	December 2014
Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions Zuñiga, Cristal; Li, Chien-Ting; Huelsman, Tyler Plant Physiology, Vol. 172, Issue 1 https://doi.org/10.1104/pp.16.00593	journal	July 2016
Predicting Dynamic Metabolic Demands in the Photosynthetic Eukaryote Chlorella vulgaris Zuñiga, Cristal; Levering, Jennifer; Antoniewicz, Maciek R. Plant Physiology, Vol. 176, Issue 1 https://doi.org/10.1104/pp.17.00605	journal	September 2017
The Biotransformation, Biodegradation, and Bioremediation of Organic Compounds by Microalgae1: Biotransformation of Organic Compounds by Microalgae Ghasemi, Younes; Rasoul-Amini, Sara; Fotooh-Abadi, Elham Journal of Phycology, Vol. 47, Issue 5 https://doi.org/10.1111/j.1529-8817.2011.01051.x	journal	September 2011
A refined genome-scale reconstruction of Chlamydomonas metabolism provides a platform for systems-level analyses Imam, Saheed; Schäuble, Sascha; Valenzuela, Jacob The Plant Journal, Vol. 84, Issue 6 https://doi.org/10.1111/tpj.13059	journal	November 2015
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Integrated Regulatory and Metabolic Networks of the Marine Diatom Phaeodactylum tricornutum Predict the Response to Rising CO ₂ Levels Levering, Jennifer; Dupont, Christopher L.; Allen, Andrew E. mSystems, Vol. 2, Issue 1 https://doi.org/10.1128/mSystems.00142-16	journal	February 2017
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Clostridium butyricum maximizes growth while minimizing enzyme usage and ATP production: metabolic flux distribution of a strain cultured in glycerol Serrano-Bermúdez, Luis Miguel; González Barrios, Andrés Fernando; Maranas, Costas D. BMC Systems Biology, Vol. 11, Issue 1 https://doi.org/10.1186/s12918-017-0434-0	journal	June 2017
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