Title |
Reconstruction of the microalga Nannochloropsis salina genome-scale metabolic model with applications to lipid production
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Published in |
BMC Systems Biology, July 2017
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DOI | 10.1186/s12918-017-0441-1 |
Pubmed ID | |
Authors |
Nicolás Loira, Sebastian Mendoza, María Paz Cortés, Natalia Rojas, Dante Travisany, Alex Di Genova, Natalia Gajardo, Nicole Ehrenfeld, Alejandro Maass |
Abstract |
Nannochloropsis salina (= Eustigmatophyceae) is a marine microalga which has become a biotechnological target because of its high capacity to produce polyunsaturated fatty acids and triacylglycerols. It has been used as a source of biofuel, pigments and food supplements, like Omega 3. Only some Nannochloropsis species have been sequenced, but none of them benefit from a genome-scale metabolic model (GSMM), able to predict its metabolic capabilities. We present iNS934, the first GSMM for N. salina, including 2345 reactions, 934 genes and an exhaustive description of lipid and nitrogen metabolism. iNS934 has a 90% of accuracy when making simple growth/no-growth predictions and has a 15% error rate in predicting growth rates in different experimental conditions. Moreover, iNS934 allowed us to propose 82 different knockout strategies for strain optimization of triacylglycerols. iNS934 provides a powerful tool for metabolic improvement, allowing predictions and simulations of N. salina metabolism under different media and genetic conditions. It also provides a systemic view of N. salina metabolism, potentially guiding research and providing context to -omics data. |
X Demographics
Geographical breakdown
Country | Count | As % |
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France | 1 | 50% |
Chile | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 50% |
Members of the public | 1 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 114 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 18 | 16% |
Student > Master | 16 | 14% |
Researcher | 15 | 13% |
Student > Bachelor | 12 | 11% |
Student > Doctoral Student | 7 | 6% |
Other | 16 | 14% |
Unknown | 30 | 26% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 29 | 25% |
Agricultural and Biological Sciences | 18 | 16% |
Engineering | 11 | 10% |
Immunology and Microbiology | 3 | 3% |
Chemical Engineering | 3 | 3% |
Other | 12 | 11% |
Unknown | 38 | 33% |