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Metabolic modeling of Chlamydomonas reinhardtii: energy requirements for photoautotrophic growth and maintenance
In this study, a metabolic network describing the primary metabolism of Chlamydomonas reinhardtii was constructed. By performing chemostat experiments at different growth rates, energy parameters for maintenance and biomass formation were determined. The chemostats were run at low irradiances result...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3289792/ https://www.ncbi.nlm.nih.gov/pubmed/22427720 http://dx.doi.org/10.1007/s10811-011-9674-3 |
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author | Kliphuis, Anna M. J. Klok, Anne J. Martens, Dirk E. Lamers, Packo P. Janssen, Marcel Wijffels, René H. |
author_facet | Kliphuis, Anna M. J. Klok, Anne J. Martens, Dirk E. Lamers, Packo P. Janssen, Marcel Wijffels, René H. |
author_sort | Kliphuis, Anna M. J. |
collection | PubMed |
description | In this study, a metabolic network describing the primary metabolism of Chlamydomonas reinhardtii was constructed. By performing chemostat experiments at different growth rates, energy parameters for maintenance and biomass formation were determined. The chemostats were run at low irradiances resulting in a high biomass yield on light of 1.25 g mol(−1). The ATP requirement for biomass formation from biopolymers (K (x)) was determined to be 109 mmol g(−1) (18.9 mol mol(−1)) and the maintenance requirement (m (ATP)) was determined to be 2.85 mmol g(−1) h(−1). With these energy requirements included in the metabolic network, the network accurately describes the primary metabolism of C. reinhardtii and can be used for modeling of C. reinhardtii growth and metabolism. Simulations confirmed that cultivating microalgae at low growth rates is unfavorable because of the high maintenance requirements which result in low biomass yields. At high light supply rates, biomass yields will decrease due to light saturation effects. Thus, to optimize biomass yield on light energy in photobioreactors, an optimum between low and high light supply rates should be found. These simulations show that metabolic flux analysis can be used as a tool to gain insight into the metabolism of algae and ultimately can be used for the maximization of algal biomass and product yield. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10811-011-9674-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-3289792 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-32897922012-03-16 Metabolic modeling of Chlamydomonas reinhardtii: energy requirements for photoautotrophic growth and maintenance Kliphuis, Anna M. J. Klok, Anne J. Martens, Dirk E. Lamers, Packo P. Janssen, Marcel Wijffels, René H. J Appl Phycol Article In this study, a metabolic network describing the primary metabolism of Chlamydomonas reinhardtii was constructed. By performing chemostat experiments at different growth rates, energy parameters for maintenance and biomass formation were determined. The chemostats were run at low irradiances resulting in a high biomass yield on light of 1.25 g mol(−1). The ATP requirement for biomass formation from biopolymers (K (x)) was determined to be 109 mmol g(−1) (18.9 mol mol(−1)) and the maintenance requirement (m (ATP)) was determined to be 2.85 mmol g(−1) h(−1). With these energy requirements included in the metabolic network, the network accurately describes the primary metabolism of C. reinhardtii and can be used for modeling of C. reinhardtii growth and metabolism. Simulations confirmed that cultivating microalgae at low growth rates is unfavorable because of the high maintenance requirements which result in low biomass yields. At high light supply rates, biomass yields will decrease due to light saturation effects. Thus, to optimize biomass yield on light energy in photobioreactors, an optimum between low and high light supply rates should be found. These simulations show that metabolic flux analysis can be used as a tool to gain insight into the metabolism of algae and ultimately can be used for the maximization of algal biomass and product yield. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10811-011-9674-3) contains supplementary material, which is available to authorized users. Springer Netherlands 2011-04-15 2012 /pmc/articles/PMC3289792/ /pubmed/22427720 http://dx.doi.org/10.1007/s10811-011-9674-3 Text en © The Author(s) 2011 https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. |
spellingShingle | Article Kliphuis, Anna M. J. Klok, Anne J. Martens, Dirk E. Lamers, Packo P. Janssen, Marcel Wijffels, René H. Metabolic modeling of Chlamydomonas reinhardtii: energy requirements for photoautotrophic growth and maintenance |
title | Metabolic modeling of Chlamydomonas reinhardtii: energy requirements for photoautotrophic growth and maintenance |
title_full | Metabolic modeling of Chlamydomonas reinhardtii: energy requirements for photoautotrophic growth and maintenance |
title_fullStr | Metabolic modeling of Chlamydomonas reinhardtii: energy requirements for photoautotrophic growth and maintenance |
title_full_unstemmed | Metabolic modeling of Chlamydomonas reinhardtii: energy requirements for photoautotrophic growth and maintenance |
title_short | Metabolic modeling of Chlamydomonas reinhardtii: energy requirements for photoautotrophic growth and maintenance |
title_sort | metabolic modeling of chlamydomonas reinhardtii: energy requirements for photoautotrophic growth and maintenance |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3289792/ https://www.ncbi.nlm.nih.gov/pubmed/22427720 http://dx.doi.org/10.1007/s10811-011-9674-3 |
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