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Dynamic flux balance modeling to increase the production of high-value compounds in green microalgae

Overview of attention for article published in Biotechnology for Biofuels and Bioproducts, August 2016
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Title
Dynamic flux balance modeling to increase the production of high-value compounds in green microalgae
Published in
Biotechnology for Biofuels and Bioproducts, August 2016
DOI 10.1186/s13068-016-0556-4
Pubmed ID
Authors

Robert J. Flassig, Melanie Fachet, Kai Höffner, Paul I. Barton, Kai Sundmacher

Abstract

Photosynthetic organisms can be used for renewable and sustainable production of fuels and high-value compounds from natural resources. Costs for design and operation of large-scale algae cultivation systems can be reduced if data from laboratory scale cultivations are combined with detailed mathematical models to evaluate and optimize the process. In this work we present a flexible modeling formulation for accumulation of high-value storage molecules in microalgae that provides quantitative predictions under various light and nutrient conditions. The modeling approach is based on dynamic flux balance analysis (DFBA) and includes regulatory models to predict the accumulation of pigment molecules. The accuracy of the model predictions is validated through independent experimental data followed by a subsequent model-based fed-batch optimization. In our experimentally validated fed-batch optimization study we increase biomass and [Formula: see text]-carotene density by factors of about 2.5 and 2.1, respectively. The analysis shows that a model-based approach can be used to develop and significantly improve biotechnological processes for biofuels and pigments.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 <1%
Germany 1 <1%
Unknown 102 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 21%
Student > Master 15 14%
Researcher 14 13%
Professor 8 8%
Student > Bachelor 7 7%
Other 16 15%
Unknown 22 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 18 17%
Agricultural and Biological Sciences 15 14%
Engineering 15 14%
Chemical Engineering 13 13%
Environmental Science 4 4%
Other 12 12%
Unknown 27 26%