Automated nutrient screening system enables high-throughput optimisation of microalgae production conditions

Background: Microalgae provide an excellent platform for the production of high-value-products and are increasingly being recognised as a promising production system for biomass, animal feeds and renewable fuels. Results: Here, we describe an automated screen, to enable high-throughput optimisation...

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Bibliographic Details
Main Authors: Hankamer, B. (Author), Jakob, G. (Author), Radzun, K.A (Author), Ross, I. (Author), Stephens, E. (Author), Wolf, J. (Author), Zhang, E. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd. 2015
Subjects:
Online Access:View Fulltext in Publisher
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LEADER 02976nam a2200589Ia 4500
001 10.1186-s13068-015-0238-7
008 220112s2015 CNT 000 0 und d
020 |a 17546834 (ISSN) 
245 1 0 |a Automated nutrient screening system enables high-throughput optimisation of microalgae production conditions 
260 0 |b BioMed Central Ltd.  |c 2015 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1186/s13068-015-0238-7 
856 |z View in Scopus  |u https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929335045&doi=10.1186%2fs13068-015-0238-7&partnerID=40&md5=ff292b1ad0542af6a62e713be9ae4ed6 
520 3 |a Background: Microalgae provide an excellent platform for the production of high-value-products and are increasingly being recognised as a promising production system for biomass, animal feeds and renewable fuels. Results: Here, we describe an automated screen, to enable high-throughput optimisation of 12 nutrients for microalgae production. Its miniaturised 1,728 multiwell format allows multiple microalgae strains to be simultaneously screened using a two-step process. Step 1 optimises the primary elements nitrogen and phosphorous. Step 2 uses Box-Behnken analysis to define the highest growth rates within the large multidimensional space tested (Ca, Mg, Fe, Mn, Zn, Cu, B, Se, V, Si) at three levels (-1, 0, 1). The highest specific growth rates and maximum OD750 values provide a measure for continuous and batch culture. Conclusion: The screen identified the main nutrient effects on growth, pairwise nutrient interactions (for example, Ca-Mg) and the best production conditions of the sampled statistical space providing the basis for a targeted full factorial screen to assist with optimisation of algae production. © 2015 Radzun et al.; licensee BioMed Central. 
650 0 4 |a algae 
650 0 4 |a Algae 
650 0 4 |a Animalia 
650 0 4 |a Batch cell culture 
650 0 4 |a biological production 
650 0 4 |a biomass 
650 0 4 |a Biomass 
650 0 4 |a Calcium 
650 0 4 |a Ecology 
650 0 4 |a fuel 
650 0 4 |a Growth 
650 0 4 |a Growth (materials) 
650 0 4 |a growth response 
650 0 4 |a High throughput 
650 0 4 |a High-throughput 
650 0 4 |a Manganese 
650 0 4 |a microalga 
650 0 4 |a Microorganisms 
650 0 4 |a Multi-dimensional space 
650 0 4 |a Nutrient 
650 0 4 |a nutrient dynamics 
650 0 4 |a Nutrient interactions 
650 0 4 |a Nutrients 
650 0 4 |a Production system 
650 0 4 |a Renewable fuels 
650 0 4 |a renewable resource 
650 0 4 |a Screening 
650 0 4 |a Screening system 
650 0 4 |a Specific growth rate 
650 0 4 |a Throughput 
650 0 4 |a Two-step process 
700 1 0 |a Hankamer, B.  |e author 
700 1 0 |a Jakob, G.  |e author 
700 1 0 |a Radzun, K.A.  |e author 
700 1 0 |a Ross, I.  |e author 
700 1 0 |a Stephens, E.  |e author 
700 1 0 |a Wolf, J.  |e author 
700 1 0 |a Zhang, E.  |e author 
773 |t Biotechnology for Biofuels