Dual gradients of light intensity and nutrient concentration for full-factorial mapping of photosynthetic productivity

• Published in: Lab on a chip Vol. 16; no. 15; pp. 2785 - 2790
• Main Authors: Nguyen, Brian, Graham, Percival J, Sinton, David
• Format: Journal Article
• Language: English
• Published: England 01.01.2016
• Subjects: Availability; Chlamydomonas reinhardtii; Hydrogels; Inhibition; Luminous intensity; Nutrients; Photosynthesis; Productivity
• ISSN: 1473-0197; 1473-0189
• DOI: 10.1039/c6lc00619a

Optimizing bioproduct generation from microalgae is complicated by the myriad of coupled parameters affecting photosynthetic productivity. Quantifying the effect of multiple coupled parameters in full-factorial fashion requires a prohibitively high number of experiments. We present a simple hydrogel-based platform for the rapid, full-factorial mapping of light and nutrient availability on the growth and lipid accumulation of microalgae. We accomplish this without microfabrication using thin sheets of cell-laden hydrogels. By immobilizing the algae in a hydrogel matrix we are able to take full advantage of the continuous spatial chemical gradient produced by a diffusion-based gradient generator while eliminating the need for chambers. We map the effect of light intensities between 0 μmol m(-2) s(-1) and 130 μmol m(-2) s(-1) (∼28 W m(-2)) coupled with ammonium concentrations between 0 mM and 7 mM on Chlamydomonas reinhardtii. Our data set, verified with bulk experiments, clarifies the role of ammonium availability on the photosynthetic productivity Chlamydomonas reinhardtii, demonstrating the dependence of ammonium inhibition on light intensity. Specifically, a sharp optimal growth peak emerges at approximately 2 mM only for light intensities between 80 and 100 μmol m(-2) s(-1)- suggesting that ammonium inhibition is insignificant at lower light intensities. We speculate that this phenomenon is due to the regulation of the high affinity ammonium transport system in Chlamydomonas reinhardtii as well as free ammonia toxicity. The complexity of this photosynthetic biological response highlights the importance of full-factorial data sets as enabled here.