Optimization of an Haematococcus pluvialis medium by a Genetic Algorithm-based strategy

The successful use of living organisms for the production of biomass or metabolites requires a careful control and optimization over growing conditions. However the range of interacting parameters makes full optimization difficult and time-consuming. In this context, Genetic Algorithm-based (GA) methods emerge as promising strategies for optimization of biotechnological processes. However, the potential of GA in the microalgae field remains, today, poorly explored. As a proof-of-concept, we evaluated how GA could be applied for the optimization of a medium for high yield photoautotrophic growth of Haematoccocus pluvialis.