A universal dynamical metabolic model representing mixotrophic growth of Chlorella sp.

An emerging idea is to couple wastewater treatment and biofuel production using microalgae to achieve higher productivities and lower costs. This paper proposes a metabolic modelling of Chlorella sp. growing on wastes in mixotrophic conditions, accounting also for the possible inhibitory substrates. A metabolic model considering several possible carbon substrates was developed and run. The addition of several organic carbon substrates such as acetate, butyrate or glucose were tested, along with glycerol, a more realistic substrate from an economical point of view. The metabolic model was built using DRUM framework and consists of 188 reactions and 176 metabolites. After a calibration phase, the model was successfully challenged with data from 122 experiments collected from scientific literature in autotrophic, heterotrophic and mixotrophic conditions. The optimal feeding strategy estimated with the model reduces the time to consume the volatile fatty acids from 16 days to 2 days. The high prediction capability of this model opens new routes for enhancing design and operation in waste valorisation using microalgae. Author SummaryWaste valorisation is one of the current envisaged strategies to make renewable processes more economically advantageous. For example, wastewater treatment can be used to produce biohydrogen from bacteria, through a process called dark fermentation, and to cultivate microalgae for biofuel production. Dark fermentation has, as by-products, organic acids that have inhibitory effects on the growth of microalgae, increasing the time to completely treat the waste. Advances in metabolic knowledge and techniques allow for the deployment of new strategies to improve the efficiency of bioprocesses. In this work, we validate a mathematical model of the metabolism of the microalgae genus Chlorella using the DRUM framework for 122 experiments from the scientific literature. This model enables us to apply control and optimisation techniques to provide a strategy to treat wastes coming from dark fermentation processes, overcoming the inhibition of some organic acids. The strategy is able to reduce the time to treat the waste from 16 days to only 2 days. The high prediction capability of this model opens new routes for enhancing design and operation in waste valorisation using microalgae.