Metabolic modelling of microalgae for wastewater treatment

Abstract

Wastewater treatment using microalgae presents a promising approach for sustainable and efficient removal of pollutants. However, the complexity of metabolic networks involved in microalgae metabolism poses challenges for computational analysis. This chapter explores network reduction methods, specifically focusing on the application of the DRUM (dynamic reduction of unbalanced metabolism) framework, to streamline the modelling of microalgae-based wastewater treatment systems. This chapter describes the general core metabolism of microalgae, reviews methods of metabolic network reduction, and ends it with the application of a case study. The DRUM framework divides the complete metabolic network into subnetworks where the quasi-steady-state assumption (QSSA) holds, reducing the number of state variables and simplifying the kinetic modelling. By calculating the elementary flux modes (EFMs) for each subnetwork, macroscopic reactions are derived, representing the collective behaviour of internal reactions within the subnetworks. To demonstrate the effectiveness of the DRUM framework, a case study based on Chlorella sp. microalgae is presented. The study focuses on treating volatile fatty acid waste, a common byproduct of dark fermentation. The reduced metabolic model, obtained using the DRUM framework, accurately captures the dynamics of microalgae growth and medium concentration. This chapter underscores the significance of network reduction methods in optimizing microalgae-based wastewater treatment systems. These reduction methods pave the way for further advancements in the development and optimization of microalgae-based wastewater treatment technologies.