A conventional approach to microalgae process modeling has been the development of empirical models, which are based on experimental observations and measurements. This approach offers high simplicity and accessibility, but it has the limitations of being highly dependent on experimental settings and not reflecting a mechanistic understanding of the underlying biochemical phenomena. As an alternative, metabolic network models, which develop stoichiometric model parameters based on biochemical data representing the metabolism of a species or community, have also been suggested. This approach tracks the flows of individual metabolites and typically involves hundreds or thousands of reactions, providing a thorough view of the entire metabolic system with theoretically grounded yield coefficients. However, metabolic reconstructions are complex, hard to manipulate, and computationally burdensome, making the information impractical for process engineers implementing full-scale microalgal technologies.