Integration of the droop model with the flux balance analysis method to predict metabolic shifts in microalgae growth

Abstract

Identifying the mechanism for and predicting the metabolic shift between lipid accumula-tion and cell growth is a key research issue for microalgal biodiesel production. In this study, we propose a novel way to integrate a metabolic network model with a semi-empirical model (called “Droop model”) for predicting the lipid accumulation and cell growth simultaneously. At each time instant of mass balance model integration, the Droop model is used to predict the cell growth rate. Then, the Flux Balance Analysis (FBA) model is used to predict the rate of lipid accumulation, which is biochemically consistent with the predicted growth rate. In order to test the validity of the proposed approach, experiments are conducted for growing microalgae specie C. reinhardtii in a batch photobioreactor. Droop model’s parameters are estimated using the gathered data and model predictions for the lipid contents are verified. Parameter sensitivity analysis is conducted to investigate how the various parameters affect the cell growth and lipid accumulation. With the model, metabolic shift is simulated with time variation, and flux distribution movement is specified and described on a microalgae metabolism map. In addition, the relationship between phenotypes and internal fluxes is analyzed under several initial conditions. Ultimately, the integrated model will be extended to develop a multi-input system and/or applied to a large-scale open pond system.