Optimization of lipid production in Chlorella vulgaris for biodiesel production using flux balance analysis
Introduction
Due to the fossil fuels crisis in the mid 1970s and the emission of atmospheric carbon dioxide on their combustion, using biofuels to replace diminishing oil reserves has become an important topic worldwide. Among different types of biofuels, biodiesel production has recently attracted much attention [1,2].
Biodiesel is made from oils of biomass and oleaginous plants such as palm, sunflower, and soybean [1]. During recent years, a lot of research reports have described the advantages of using microalgae over other available resources for biodiesel production, such as rapid growth rate, high photosynthetic efficiency and biomass productivity [[3], [4], [5], [6]], relatively high oil content [7], and fatty acid profile similar to typical vegetable oils [1]. Since the low biomass production rate and lipid content of some algae species are the main obstacles for commercial production of biodiesel, maximizing algal biomass and lipid biosynthesis are necessary for bulk production of algal oil. In this regard, several approaches comprising screening of microalgae [8], bioprocess optimization [9], medium optimization [10], genetic manipulation [11], and mathematical modeling [12] have already been used to increase algal biomass and lipid yield.
Metabolic flux analysis (MFA) is a suitable methodology for mathematical modeling of metabolic pathways. Among different flux analysis techniques, flux balance analysis (FBA) has been developed and applied to determine pseudo steady state metabolic flux distribution in complex biochemical and biological systems in the past three decades. FBA is an optimization-based approach that requires stoichiometric information about the metabolic pathway and relies on relatively strong measurements of extracellular metabolites [13]. FBA has been utilized in a diverse range of applications including metabolic engineering [14], development and analysis of metabolic networks at genome-scale [15], analysis of gene deletion effects [16], identification of drug target [17], as well as in refinement of biochemical/metabolic networks [18].
Due to the increasing interest in microalgae as multi-use feedstocks, some metabolic models have been proposed for better understanding of algal metabolic networks. In simple models, each algal cell was considered as a black box, in which merely production and consumption rates were considered [19,20]. A relatively simple metabolic model has been developed to describe the primary metabolism of Chlorella pyrenoidosa when cultivated under autotrophic, heterotrophic, and mixotrophic conditions. In this model, the effect of light on carbon and energy metabolism was investigated using MFA. The model comprised chloroplast and cytosol compartments, in which all the processes except Calvin cycle were placed in cytosol. Furthermore, the metabolic pathways in biosynthesis of different cellular macromolecules such as carbohydrates, proteins, nucleic acids, chlorophylls and lipids were not investigated [21]. In other proposed models, the construction of two extensive metabolic models elucidating the metabolism of Chlamydomonas reinhardtii has been described. These models had a high level of compartmentalization but the localization was not known for a number of reactions and there was limited information regarding the metabolites exchange between the compartments. The quantitative validation of the model was very limited, and only the way algae controls the hydrogen biosynthetic pathways was investigated [22,23]. A more condensed metabolic model has been developed for the description of the growth and metabolism of C. reinhardtii. In the proposed model, only photosynthetic light reactions and Calvin cycle were compartmentalized and transport steps were not accurately considered. The energy parameters for biomass formation and maintenance were determined using the results of chemostat experiments at different growth rates [24]. Another metabolic model was also constructed to explain the phenotypes of Chlorella sp. FC2 IITG under photoautotrophic and heterotrophic conditions. Flux distributions were predicted during a transition from nutrient repletion to nutrient starvation phases of the growth using FBA. The model employed the maximization of biomass and neutral lipid as the objective functions during only nutrient deficient-phase [25]. Recently, a genome-scale metabolic model has been proposed to elucidate the metabolism of Chlorella vulgaris UTEX 395 (iCZ843) under photoautotrophic, heterotrophic, and mixotrophic growth conditions. In this model, the aim was to maximize the specific growth rate and the influence of medium compositions (e.g. glucose, acetate, glycerol, and nitrate) on central carbon metabolism and biosynthetic pathways of amino acid, pigment, and nucleotide were studied at different trophic conditions. Although, the model comprised 843 genes, 2294 reactions, and 1770 metabolites that were distributed among six compartments (thylakoid, chloroplast, mitochondrion, glyoxysome, cytoplasm, and extracellular space), but its high complexity did not allow direct comparison of the simulation results with the experimental data [26]. Still, more fundamental studies in algal metabolism would be required to understand and predict the ways that algae regulate the lipid biosynthetic pathways in response to different environmental and nutritional perturbations.
In this study, FBA was used as an optimization tool for determination of the intracellular metabolic fluxes and the maximum theoretical specific growth and lipid production rates for oleaginous green microalgae C. vulgaris. The aim of this work was to develop a comprehensive metabolic model, simulate the behavior of the photosynthetic organism, predict the effect of cultivation factors on the lipid yield, and identify the metabolites that significantly enhance lipid production using sensitivity analysis concept. To elucidate the practical applicability of the given model, the simulation results of growth and algae metabolic functions were validated with the experimental results inferred from other works in the literature.
Section snippets
Materials and methods
Mathematical modeling usually represents a successful approach to analyze the complex biological and biochemical processes [27,28]. In some biochemical systems, there is insufficient kinetic information about metabolic pathways in the organism under investigation. Therefore, metabolic engineering techniques such as FBA that involves less detailed kinetic information is required [29]. FBA can calculate the fluxes through metabolic pathways using a stoichiometric model and by applying mass
Validation of model predictions
The practical applicability of the proposed metabolic model was investigated using experimental data found from other works in the literature.
Conclusions
A flux-based approach was used to analyze a fully compartmentalized metabolic network including 347 enzymatic reactions, 195 transport processes, and 258 intracellular metabolites that were distributed among four cell compartments (chloroplast, mitochondrion, peroxisome, and cytosol) for C. vulgaris AG10032. The developed model was able to predict the specific growth rates with maximum relative errors of 11.74% and 13.67%, respectively, during the autotrophic cultivations without additional
Declarations
No conflicts, informed consent, human or animal rights applicable.
Acknowledgments
This research project was funded by Ferdowsi university of Mashhad’s research center, Iran (grant number 25902). The authors especially appreciate Dr. Abrishamchi for her fruitful suggestion and assistance.
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