Modeling of microalgal metabolism

Microalgae are a potential source for a wide range of products, such as carotenoids, lipids, hydrogen, protein and starch, which are of interest for food, feed and biofuel applications. Maximization of microalgal product and biomass productivity in (large-scale) outdoor photobioreactors is important for commercial production of these compounds. Microalgae are unicellular eukaryotic organisms capable of using (sun)light as an energy source through photosynthesis. During photoautotrophic growth microalgae consume CO2 and produce O2. In addition, O2 is taken up in a number of processes such as mitochondrial respiration and photorespiration.
A very important objective of applied algae research is to maximize biomass (or product) yield and at the same time minimize the energy input to reduce cultivation costs. Insight in the metabolism of the microalgae is a valuable tool to optimize cultivation parameters accordingly.
To be able to study the effect of cultivation parameters such as mixing on productivity of algal cultures we designed a lab-scale photobioreactor in which a short light path (SLP) of 12 mm is combined with controlled mixing and aeration (Chapter 2). Mixing was provided by rotating an inner tube in the cylindrical cultivation vessel creating Taylor vortex flow and as such it could be uncoupled from aeration. Gas exchange was monitored on-line to gain insight in growth and productivity. The maximal productivity, hence photosynthetic efficiency, of Chlorella sorokiniana cultures at high light intensities (1500 µmol m-1 s-1) was investigated in this Taylor vortex flow SLP photobioreactor. We performed duplicate batch experiments at three different mixing rates: 70, 110 and 140 rpm, all in the turbulent Taylor vortex flow regime. For the mixing rate of 140 rpm we calculated a quantum requirement for oxygen evolution of 21.2 mol PAR photons per mol O2 and a yield of biomass on light energy of 0.8 g mol-1. The maximal photosynthetic efficiency was found at relatively low biomass densities (2.3 g L-1) at which light was just attenuated before reaching the rear of the culture. Upon doubling the mixing rate we only found a small increase of 5% in productivity. Based on these results we concluded that the maximal productivity and photosynthetic efficiency for C. sorokiniana can be found at that biomass concentration where no significant dark zone is present and that the influence of mixing-induced light/dark fluctuations is marginal.
In addition to moving the algae through the light gradient in the photobioreactor and preventing the cells from settling, mixing is also needed to supply CO2 to the culture and remove the O2 produced in photosynthesis. This O2 can easily build up to high concentrations in closed photobioreactors and this can have a negative effect on productivity and inhibit growth of the microalgae. If mixing is decreased to reduce energy consumption, which is preferable, this effect can be even larger because more O2 can build up in the culture. In illuminated microalgal cells several processes in which O2 is involved occur simultaneously. Under the cultivation conditions in the experiments in Chapter 3 two processes dominated, photosynthesis and mitochondrial light respiration. The net O2 exchange rate, which could be measured on-line, is the sum of the O2 production by photosynthesis (gross OPR) and O2 consumption through respiration in the mitochondria (OUR). To know the rates of these two processes one of these had to be measured. We measured the immediate post-illumination respiratory O2 uptake rate (OUR) in-situ, using fiber-optic O2 micro sensors, and a small and simple extension of the cultivation system. This method enabled rapid and frequent measurements without disturbing the cultivation and growth of the microalgae. Two batch experiments were performed with C. sorokiniana in the SLP photobioreactor and the OUR was measured at different time points during cultivation. The net O2 production rate (net OPR) was measured on-line. Adding the OUR and net OPR gave the gross O2 production rate (gross OPR), which is a measure for the O2 evolution by photosynthesis. The gross OPR was 35-40% higher than the net OPR for both experiments, showing that photosynthesis is underestimated when only looking at net OPR. The respiration rate is known to be related to the growth rate and it is suggested that faster algal growth leads to a higher energy (ATP) requirement and, as such, respiratory activity increases. This hypothesis is supported by our results, as the specific OUR was highest in the beginning of the batch culture when the specific growth rate was highest. In addition, the specific OUR decreased towards the end of the experiments until it reached a stable value of around 0.3 mmol O2 h-1 g-1. This suggests that respiration could fulfill the maintenance requirements of the microalgal cells.

To obtain the desired insight in the metabolism (more specifically O2 metabolism) and understand the mechanisms behind the allocation of light energy for microalgal growth and maintenance, we constructed a metabolic model describing the metabolism of the green microalga Chlamydomonas reinhardtii (Chapter 4). Starting from genomic information a large network with over 300 enzymatic reactions was obtained which was reduced to a smaller, more practical network by lumping linear pathways. The resulting network contained 160 reactions and 164 compounds. Seven chemostat experiments were performed at different growth rates to determine the energy requirements for maintenance and biomass formation in vivo. The chemostats were run at low light intensities resulting in a high biomass yield on light of 1.25 g mol-1. The ATP requirement for biomass formation from biopolymers was determined to be 109 mmol ATP g-1 (18.9 mol mol-1) and the maintenance requirement was determined to be 2.85 mmol ATP g-1 h-1. With these energy requirements included in the metabolic network, the network accurately described the primary metabolism of C. reinhardtii and can be used for modeling of C. reinhardtii growth and metabolism. Simulations showed that cultivating microalgae at low growth rates is unfavorable because of the high maintenance requirements which result in low biomass yields. At high light supply rates biomass yields will decrease due to light saturation effects. Thus, to optimize biomass yield on light energy in photobioreactors, an optimum between low and high light supply rates should be found. In addition, these simulations showed that respiration can provide both energy for maintenance and additional energy to support growth.
With this model Metabolic Flux Analysis (MFA) could be performed to study the effect of elevated O2 concentrations on the metabolism and the biomass yield of C. reinhardtii (Chapter 5). High O2 : CO2 ratios can have a negative effect on growth and productivity of microalgae. To investigate the effect of O2 and CO2 concentrations and the ratio between these on the metabolism of C. reinhardtii we performed turbidostat experiments at different O2 : CO2 ratios. These experiments showed that elevated O2 concentrations and the corresponding increase in the ratio of O2 : CO2 common in photobioreactors led to a reduction of growth and biomass yield on light with 20-30%. This was most probably related to the oxygenase activity of Rubisco and the resulting process of photorespiration. Using MFA with measured rates for each experiment we were able to quantify the ratio of the oxygenase reaction to the carboxylase reaction of Rubisco and could demonstrate that photorespiration indeed could have caused the reduction in biomass yield on light. The calculated ratio of the oxygenase reaction to the carboxylase reaction was 16.6% and 20.5% for air with 2% CO2 and 1% CO2 respectively. Thus, photorespiration has a significant impact on the biomass yield on light already at conditions common in photobioreactors (air with 2% CO2).
To thoroughly understand the energy metabolism of microalgal cells and with that the conversion of light energy into biomass, insight into the different processes involved in O2 production and uptake and CO2 consumption and production is necessary. An overview of the processes that play a dominant role in the generation of metabolic energy and reductants is given in Chapter 6. To reach high biomass yields on light (i.e. productivity) processes such as the Mehler reaction, photoinhibition and photorespiration should be avoided. A number of measurement methods are proposed which could be used to determine under which cultivation conditions certain processes take place and to what extent, so that these conditions possibly can be avoided. These measurements need to be combined with metabolic flux analysis in order to accurately assess the real implications of these pathways for the energy budget of the microalgae. For this purpose the metabolic model for C. reinhardtii itself can be further improved and expanded by introducing cellular compartments and possibly adding desired product pathways. This will give more insight in the metabolism and the effect of different parameters on the conversion of light energy into biomass or useful products. If including a dynamic description of the light reactions of photosynthesis it might ultimately be possible to make predictions on biomass and product yield based on the light regime the microalgae are exposed too.