Simulating cyanobacterial phenotypes by integrating flux balance analysis, kinetics, and a light distribution function
© He et al. 2015
Received: 7 October 2015
Accepted: 12 December 2015
Published: 24 December 2015
Genome-scale models (GSMs) are widely used to predict cyanobacterial phenotypes in photobioreactors (PBRs). However, stoichiometric GSMs mainly focus on fluxome that result in maximal yields. Cyanobacterial metabolism is controlled by both intracellular enzymes and photobioreactor conditions. To connect both intracellular and extracellular information and achieve a better understanding of PBRs productivities, this study integrates a genome-scale metabolic model of Synechocystis 6803 with growth kinetics, cell movements, and a light distribution function. The hybrid platform not only maps flux dynamics in cells of sub-populations but also predicts overall production titer and rate in PBRs.
Analysis of the integrated GSM demonstrates several results. First, cyanobacteria are capable of reaching high biomass concentration (>20 g/L in 21 days) in PBRs without light and CO2 mass transfer limitations. Second, fluxome in a single cyanobacterium may show stochastic changes due to random cell movements in PBRs. Third, insufficient light due to cell self-shading can activate the oxidative pentose phosphate pathway in subpopulation cells. Fourth, the model indicates that the removal of glycogen synthesis pathway may not improve cyanobacterial bio-production in large-size PBRs, because glycogen can support cell growth in the dark zones. Based on experimental data, the integrated GSM estimates that Synechocystis 6803 in shake flask conditions has a photosynthesis efficiency of ~2.7 %.
The multiple-scale integrated GSM, which examines both intracellular and extracellular domains, can be used to predict production yield/rate/titer in large-size PBRs. More importantly, genetic engineering strategies predicted by a traditional GSM may work well only in optimal growth conditions. In contrast, the integrated GSM may reveal mutant physiologies in diverse bioreactor conditions, leading to the design of robust strains with high chances of success in industrial settings.
In photobioreactors (PBRs), light penetration depth at high cell density can be as short as a few centimeters . Thus, during large-size PBR cultivation, cyanobacteria move continuously between the “light zone” (where light is sufficient) and the “dark zone” (where light is substantially shaded). As a consequence, cyanobacterial metabolism in PBRs is spatially and temporally dependent: cells have autotrophic growth in the light zone, and they perform heterotrophic growth in the dark zone by consuming energy-storage compounds. Moreover, PBR performances are also affected by the efficiency of CO2 gas–liquid transfer. To enhance mass transfer, people often use CO2-enriched air in combination with high intensity mixing. Many models have been developed to understand how cyanobacterial physiological dynamics are influenced by the light intensity, CO2 supply, temperature, and geometry of PBRs [2–6]. Those kinetic and reactor studies are useful in optimizing PBR design and operations. However, bioprocess modeling is unable to provide an understanding of intracellular enzyme functions and metabolic fluxes in cyanobacteria. To improve engineered microalgae strains’ metabolisms in large-size PBRs, it is necessary to link process models to metabolic models.
On the other hand, metabolic flux analyses (MFA) can quantify in vivo enzyme reaction rates, and thus allow us to investigate the flux phenotypes resulting from complicated gene-protein-metabolite regulations. 13C-MFA measures carbon fluxes through the central metabolism via 13C labeling experiments. Alternatively, genome-scale flux balance analysis (FBA) can generate a holistic intracellular flux distribution map  owing to its extended coverage of genomic information . Computational platforms, such as COBRA  and OptForce , can predict genetic targets and guide rational designs of engineered strains. FBA can also be integrated with constraint-based elementary flux mode analysis to identify optimal pathways for bio-productions . However, an inherent limitation of traditional GSM is that it predicts only flux distributions that result in maximal yields in an optimal culture condition. They cannot forecast mutant strains’ production titers and rates in dynamic and heterogeneous bioreactors.
Simulation of cyanobacterial optimal growth in a cylindrical PBR
Finally, the simulations also demonstrate that, due to random cell movements in PBRs, single cell fluxome may show stochastic changes (Additional file 2: Fig. S2 b, c). Additionally, we tested the sensitivity of biomass growth to circulation time. The model indicates that perturbing the circulation speeds of cell subpopulations did not affect total biomass production by PBRs, provided that the CO2 mass transfer and surface light were constant (Additional file 2: Fig. S3).
Simulation of cyanobacterial oxidative pentose phosphate pathway in a cylindrical PBR
Investigation of cyanobacterial photosynthesis efficiency in shake flasks
Model-based investigation of lactate production by engineered cyanobacteria in PBRs
Deleting carbon storage in cyanobacteria is one strategy to redirect carbon flux to product synthesis . However, the integrated GSM shows that such a strategy may not offer significant improvements in final lactate productivity in PBRs (Fig. 6a, d, e). This finding is consistent with two recent reports: (1) Glycogen knockout did not enhance lactate productivity under nutrient-sufficient growth conditions . (2) Removal of glycogen in an isobutanol-producing cyanobacterium yielded no benefit in production titer and rate . Possibly, glycogen serves as the carbon and energy reservoir to store the energy and carbon excess flow in the light zone, and this carbon and energy reservoir can maintain redox homeostasis under stressed growth conditions or in darkness . Therefore, deleting glycogen or other carbon storage may impair cyanobacterial survival as well as its resistance to environmental stresses and contaminations.
In this study, a genome-scale FBA model was integrated with information on kinetics, light distribution, and cell movement. Using the integrated GSM, one can simultaneously learn both intracellular information (e.g., flux distributions as functions of time) and extracellular information (e.g., growth curve and nutrient changes in the medium) simultaneously.
In the extracellular domain, the integrated GSM can describe changes in nutrient concentrations, biomass accumulation, and local light intensities. As demonstrated by Fig. 3, cyanobacteria are intrinsically capable of reaching high biomass concentration in PBRs, however, their performance is usually limited by low light availability and low mass transfer rate. To reduce cell self-shading, high surface-to-volume ratio PBRs equipped with thin panel or hollow fibers have been employed . To improve the mass transfer rate, enhancing aeration rates has proved efficient . Better mixing conditions not only lead to better gas transfer rates, but also help maintain more homogeneous conditions for both cells and nutrients.
In the intracellular domain, we observed continuously changing fluxes in the cyanobacterial metabolic network, which were mostly affected by reduced energy and carbon inputs. One interesting finding is the OPP pathway activation as cell cultures get denser. It is a traditional point of view that the Calvin cycle and the OPP pathway are separate systems because the two pathways are reciprocally regulated . In this study, the integrated GSM describes that these two pathways could be employed by two different subpopulations in PBRs at the same time. This simulation explains that a measureable flux through the OPP pathway could be observed in both wild type and engineered cyanobacterial strains via 13C-based flux analysis [24, 25]. In addition, the elementary modes analysis shows that the Calvin cycle and the OPP pathway may function in complementary ways in photoautotrophs, since an active OPP pathway ensures a maintainable flux to triose phosphate synthesis from carbohydrate degradation in low light or darkness . Furthermore, we noticed that an active OPP pathway was always present in the d-lactate producing strain, and it became stronger with increased d-lactate production (Additional file 2: Fig. S7). This indicates that the enhanced OPP activity benefits bio-production by providing more reducing power. In summary, the plasticity of the OPP pathway endows cells with high vitality and energy flexibility [34, 35].
The traditional FBA model usually describes the optimal growth condition, and thus it may not be suitable for suboptimal and heterogeneous cultivation conditions. Our model, on the other hand, is integrated with growth kinetics and a heterogeneous light distribution in PBRs. Hence, the model can not only predict the production yield, titer and rate, but also offer insights into how cells adjust their internal metabolisms to survive under different growth conditions and genetic manipulations. Moreover, the integrated GSM may give more accurate predictions of mutant physiology than GSM alone in bioreactor conditions. For example, the integrated GSM correctly indicates that glycogen knockout may not be an effective strategy to improve PBR lactate production. Lastly, the integrated GSM can reveal real-time variations/dynamics in metabolisms of different subpopulation cells, and thus improve understandings of cellular responses to large-size PBRs.
Nevertheless, our model still has limitations. For example, previous studies have shown that glycogen synthesis could be connected with unknown regulations affecting cyanobacterial viability under stress conditions [32, 36]. However, the integrated GSM may not give the same prediction without further constraints from knowledge of genetic regulations. Additionally, it has been demonstrated that cyanobacteria have circadian behaviors (i.e., their metabolism exhibits day and night rhythms) , while our model does not include this property. Finally, some inhibition factors may also influence cyanobacterial growth (e.g., effects of crowding), which are not included in the model. In the future, this model platform should be further improved via additional multi-scale modeling approaches.
This study demonstrates a genome-scale FBA model integrated with kinetics, cell movements, and a light distribution function. With constraints obtained from bioprocess variables, the integrated GSM can not only simulate the dynamic metabolisms in sub-population cells but also predict PBR overall productivity under light and CO2 conditions. The integration of GSMs with PBR modeling can facilitate the development of new cyanobacterial strains for industrial settings.
Synechocystis PCC 6803 was cultivated in a modified BG-11 medium  at 30 °C and 180 rpm. We first tested the cyanobacterial growth in different culture volumes. In brief, 50, 100, and 150 mL of cell suspensions were cultivated in 250 mL shake flasks under continuous illumination of ~50 µE/m2/s. We also tested the cyanobacterial growth under different light conditions. Specifically, 15 mL of cultures were grown in 150 mL shake flasks under different light intensities (from ~15 to ~35 µE/m2/s). OD730 was used to measure biomass density, and the relationship between the biomass concentration and OD730 was 0.45 × OD730 = Biomass (g/L) . We made duplicate cultures of each condition (n = 2).
13C-labeling experiments were performed to determine histidine labeling as evidence of OPP pathway activity under different light conditions. We grew photomixotrophic cultures in BG-11 medium supplied with 2.5 g/L [1-13C] glucose and 4 g/L NaH13CO3 (tracers were purchased from Sigma-Aldrich, Saint Louis, USA). The TBDMS (N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide) method  was used to analyze the labeling patterns of proteinogenic histidine. In brief, cells were harvested by centrifugation, and cell pellets were hydrolyzed in 6 mol/L HCl solution at 100 °C for 24 h. The amino acid solution was air-dried and then derivatized by TBDMS (Sigma-Aldrich, USA) at 70 °C for 1 h. A gas chromatograph (GC) (Hewlett-Packard model 7890A; Agilent Technologies, CA) equipped with a DB5-MS column (J&W Scientific, Folsom, CA) and a mass spectrometer (model 5975C; Agilent Technologies, CA) were used for analyzing amino acid labeling profiles. The GC–MS fragment [M-57]+ contains the complete amino acid backbone, and MS data M + 0, M + 1, and M + 2 represent isotopomers with zero, one, and two 13C atoms, respectively.
Flux balance analysis model
Moreover, we considered three growth states for cyanobacteria in the FBA model: (1) the ‘light condition’: an autotrophic sub-population in the light zone, (2) the ‘dark condition’: a heterotrophic sub-population in the dark zone, where the photon influx is below 0.4 mmol/g/h (under which the cyanobacterial growth rate is lower than the heterotrophic growth rate in darkness) and glycogen is consumed at a rate of 0.01 mmol/g/h  to maintain minimal growth, and (3) the ‘resting condition’: a glycogen-depleted sub-population with no active fluxes in the dark zone. To improve the calculation efficiency, we built a database containing all the flux distributions in response to different photon influxes (Additional file 1). By having such a database, we could directly use pre-calculated fluxome from the database according to culture conditions in PBRs. Thereby, we did not need to redo flux calculations at each time interval during new simulations.
Simulation of cyanobacterial growth via integrating FBA, kinetics, and cell movements
List of parameters used to simulate the growth and metabolic fluxes of cyanobacteria growing in a cylindrical PBR
Mass transfer rate of CO2
Radius of PBR
Similar to the reactor used in Reference 
Surface light intensity
Equivalent to ~50 µE/m2/sb
Half-saturation constant of CO2 uptake rate
Initial biomass concentration
Equivalent to an OD730 of ~0.2
Initial concentration of dissolved CO2 and HCO3 − (in equilibrium with air)
YJT and LH initialized this project. LH and SW built the integrated GSM. LH, NW, and AR performed the experiments. All authors read and approved the final manuscript.
We would like to thank Dr. Le You for valuable discussions for this project. We are also grateful for the funding supports from a US DOE grant (DESC0012722) and an NSF Grant (CBET 1438125).
The authors declare that they have no competing interests.
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