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MBW:Engineering Cyanobacteria to Produce Biofuels

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Engineering Cyanobacteria to Produce Biofuels

Executive Summary

Our reliance on fossil fuels has become unsustainable, not only due to severe environmental impacts but also because supply is limited. Biodiesel presents an exciting solution to this problem. In the article reviewed, Jawaharraj, et al., present and optimize a model for the ideal substrate composition which maximizes lipid production in several strains of cyanobacteria. Myxosarcina sp., a unicellular cyanobacteria strain, is selected as the standout option for biofuel production. This particular strain, in the right substrate, was able to yield high quantities of lipids which met almost all of the national and international requirements for biodiesel. Cyanobacteria is a stimulating option for biofuel production as it provides additional benefits such as carbon fixation and the utilization of wastewater as a substrate, thus mitigating the effects of global warming.

Biological Background

As global demand for energy increases, we will be forced to contend with an already limited supply of fossil fuels. It has been postulated that fossil fuels only meet about 85% of today’s total global energy demand and that these resources could be completely exhausted in the near future. Burning these fuels at such a rapid pace emits large quantities of CO_{2} into the atmosphere thus, raising global temperatures. Fossil fuel reliance is accelerating and the need for a renewable and clean replacement is imminent. Biodiesel from cyanobacteria could be the solution to this problem as they can be engineered to excrete biodiesel precursors by fixing carbon and elements from wastewater while using the sun as an energy source.

Cyanobacteria naturally produce free fatty acids which can be mixed with alcohol to make biodiesel. In order to produce biodiesel on a large enough scale, cyanobacteria can be engineered to excrete fatty acids but, this compromises cellular fitness (which could give an advantage to intrusive algae species as described in Resource Competition Between Algae). To mitigate these effects, cyanobacteria must be grown in the correct medium. The right balance of atmospheric and substrate composition can be controlled to yield a lipid content which will meet biodiesel standards.

Cyanobacteria are photosynthesizing organisms. Photosynthesis occurs via the Calvin-Benson-Bassham cycle: sunlight enters the cell and that energy is stored, and later transported by, the molecules ATP and NADPH. That solar energy is then used in carbon fixation, where a molecule containing carbon, such as bicarbonate (HCO_{3}), enters the cell and is transformed by the enzymes RuBisCO (ribulose 1, 5-biphosphate carboxylase/oxygenase) and carbonic anhydrase into usable energy for the cell, generally in the form of sugars like glucose.

Prehistorically, cyanobacteria were responsible for changing the composition of Earth’s atmosphere from a very high carbon concentration to what we observe today. Currently, cyanobacteria are found in nearly every ecosystem on Earth and contribute significantly to Earth’s total carbon fixation.

At very high carbon concentrations, cyanobacteria have no trouble performing carbon fixation. However, at a lower carbon concentration, like on Earth, available carbon is limited. Cyanobacteria evolved a mechanism to compensate for this by concentrating carbon within the cell. This is referred to, not surprisingly, as the carbon concentrating mechanism (ccm) which brings about carboxysome formation. While this process is necessary for cyanobacteria survival on Earth, it comes at a high energy cost for the cell.

Nevertheless, cyanobacteria are an ideal candidate for biodiesel production. Other sources of biodiesel include plant oils, animal fat, used cooking oil and, most commonly, soybean oil. Use of these oils is effective, however, they are all limited in quantity and compete with the need for human consumption. This points to algal oils, affectionately referred to as oilgaes, as a best option for a renewable biodiesel feedstock.

Utilizing cyanobacteria for this purpose has additional benefits such as their ability to fix atmospheric nitrogen and carbon. Nutrient requirements are inexpensive and can utilize wastewater as cyanobacteria uptake in inorganic nitrogen and phosphorous, applicable in bioremediation of wastewater. Cyanobacteria have high photosynthetic capability as they can convert up to 10% of solar energy into biomass, whereas other microalgae strains can only convert around 5%. Growing cyanobacteria is space efficient and does not require fertile land like plant oils do. They can be cultivated almost anywhere, indoor or outdoor, via photo-bioreactors (PBRs) or raceway ponds. For large scale applications, PBRs are likely the best option as pond growth requires much more space and has the possibility of contamination. Cultivation of algae will be most successful in well designed outdoor PBRs under controlled conditions.

Engineering Cyanobacteria to Excrete Free Fatty Acids

Free Fatty Acids are large hydrocarbon chains that can be converted into biodiesel. Cyanobacteria naturally produce these fatty acids to reinforce the lipid layer in the thylakoid membrane. The gene acyl-ACP is responsible for keeping these fatty acids inside the cell. This gene can be interrupted causing the bacteria to excrete the fatty acids instead of recycling them in the cell. Engineering cyanobacteria to excrete fatty acids means they can be recovered easily without killing or severely damaging cells, as opposed to other microalgae, which cannot be cultivated as easily. Leeching lipids from cells does however, negatively impact some cellular functionality. Current free fatty acid yields are much too low for large-scale production and engineered strains may show reduced photosynthetic yields as well. The results of this paper seem to suggest that some of these effects can be controlled with the optimized medium.

In order for cyanobacteria to be a viable option for mass biodiesel production, these limitations must be addressed. Jawaharraj, et al., propose a substrate optimization scheme for the medium in which the cyanobacteria are grown. This environment can be tightly controlled so as to maximize both biomass production and lipid production and content.

The study highlights a specific strain of cyanobacteria, Myxosarcina sp., as having the best productivity in a medium comprised of wastewater discharged from sugarcane processing (SIW) and, subjecting the strain to some small salinity stress. They use response surface methodology to analyze a mathematical model for optimal medium content. The fatty acid profile of the resultant biodiesel is then tested for fuel properties identified by the US government as well as the European Union and India, and is found to meet nearly every requirement.

The Model

Jawaharraj, et al., propose a model for biomass production, lipid production and lipid content with the aim of optimizing medium conditions for these important parameters. These quantities were estimated from the following equations:

Biomass Productivity, BP = {\frac  {(A_{1}-A_{2})}{(t_{2}-t_{1})}} in mg/L per day, the change in the dry cell weight of the initial and final biomass in the time interval t2 – t1

Lipid Content, LC = DLW/DCW, the percentage of dry lipid weight per dry cell weight

Lipid Productivity, LP = BP x LC in mg/L per day, Biomass productivity times Lipid Content

From these estimates, response surface methodology, RSM, was used to find the optimal medium composition. RSM is a method which seeks optimal settings for process factors, or independent variables, in order to maximize, minimize or stabilize a response, the dependent variables. The response is a surface constructed by overlaying contour maps from multiple responses and searching for the ideal setting. The RSM method is built upon the idea that any mathematical function can be approximated by a power series, like a Taylor series. This method only requires up to a second order polynomial.

Factors are screened for relevance by determining their influence on the response variables. In this case, relevant factors were determined by performing statistical analysis on the following quadratic equation in which represents one of biomass productivity, lipid content or lipid productivity.

Y_{i}=\beta _{0}+sum\beta _{i}X_{i}+sum\beta _{i}i(X_{i})^{2}+sumsum\beta _{i}jX_{i}X_{j}+\epsilon

\beta _{0} is the intercept term \beta _{i} the linear effect \beta _{i}i the squared effect \beta _{i}j the interaction effect X_{i},X_{j} the factors or independent variables \epsilon the error term

The significant independent variables in this experiment were (A) NaCl, (B) SIW, (C) NaNO_{3}, and (D) MgSO_{4}. Design Expert® Software was used to perform the analysis using a central composite rotary design, CCRD, which builds the second order quadratic model for the response variables.


This method, RSM-CCRD, produced the following quadratic equation for the response in lipid productivity.

Lipid Productivity = +1.65 - 0.16A + 0.032B + 0.15C + 0.3D - 0.13AB + 0.059AC + 0.07AD - 0.12BC + 0.16BD - 0.083CD - 0.13A^2 - 0.21B^2 + 0.24C^2 - 0.17D^2

The response for this measure of biodiesel viability was found to be statistically significant, whereas biomass productivity and lipid content were not. Varying the concentrations of the four independent variables A, B, C, and D, a maximum lipid productivity was obtained.

The graphs below show the results of RSM, the surfaces shown represent lipid productivity in response to the four independent variables included in the optimized medium.

Statistical analysis shows that the model for lipid productivity fits with experimental data from tests with the RSM optimized medium. The optimized medium showed greater biomass, lipid productivity and lipid content. The results from RSM indicated that a higher concentration of nitrogen and SIW along combined with a slight saline stress, excess salt, improve lipid production for this strain of cyanobacteria. Though saline stress may be an important tool for the production of biomass and lipids, it may also negatively affect growth rate at too high of concentrations.

Tests run with a medium lacking nitrogen, resulted in negative metabolic changes such as decreased fatty acid content and photosynthetic impairment, which suggest that nitrogen is essential to cyanobacterial growth and lipid production.

Testing Biofuel Standards

With the increased yield in lipids, biofuel was produced and tested again national and international standards. The agencies responsible for setting these standards identify eleven critical fuel parameters which can be evaluated from the fatty acid profile empirically using the following equations.

Biodiesel Fuel Properties:

Saponification Value, SV, sumfrac{560F_{i}}{M_{i}} Iodine Value, IV, sumfrac{254F_{i}D_{i}}{M_{i}} Cetane Number, CN, (46.3+(frac{5458}{SV}))-0.225IV

F_{i} is the percentage of each type of fatty acid M_{i} is the molecular mass of the corresponding fatty acid D_{i} is the number of double bonds on that fatty acid

Degree of Unsaturation, DU, MUFA wt% + (2PUFA wt%)

MUFA are monounsaturated fatty acids PUFA are polyunsaturated fatty acids

Long Chain Saturation Factor, LCSF, 0.1C16 + 0.5C18 + C20 + 1.5C22 + 2C24

C16, C18, C20, C22 represent percentages of these specific types of fatty acids

Cold Filter Plugging Point, CFPP, 3.1417LCSF - 16.477 Cloud Point, CP, 0.526C16 - 4.992 Pour Point, PP, 0.571C16 - 12.240 Viscosity, v, ln(v) = -12.503 + 2.496ln(Mi) - 0.178Di Density, r, \rho _{i}=0.8463+frac{4.9}{M_{i}}+0.0118D_{i} Higher Heating Value, HHV, 46.19+frac{1794}{M_{i}}-0.21D_{i}

These eleven fuel properties depend heavily on the proportions of saturated vs unsaturated fatty acids. Unsaturated fatty acids may become toxic to cyanobacteria in large quantities or if not kept in balance with saturated fatty acids. All but one of the standard specifications was met and by a good margin. For example, cold filter plugging point, CFPP, produced by this strain was , much lower than the allowable maximum of . This measure is important in colder climates as a higher degree of long chain saturation results in crystallization which clogs fuel lines and impairs engine performance. The kinematic viscosity of biodiesel from this strain was found to be 1.5 mm2/second. This is lower than the required specifications. High viscosity fuel results in poor engine performance with greater exhaust smoke and emissions.

The only specification not met, was density. The density was only 1.1 gram/m3, slightly higher than the standard 0.86 – 0.9 g/m3. The paper neglected to discuss the affect this would have on engine performance.


The model presented here produces an optimal medium in which to grow cyanobacteria for the best quality biodiesel. The results suggest that this strain of cyanobacteria can be used widely as an alternate biodiesel feedstock that can mitigate atmospheric CO2 while utilizing wastewater as a substrate simultaneously. In future experiments, CO2 concentration in the medium could be increased to the point where the costly carbon concentrating mechanism is no longer necessary, thus freeing up energy in the cell for lipid production.


Jawaharraj, Kalimuthu, et al. "Green Renewable Energy Production from Myxosarcinasp.: Media Optimization and Assessment of Biodiesel Fuel Properties." RSC Advances 5.63 (2015): 51149-57. ProQuest. Web. 16 Mar. 2016.

Additional References

Cameron, Jeffrey C. et al. “Biogenesis of a Bacterial Organelle: The Carboxysome Assembly Pathway.” Cell 155.5 (2013): 1131–1140. Web. 16 Sept. 2015.

Clark, Ryan L. et al. “Insights into the Industrial Growth of Cyanobacteria from a Model of the Carbon-Concentrating Mechanism.” AIChE Journal 60.4 (2014): 1269–1277. Web. 16 Sept. 2015.

Ruffing, Anne M., and Howland DT Jones. "Physiological effects of free fatty acid production in genetically engineered Synechococcus elongatus PCC 7942." Biotechnology and bioengineering 109.9 (2012): 2190-2199.

See Also

MBW:Modeling method for combining fluid dynamics and algal growth in a bubble column photobioreactor

MBW:Simulate a continuous operation photobioreactor utilizing the Photosynthetic Factory Model (PSF)

MBW:Modeling Photoinhibition of Photosystem II