Team:KU LEUVEN/Modeling/Yield analysis

KUL iGEM wiki 2019

KUL iGEM wiki 2019

Yield Analysis


For industrial production of enzymes, it is crucial to predict the amount of biomass that can be harvested, i.e., the final yield of production. Our project involves two different techniques of utilizing different strains of cyanobacteria, which require a different evaluation method:

  1. Farming of Freshwater Cyanobacteria (Synechococcus elongatus sp. UTEX 2973).
  2. Utilizing Wild-type Seawater Cyanobacteria (Synechococcus sp. WH8109) with the use of cyanophages.

Protein yield of UTEX 2973

The production plants using UTEX 2973 would be relatively similar in comparison to other traditional biological organisms currently used in enzymatic production plants (for a more comprehensive explanation please refer to the Industrial Designsection). However, an abundance of incoming solar rays is required to create sustainable growth. Due to this, the local climate plays a vital role in regulating the efficiency of enzymatic production. Similar problems concerning solar light visibility are associated with photovoltaics. A unit often used within the solar panel field is the air mass coefficient(AM), which corresponds to the path taken by a light ray inside the atmosphere. Increasing values correspond to a higher rate of scattering, so a lower amount of light will be detected. Weather conditions can adversely impact the AM value, resulting in lower light conversion rates. In likeness to solar panels, UTEX 2973 also depends on weather conditions for its light ray usage, so our team made the analogy between these systems.

UTEX 2973 has in vivo experimentally reported doubling time of around 1.9h at 500 photon μmol/(m².s), also known as μE [1]. For solar light conditions, such a growth rate would be achieved at an air mass coefficient of around 1.5, which frankly is a long cloudless summer day. If we assume perfect conditions for a 100g dry mass (DM)1L batch of UTEX 2973, it will grow almost 20-fold to 1900g DM after eight hours of photosynthetic growth. Moreover, considering conversion rates for extracellular enzymatic secretion in current industry standards are within the magnitude of 0.1g/g DM conversion, such a rate would result in enzymatic outputs of 190g/L for UTEX 2973.

The above calculation serves as an illustrative example of the hypothetical yield achievable with UTEX 2973, but these perfect conditions are quite unlikely to happen. According to research by Yu et al., it was discovered that a high light dependency existed between the doubling time; for a light intensity of 500μE, corresponding to a sunny day, the previously mentioned rate was achieved, although, an 80% reduction in the photon flux, which gives a significantly lower doubling time. Please refer to Figure 1 for these results [1]. Therefore, production yields of cyanobacteria are incredibly dependent on the available sun photon flux. It is important to note, from Figure 1, that although the yield is reduced at low light levels, the necessity for increased CO2 input is lost, reducing the cost of upkeep.

Moreover, a proper analysis of the climate irradiance cycle would be insufficient if it was missing a 24-h daily sunlightcycle discussion. During night time, cyanobacteria undergo a circadian rhythm that is universally found in all strains [2]. A very similar strain of UTEX 2973 was found to have clock proteins that regulate transcription [3]. Thus, the cyanobacteria used in industrial plants would undergo an oscillating daily circadian rhythm, sufficing a need for protein extraction during the daytime.

Figure 1: Growth Curves of UTEX 2973 for different light and CO2 levels. Image taken from Yu et al. [1] HL=500 µE, HC=3%, LL=100 µE, LC= 0.04% (air concentration)

The yearly sunlight varies, the further the location is from the equator. Please note the y-axis corresponds to the average light energy at the surface, not the solar irradiance, albeit they are directly proportional. Since our current team headquarters are in Leuven, Belgium, at a latitude of 54N, Figure 2 does not boast well for the yearly yield values. Therefore, a modeling system for the growth rate of our cyanobacteria was developed from which a Growth Rate vs. Irradiance Level relation was made (please refer to Figure 3).

Figure 2: Yearly Light energy at the earth's surface. Different lines represent different latitude levels. Image taken from the database of NASA [4].

Flux Balance Analysis

A computational model predicting the growth rates of UTEX 2973 was necessary to predict the yearly changes in growth patterns of the cyanobacteria. The system of choice was Flux Balance Analysis (FBA) that offers a fast and low power modeling technique. The flow of metabolites of the organism’s metabolic network is the main output of the FBA model system, from which growth rate values of the organism can be predicted. Stoichiometric matrices are used to impose constraints on the reaction rates of the network [5]. With well-defined constraints, the target output can be predicted, which, in the case of this investigation, is that of the organisms doubling time.

Our inspiration for using FBA came from the 2018 Stony Brooks iGEM team as they used a similar approach when calculating growth rates of Synechococcus elongatus PCC 7942. The cyanobacterium that was examined by this team is a well-known strain with many in vivo and in silico experimental results as of date. Unfortunately, although FBA has many already existing biological organisms publicly accessible, the novelty of UTEX 2973 created a problem. Nonetheless, the group of Yu et al. argued that both UTEX 2973 and PCC 7942 are incredibly similar in terms of genotype and phenotype [1]. Therefore, we decided to use the Model iJB785 (describing the organism Synechococcus elongatus PCC 7942) corresponding to PCC 7942 akin to the Stony Brooks 2018 team.

The model was taken from the BiGG database (contained are many different types of genome-scale metabolic networks for different organisms which can then be used with the Constraint Based Reconstruction and Analysis, COBRA, toolbox; for further information refer to Shellenberger et al. [6]) As UTEX 2973 is a recently discovered strain, research into understanding the inner workings of the organism is currently being done. Therefore, a full genomic-based model of the internal metabolic networks of UTEX 2973 is poised to be in an incomplete formulation. However, due to its similarity to PCC 7942, a well-studied strain, models that compare both PCC 7942 and UTEX 2973 have been made. For further insight, we consulted the work of Mueller et al. wherein FBA analysis of both PCC 7942 and UTEX 2973 was made and compared [7]. It turns out that a crucial difference between the two organisms is the latter’s increased CO2 uptake, which is almost 5-fold that of PCC 7942. Therefore, in our model, we decided to try and emulate this increased CO2 uptake.

Figure 3: shows the change in doubling time as a ratio to the desirable time calculated using the COBRA toolbox. Please note, the irradiance level is presented as a percentage as the strength and level of irradiance has slight variations in terms of light wavelength.

Unfortunately, the values contained in Figure 3 are not consistent with the previously mentioned experimental values, at an irradiance of 20% that of the max growth rate, the FBA analysis observed a much smaller loss of growth rate than in vivo data would suggest. Nevertheless, a dip in growth rate is observed in very deficient photon flux levels as expected with experimental data; sustainable yearly production will then have a low yield season during the winter months. The inconsistency between our model and experimental data is most likely due to the lack of other factors determining the metabolic networks of UTEX 2973. Furthermore, although PCC 7942 and UTEX 2973 are close relatives of each other, they do ultimately differ in their genomic structure. The group of Unegerer et al. discovered that three crucial genes that vary between these two strains are responsible for the extremely rapid growth rate of UTEX 2973 [9]. Further investigation into this area would require the inclusion of these essential genes in order to better the model to experimental values.

Protein yield of WH8109 using cyanophages

To calculate the yield values utilizing phages for enzymatic secretion, FBA could not be used as steady flow inside the cell culture cannot be sustained due to the use of cyanophages. However, as we identify the different applicable ways one can scale up the phage powered enzymatic production concept, a fundamental bottleneck was found. Unfortunately, phages have a low biomass conversion rate, i.e., the total mass-produced after lysis is low. A brief engineering-styled Fermi problem was made in order to calculate these shortcomings. This technique is often used to create estimates when given low amounts of data to work with; it necessitates making well-educated guesses and assumptions. Herein, a Fermi problem with respective sources for specific values is presented.

Step 1: Calculate the biomass of one phage
The cyanophage S-TIP37 is a T7 phage of a capsid radius of 25nm [9]. The shape is icosahedral with a very short tail. It is safe to assume that the total amount of proteins that make up the scaffold of the capsid and tail is within a total magnitude of 100. Considering the research by Cui et al., the total number of DNA stored in a T7 virus is around 47kbp [10]. Therefore, assuming a mass of 650 Da per base pair and a total mass of 50 kDa per each scaffold protein, a total mass of 3.5 MDa is found for an individual cyanophage.

Step 2: Calculate biomass conversion at maximal burst size
The average burst size for Synechococcus hosts is in the magnitude of 100 [11]. If this number is assumed, then the final conversion rate of the total biomass of the bacterium to the released phages is around 0.0058%, which is lower by a factor of a thousand than the values achieved industrial enzymatic production.

Considering that wild type concentrations are not constant and vary per strain, this number is unfortunately not applicable for industrialization. Even if unnaturally high population numbers are reached, the relatively low conversion rate means that for 20L of water and humic acid (remains of lysed bacteria), only around 100 mL of industrial material can be extracted, resulting in inefficient production as well as overly expensive purification procedures.


  1. J. Yu, M. Liberton, P. F. Cliften, R. D. Head, J. M. Jacobs, R. S. Smith, D. W. Koppenaal, J. J. Brand and H. B. Pakrasi, "Synechococcus elongatus UTEX 2973, a fast growing cyanobacterial chassis for biosynthesis using light and CO2," Scientific Reports, vol. 5, no. 1, p. 8132, July 2015.
  2. J. Lorne, J. Scheffer, A. Lee, M. Painter and V. P. MIao, "Genes controlling circadian rhythm are widely distributed in cyanobacteria.," FEMS Microbiol. Lett., vol. 189, no. 2, pp. 129-133, August 2000.
  3. T. Nishiwaki, Y. Satomi, M. Nakajima, C. Lee, R. Kiyohara, H. Kageyama, Y. Kitayama, M. Temamoto, A. Yamaguchi, A. Hijikata, M. Go, H. Iwasaki, T. Takao and T. Kondo, "Role of KaiC phosphorylation in the circadian clock system of Synechococcus elongatus PCC 7942.," Proceedings of the National Academy of Sciences, vol. 101, no. 38, pp. 13927-13932, 21 September 2004.
  4. NASA: Earth Observatory, "Heating Imbalances," 14 January 2009. [Online]. Available: [Accessed September 2019].
  5. J. Orth, I. Thiele and B. Ø. Palsson, "What is flux balance analysis?," Nature Biotechnology, vol. 28, pp. 245-248, 2010.
  6. J. Schellenberger, J. Park, T. Conrad and B. Ø. Palsson, "BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions.," BMC Bioinformatics, vol. 11, 29 April 2010.
  7. T. J. Mueller, J. L. Ungerer, H. B. Pakrasi and C. D. Maranas, "Identifying the Metabolic Differences of a Fast-Growth Phenotype in Synechococcus UTEX 2973," Scientific Reports, no. 41569, 2017.
  8. J. Ungerer, K. E. Wendt, J. I. Hendry, C. D. Maranas and H. B. Pakrasi, "Comparative genomics reveals the molecular determinants of rapid growth of the cyanobacterium Synechococcus elongatus UTEX 2973," Proceedings of the National Academy of Sciences, vol. 115, no. 50, pp. E11761-E11770, 2018.
  9. N. Dekel-Bird, S. Avrani, G. Sabehi, I. Pekarsky, M. F. Martson, S. Kirzner and D. Lindell, "Diversity and evolutionary relationships of T 7‐like podoviruses infecting marine cyanobacteria," Environmental microbiology, vol. 15, no. 5, pp. 1476-1491, 2013.
  10. J. Cui, T. E. Schlub and E. C. Holmes, "An allometric relationship between the genome length and virion volume of viruses," Journal of Virology, vol. 88, no. 11, pp. 6403-6410, 2014.
  11. R. Milo and R. Phillips, "Cell Biology by the Numbers," July 2015. [Online]. Available: [Accessed September 2019].

KUL iGEM wiki 2019