The basic deterministic design of our model means that complexity is removed at the cost of some accuracy. By manipulating the regulatory structure of the pathway through the design of our model, bottlenecks and stoichiometric imbalances could be investigated. Figure 1A shows that the model meets the most basic requirement in converting L-tyrosine to naringenin.

The extent of active site saturation for each of the four enzymes is displayed in Figure 1A. Both CHI and 4CL remain unsaturated throughout the run time suggesting that these two reactions in the pathway are not rate limiting. TAL is initially highly saturated when L-tyrosine is in greatest excess but as the system reaches a steady state TAL concentration has a less significant effect on the flux of the pathway. As the system moves towards a steady state CHS becomes the most rate limiting enzyme. P-coumaroyl CoA produced by 4CL starts to build up and binds to the active site of CHS. Insufficient malonyl CoA in the system means that naringenin chalcone cannot be produced quickly enough and CHS becomes increasingly saturated with P-coumaroyl CoA.

Figure 1A. Change in relative number of free enzymes for each of the four enzymes in the system over a time period of 50 seconds.

Figure 1B shows how the three intermediate resources in the pathway change over time in comparison to naringenin production. Malonyl CoA is utilised more rapidly than ATP in the system as three molecules of malonyl CoA and only one ATP are required for each molecule of naringenin. In the pathway, one free CoA is utilised to produce P-coumaroyl CoA and four CoA’s are produced as a by-product in naringenin chalcone production. Due to higher flux through the pathway up until P-coumaroyl CoA, after initial fluctuation CoA concentration remains steady.

Figure 1B. Consumption of input resources by the system over a time period of 50 seconds.

The rate of accumulation of each waste product from the pathway is shown in Figure 1C. As CoA is both a substrate and waste product it is included in both Figure 1B and 1C. One molecule of Ammonia and a proton as well as AMP and PPi are produced by TAL and 4CL respectively. Production of these pairs are therefore equal and each pair are represented by one line in Figure 1C. With the exception of CoA, CO2 is the only by-product produced after Pcoumaroyl CoA. Three waste molecules of CO2 are produced for every Ammonia, H+, AMP and PPi. Despite this similar amount of each waste product are produced. This again suggests an imbalance of flux in the pathway

Figure 1C. Accumulation of waste resources in the system over a time period of 50 seconds.

In Figure 2A, 2B and 2C the bottleneck is removed by increasing the input amount of malonyl CoA by 3-fold compared to the other resources required. Figure 2B shows that the change has removed the bottleneck as CHS no longer becomes saturated. Pcoumaroyl CoA no longer remains bound to CHS as sufficient malonyl CoA is available for production of naringenin chalcone.

Figures 2C and 2D show that after an initial decrease CoA increases over time. This is evidence that flux through the system is more balanced as more CoA is produced than utilised by the system so this would be expected to increase over time.

Figure 2B. Consumption of input resources by the system over a time period of 50 seconds when input of Malonyl CoA is 3x other resources.

Figure 2C. Accumulation of waste resources in the system over a time period of 50 seconds when input of Malonyl CoA is 3x other resources

The results of the initial characterisation of the system show that CHS has the greatest control of flux through the pathway due to the high demand on malonyl CoA which is a limiting resource. This bottle neck could be removed by two approaches. Additional parts could be added to the operon to up regulate malonyl CoA production. This would increase the metabolic load and resource drain on the chassis organism potentially impacting on its ability to completely colonise. Another approach would be to downregulate pCoumaroyl CoA production by altering expression of 4CL and TAL. Only trace amounts of naringenin are required for chemotaxis this option is more suitable for our system.

To characterise performance and identify regulatory elements within the design, scans of the model were performed where 100 simulations were ran for each species; each time increasing an individual species count by 5 molecules from 0 to 500. Other species amounts were kept at a constant of 100 molecules. The effect of varying each species on conversion of naringenin to L-tyrosine (d[Naringenin] / d[L-tyrosine]) was plotted to outline performance sensitivity for each species on overall output. Through these scans parts of the system most likely to cause stability issues in a dynamic environment can be identified. Further evidence of bottlenecks within the system is also provided. With this information we conclusions can be drawn on how the design may be improved by changing parts.

TAL, CHS and 4CL did not have a steady gradual effect on d[Naringenin] / d[L-tyrosine], therefore at low concentrations, behaviour may not be consistent and predictable in the dynamic environment for which this system is being built (Figures 3A, 3B and 3C) . TALs sensitivity fluctuates as its amount increases from 0 to 40. Similar behaviour is seen when molecule counts are greater than 10 for 4CL. Production plateaued at around 50 molecules for 4CL and CHI and at 150 molecules for TAL and CHS. TAL and CHS therefore have a greater regulatory effect on naringenin production.

Figure 3A. Change in d[Naringenin]/d[L-tyrosine] as the number of 4CL molecules is increased from 0 to 500 in 100 steps when all other input molecules are set to 100.

Figure 3B. Change in d[Naringenin]/d[L-tyrosine] as the number of TAL molecules is increased from 0 to 500 in 100 steps when all other input molecules are set to 100.

Figure 3C. Change in d[Naringenin]/d[L-tyrosine] as the number of CHS molecules is increased from 0 to 500 in 100 steps when all other input molecules are set to 100.

Figure 3D. Change in d[Naringenin]/d[L-tyrosine] as the number of CHI molecules is increased from 0 to 500 in 100 steps when all other input molecules are set to 100.

The demand of the system on ATP, CoA and MCoA varies across all three resources. CoA is only rate limiting up to about 45 molecules whereas ATP and MCoA are limiting up to 100 and 375 respectively (Figure 3E). This is because the metabolite produced using CoA produces 3 x CoA as waste upon catalysis with CHS. The net change is therefore + 2 so as long as there is sufficient CoA to begin with, the systems waste will satisfy its demand. ATPs sensitivity fluctuates up and down till its plateau at approximately 100 molecules (Figure 3F). The fluctuations may be the result of ATP increasing the rate of conversion of pCoumaric acid to pCoumaroyl CoA such that CoA is used quicker than initially produced so waste does not satisfy demand. Malonyl CoA is the most rate limiting resource as the demand per unit time is three times than any other resources (Figure 3G). This can be seen with its plateau being approximately 3 times that of ATP. Malonyl-CoA also has by far the greatest effect on production, suggesting it is the most rate limiting resource.

Figure 3E. Change in d[Naringenin]/d[L-tyrosine] as the number of CoA molecules is increased from 0 to 500 in 100 steps when all other input molecules are set to 100.

Figure 3F. Change in d[Naringenin]/d[L-tyrosine] as the number of ATP molecules is increased from 0 to 500 in 100 steps when all other input molecules are set to 100.

Figure 3G. Change in d[Naringenin]/d[L-tyrosine] as the number of Malonyl CoA molecules is increased from 0 to 500 in 100 steps when all other input molecules are set to 100.

The sensitivity scans found that MCoA had the largest overall effect on d[Naringenin] / d[L-tyrosine], with there being approximately a 25 fold difference on overall d[Naringenin] / d[L-tyrosine] between minimal and maximal starting concentrations of MCoA (Figure 3G).

As Malonyl CoA can vary d[Naringenin] / d[L-tyrosine] drastically due to its relative demand, the effects on the system whereby Malonyl CoA wasn’t limited was studied. It was hoped that this would stabilize phase transient fluctuations to make the system more predictable and robust at low molecular counts seeing as our system requirements are to have a steady and continuous output of Naringenin.

Increasing Malonyl CoA relative to the enzymes did what was expected in the sense that transient phase fluctuations for the enzymes were no longer present (Figures 2.B). What’s more TAL becomes the most rate limiting enzyme under these conditions and could be used to adjust Naringenin output without comprising stability. This could be done by adjusting regulatory parts preceding the TAL coding sequence. Removing the Malonyl CoA bottle neck changed the range of effect on d[Naringenin] / d[L-tyrosine] such that minimal and maximal sensitivity values were 0 to 20-24 for all instead of 0 to 1-2.5 (Figures 3 and 4).

Figure 4A. Change in d[Naringenin]/d[L-tyrosine] as the number of TAL molecules is increased from 0 to 500 in 100 steps when number Malonyl CoA molecules is set to 300 and all other input molecules are set to 100.

Figure 4B. Change in d[Naringenin]/d[L-tyrosine] as the number of 4CL molecules is increased from 0 to 500 in 100 steps when number Malonyl CoA molecules is set to 300 and all other input molecules are set to 100.

Figure 4C. Change in d[Naringenin]/d[L-tyrosine] as the number of CHS molecules is increased from 0 to 500 in 100 steps when number Malonyl CoA molecules is set to 300 and all other input molecules are set to 100.

Figure 4D. Change in d[Naringenin]/d[L-tyrosine] as the number of CHI molecules is increased from 0 to 500 in 100 steps when number Malonyl CoA molecules is set to 300 and all other input molecules are set to 100.

ATP fluctuations were removed such that increasing the supply of ATP gradually increased d[Naringenin] / d[L-tyrosine] till the plateau. Alterations in should affect d[Naringenin] / d[L-tyrosine] more linearly therefore making the behavior of the system more predictable and robust.

Figure 4E. Change in d[Naringenin]/d[L-tyrosine] as the number of CoA molecules is increased from 0 to 500 in 100 steps when number Malonyl CoA molecules is set to 300 and all other input molecules are set to 100.

Figure 4F. Change in d[Naringenin]/d[L-tyrosine] as the number of ATP molecules is increased from 0 to 500 in 100 steps when number Malonyl CoA molecules is set to 300 and all other input molecules are set to 100.

For the system to be robust and predictable, the most sensitive regulatory elements of the system need be relatively stable in a dynamic environment. Increasing the supply of MCoA to three times that of other species remived transient phase perturbations on d[Naringenin] / d[L-tyrosine] making system behaviour more robust and predictable. Removing the MCoA bottle neck increased the range of effect of individual enzyme expression on d[Naringenin] / d[L-tyrosine] allowing more intricate tuning with respect to making the system stable or broadening its range of function. To implement this, we plan to under-express TAL and 4CL in turn making Malonyl CoA no longer limiting.

To model the effects of changing device design (i.e. promotor and RBS components), a transcription and translation system was built. This system was used to model the effects of decreasing promotor strength for TAL and 4CL. The transcription rate of TAL and 4CL was reduced by a factor of ten compared to CHS and CHI and the effects on Malonyl CoA demand and system stability were analysed.

Figures 5A and 5B show that decreasing the transcription rate of TAL and 4CL relative to CHS and CHI resulted in a decrease in abundance of these enzymes. Figures 5C and 5D show how a reduction in enzyme abundance due to lowering polymerase recruitment via a weaker promoter affects Malonyl CoA demand. When TAL and 4CL expression is reduced, Malonyl CoA supply is used up more gradually (Figure 5D) compared to when all enzymes have the same promoter strength (Figure 5C).

Figure 5A. Accumulation of enzymes in system over time when all enzymes are expressed equally.

Figure 5B. Accumulation of enzymes in the system over time when expression of TAL and 4CL is reduced by a factor of 10 compared to CHS and CHI.

Figure 5C. Rate of consumption of Malonyl CoA compared to accumulation of soil naringenin when all enzymes are expressed equally.

Figure 5D. Rate of consumption of Malonyl CoA compared to accumulation of soil naringenin when expression of TAL and 4CL is reduced by a factor of 10 compared to CHS and CHI.

The drawn-out demand that results from reducing the expression of TAL and 4CL, relative to CHI and CHS, also reduces transient phase perturbations for CHI and CHS (Figure 5E). In a dynamic environment, this will increase how stable and robust the defined output range is.

Figure 5E. Accumulation of enzymes in the system over time when all enzymes are expressed equally (i) and when expression of TAL and 4CL is reduced by a factor of 10 compared to CHS and CHI (ii).

The original objective when creating a synthesis pathway model was to improve on the design by team Darmstadt 2014 in order to increase flux through the pathway. The Darmstadt design consisted of all four enzyme coding sequences under the control of a single constitutive promoter (Figure 6).

Figure 6. Naringenin synthesis operon BBa_K1497016 designed by team Darmstadt 2014.

The new design is based on the results of the model that identified conversion of malonyl CoA and p-coumaric acid to p-coumaroyl CoA by CHS as the major rate limiting step in the pathway. Rather than introduce new coding sequences to up-regulate malonyl CoA production the design down-regulates p-coumaric acid production. The promoters in the new operon design were sourced from literature examining promoter strength in Pseudomonas putida (1). The promoter selected for TAL and 4CL induces expression weaker than the promoter for CHS and CHI by approximately ten-fold. The team has examined measurement of promoter strength as part of our project (Measurement Page). Information from these new methods of measurement and automated transformation can be deployed when characterising promoters in our endophyte chassis, which can be taken into account when finalising the model and construct design for deployment in our Pseudomonas sp. chassis.

Figure 7. Novel naringenin synthesis operon designed for optimum pathway flux.