Team:NUS Singapore-A/shadow/Modelling

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OVERVIEW

Modelling was heavily utilised to obtain a better understanding of our systems, as well as, shaping our experimental designs which helped us save unnecessary wastage of resources and time. We constructed models that allowed us to achieve the following:

  1. Preliminary study of our intended biochemical pathway
  2. Optimal genetic circuit design
  3. Proof that optogenetics can work in our systems and improve upon existing inducible/repressible light systems
  4. Simulation and optimisation of our entire experimental process

Our MATLAB scripts can be found here.

Our team started with the intention to produce dyes consisting of the primary colours, red, yellow and blue, that can be easily mixed to create a plethora of colours for the textile industry. However, we have decided to produce namely, Chrysanthemin (red) and Luteolin (yellow) instead. This decision was due in part to our interview with Mr Holger Schlaefke (link to our interview here), Global Marketing Manager of DyStar Pte Ltd, that advised us to produce vibrant colours, and how numerous past iGEM teams have attempted to produce Indigo (a colour similar to blue).

PART 1: THE BIOCHEMICAL PATHWAY

Goal of model

Thus, Wet Lab needs to verify the feasibility of producing these two flavone compounds in E.Coli from the starting substrate, Naringenin, by considering only the biochemical reactions using mathematical modelling in-silico (refer to the biochemical pathway in Fig 1.). In addition to this, we explore how enzyme concentrations affect our system in hopes that it may aid us in our experimental design.

Fig. 1 Biochemical Pathway from starting substrate Naringenin

Considerations

Since there was a competing biochemical pathway which produces Callistephin – an undesired product – for the production pathway of Chrysanthemin, it had to be considered in our model as well.

Methods

Using the ODE15s solver on MATLAB, we solved for the following ordinary differential equations (ODEs) with respect to time. The following parameters were obtained from literature1 and the ODEs were derived from the mass balancing of the Michaelis-Menten equation2. Initial enzyme concentrations were set to high (in excess) amounts to create a simplified model for our initial study of our biochemical system.

The chemical and differential equations for the model are shown below:

Therefore, in order to obtain the maximal velocity, V_m, of the enzyme acting on a particular substrate, the concentration of the enzyme is multiplied by its turnover rate, kcat.

Table 1. Parameters for preliminary study model

Parameter Meaning Value Reference
[enzyme] Concentration of enzyme reacting with the substrate Predetermined parameter, but set to 10000nM for our initial NA
Eve Jackson 94
John Doe 80

As can be observed from Table 1 above, the concentration of all enzymes was manually altered to observe the following effects:

  1. How each individual enzyme concentration affect the conversion rate of Naringenin into its desired products
  2. Whether production of desired flavonoids (Chrysanthemin and Luteolin) were plausible

Luteolin production was studied independently from Callistephin’s and Chrysanthemin’s production pathway since there is no competition between them. Production of Callistephin and Chrysanthemin were studied simultaneously.

Assumptions of model

  • Negligible substrate degradation (these substrates refer to the starting compound Naringenin, as well as reaction intermediates such as Apigenin, Eriodictyol, Cyanidin etc.).
  • Negligible basal expression of enzymes.
  • No scarcity of bioproduction resources (nutrients, amino acids) and no other factors affecting production of enzymes (OD, cell stress, inhibition).

Findings

Fig. 2 Concentration (nM) of intermediates and final product (Luteolin) converted from Naringenin over time (hour)

Fig. 3 Concentration (nM) of intermediates and final products (Callistephin and Chrysanthemin) converted from Naringenin over time (hour)

The results prove that production of flavonoids was theoretically feasible in E.Coli, allowing for decent yield of Chrysanthemin and Luteolin. Assuming the starting amount of Naringenin substrate to be 20000nM, Fig. 2 illustrates the 100% conversion of Luteolin (yellow dye) in 5.3 hours, while Fig. 3 shows the production of ~11410nM of Chrysanthemin and ~2000nM of Callistephin in 8 hours. This indicates 57.05% Naringenin conversion at 8 hours, with 17.53% contamination in the final Chrysanthemin product. There is contamination because Callistephin is orange in colour and will alter the colouration of our desired red dye. This proves that there will be an unsatisfactory yield and purity of Chrysanthemin due to an existing competing pathway, pushing the flux towards Callistephin to be formed.

Alterations of the [enzyme] parameter in our script also show us that there could be an optimal enzyme-to-enzyme ratio for our system, as varying our enzyme concentrations allowed us to obtain vastly different yields of our final products.

Conclusions

Focusing on the production of Luteolin was the best way forward as flux control to obtain a pure Chrysanthemin product was too difficult and unfeasible due to time frame limitations. In future experiments, the Wet Lab team was also given recommendations to consider tightly controlling enzyme concentrations in order to achieve higher yield, which would influence future plasmid designs. Hence, optimisation of our genetic circuit would improve our Naringenin conversion rates.

PART 2: GETTING THE OPTIMAL GENETIC CIRCUIT

Goal

The genetic circuit should be an important design consideration as parts like the Ribosome Binding Site (RBS) affect the translation rates and thus, the concentrations of the enzymes, which would ultimately affect yield and conversion rates. Therefore, simulations to aid Wet Lab select RBS parts can help improve our system greatly.

The lab has two RBS parts available to them, namely rbsD and rbs34. However, two of the same RBS parts cannot be conjugated into our plasmids and thus, we have to decide which RBS we want to assign to FNS and F3’H.

The promoter site will be discussed in Part 3 as we plan to use light inducible and repressible promoters to reduce our need for chemical inducers.

Methods

Utilising characterisation data from a Synthetic Biology lab at E6 Engineering (NUS), we were able to compare relative strengths of rbs34 and rbsD by their ability to produce RFP. The final expression concentrations of varying inducer concentrations (of 0.002g/100ml, 0.008g/100ml, 0.031g/100ml and 0.125g/100ml) were compared between the two RBS systems and the average was taken for all inducer concentrations to obtain the relative strengths of the RBS systems. This is shown below:

Fig. 4 pBAD/rbsD RFP expression curve

Fig. 5 pBAD/rbs34 RFP expression curve

Table 2. Relative strengths of rbsD over rbs34

Inducer Concentrations Relative strength of rbsD:rbs34
0.002g/100ml 2.75
0.008g/100ml 2.3077
0.031g/100ml 1.9667
0.125g/100ml 1.3208
Average 2.086

Using the MATLAB script written above in Part 1 and the parameter values from Table 1, more constraints were added to our model by altering the [enzyme] parameter to simulate the different strengths of the RBS that we have determined in Table 2. This will allow us to determine which RBS system is the most suitable for FNS and F3’H respectively.

Assumptions of the model

  • Samples were subjected to identical conditions during the characterisation experiments of rbsD and rbs34.
  • Negligible substrate degradation (these substrates refer to the starting compound Naringenin, as well as reaction intermediates such as Apigenin, Eriodictyol, Cyanidin etc.)
  • Negligible basal expression of enzymes (=enzymes are not being produced by the cell without any induction or human input).
  • No scarcity of bioproduction resources (nutrients, amino acids) and no other factors affecting production of enzymes (OD, cell stress, inhibition).
  • Production rates (= translation rates) of Red Fluorescence Protein (RFP) is representative of the production rates of the enzymes, FNS and F3’H.

Findings

Fig. 6 Conversion of Naringenin to Luteolin (rbsD assigned to FNS and rbs34 assigned to F3’H)

Fig. 7 Conversion of Naringenin to Luteolin (rbsD assigned to F3’H and rbs34 assigned to FNS)

By comparison, it is observed that 100% naringenin conversion occurs at the 2.4 hour mark for the rbs34/FNS and rbsD/F3’H system while 100% conversion for the rbsD/FNS and rbs34/F3’H system occurs at 4.7 hours. It can also be observed from Fig. 7 that 100% conversion occurs faster in the rbsD/F3’H construct.

Conclusion

A recommendation was given to Wet Lab to construct a gene circuit for rbsD/F3’H and rbs34/FNS.

After this, a proper study of light inducible and repressible promoter systems needs to be conducted to test for the viability of the promoter parts.

PART 4: THE COMPLETE MODEL

Goal

The model aims to facilitate the experimental design constructed by the Wet Lab team using in-silico simulations. This model would also serve as a guide to troubleshoot experimental design flaws as it’s a representative model of our entire system.

Methods

PART 3: OPTOGENETICS AND PARTS IMPROVEMENT

So stuff should go right in here.

And more stuff on this side too.

Blah Blah Blah

Blargh Blargh Blargh



HEAR OUR STORY

We’re using synthetic biology to change the way the world thinks and functions.

We’re a little disruptive, dangerous and maybe a tad too bold. But the world needs some shaking up every now and then, and we think we’re just the right people for that.

Together we’re going to engineer biology and create something awesome.

CONTACT US

nusigem@gmail.com

21 Lower Kent Ridge Rd, Singapore 119077