Team:Consort Alberta/project


Our Project

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Our Project

   Introduction    Science   Comparison 


Mathematical Modelling    Prototyping





     ECOS (Environmental COntaminant Sensor) is a biological sensor theorized to detect the presence of aromatic hydrocarbons by producing an indicator protein when exposed to xylene. We chose xylene as our trigger because it is very well correlated with the presence of other more dangerous and carcinogenic compounds such as benzene and its derivatives. The danger of this class of compounds is in the fact that they can intercalate into DNA and cause a variety of mutations. Our biobrick will, in effect, create different amounts of fluorescent protein in the presence of xylene bound to XylR- which the first portion of our plasmid will constantly produce. As the economy in our rural community is based largely on agriculture and oil and gas industries, oil spills have detrimental effects on the environment, economy, and general health. To be able to detect the presence of these aromatic hydrocarbons on site would greatly benefit all members of our community. This year we will be testing out three different indicators to allow options concerning scale and intensity of colour change. In future years, our project will allow early identification of contamination will facilitate rapid clean-up and minimize health risks to members of our community and to the consumers who rely on the food we produce.

The Science...

    Our resulting biobrick consists of two main portions. The first portion of our plasmid will be responsible for producing the protein XylR. This is essential because the bonding of m-xylene undergoes a conformational change when bonded to XylR which then allows it to bind to, and positively regulate, the Pu promoter. At a 4:2 XylR to Xylene ratio the bonding starts the production of our reporter protein. The second portion of our plasmid expresses the indicator protein when in the presence of bonded XylR and m-xylene and essentially allow us to know if m-xylene is present in our soil sample or not. The structure of this biobrick is shown below.


  1. J23100 - which is a constituent promoter.
  2. B0034 - the RBS for our XylR gene.
  3. I723017 - the XylR coding region which encodes for the transcriptional regulator XylR protein.
  4. B0015 - the double stop codon for this sequence.
  5. I723020 - This is the Pu promoter.
  6. B0030 - RBS.1 strong.
  7. Reporter:
    1. E0040 - GFP wild-type, will glow when exposed to UV light.
    2. K592009 - amilCP, This chromoprotein from the coral Acropora millepora, naturally exhibits strong color when expressed.
    3. I732005 - lacZ, Beta-galactosidase cleaves X-gal and ONPG into colorful products.
  8. B0015 - lastly another double stop codon.
  9. pSB1C3 - Our plasmid backbone which includes the resistance towards the antibiotic, chloramphenicol.


     Where the comparison portion comes into play is the second part, which will be chosen from any one of three indicators: amilCP which when produced expresses a strong blue color, GFP when under a UV light source fluoresces green or, LacZ which when a substrate is added it cleaves it into strongly colored blue products. We will be looking at which indicator functions with the most efficiency and produces results of the highest clarity.

Mathematical Modelling...

     Making a sensor involves more than just the creation of the basic biological circuit, as there could be many factors that might improve the performance of the system. An example of these could be the strength of the RBS, the spacing between the promoter and the reporter gene, or the copy number of the plasmid used to house the circuit. While we couldn't nearly address all of these points in our project, we did want to look into the optimization of our system. To do this, we turned to mathematical modelling to create a visual representation of our system.

     We worked off of a basic framework published by Koutinas, M., et al.,[1,2] which was modelling the expression of the Ps promoter with XylR induction. The Ps promoter is the natural promoter found with the xylR gene, and the XylR protein can interact with both it and the Pu promoter we used in our project. Due to similarities between the Ps and Pu promoters, we assumed the deactivation rate of the two components were alike. For remaining values, we simply replaced the values of the Ps promoter with known values of the Pu promoter, keeping constants reported for XylR the same. In the end, we created five equations to represent the action of our system. Our model shows our biobrick producing the protein XylR, binding with xylene and the relationship to the output of our respective proteins. Each of our formulas are constructed as a rate in which a concentration or output is given of a specific substance in respect to time- otherwise known as a derivative. We will now expand upon the equations we modified and used.

      The first equation above represents the transcription of DNA into the XylR RNA transcript. RNA is the representation of the transcript, with P representing the number of plasmids inside a cell. J23100TC represents the activity of our promoter controlling XylR. ta is the transcription rate of the xylR into RNA, based on the transcription rate of E. Coli and the size of XylR, and KRNAdeg represents the degradation rate in the E. Coli.

      The second and third equations represent the inactive and active forms of XylR- or the concentration of XylR that has not bonded to xylene (XylRi) and the concentration of XylR that has bonded to xylene (XylRa). RNA is the substitution of our first equation. tr is the translation rate of our RNA into XylR. rXylR is the oligomerization constant of XylR, rR,XylR is the dissociation constant of active XylR, and xyl in the total concentration of xylene. αXylRi accounts for degradation of XylR.

      The fourth equation represents the concentration of xylene present. XylRi and XylRa refer to the concentrations of the inactive and active forms of XylR. rXylR is the oligomerization constant of XylR, rR,XylR is the dissociation constant of inactive XylR and xyl is the total concentration of xylene. αXylRi accounts for the degradation of XylRa, which would release the xylene it held. We multiplied by seconds per hour to get a domain more realistic to our needs.

      The fifth equation represents the output of the pu promoter. PuTC represents the activity of the Pu promoter or the output of protein. αpu is the deactivation rate of the Pu promoter. KXylRa is the activation coefficients of the Pu due to binding and nps,a is the hill coefficient for the interaction. β0 and βPS are the basal and maximal expression, respectively, of the Pu promoter.

      In order to create an output, we used the program Scilab. By inputting these equations into code we were able to have the program calculate our estimates and graph the results. In the end our output was around 5.5 mPoPS. This is a logical output and tells us our biobrick will successfully over express the protein. We set the output of our model in PoPS due to our system's flexibility when it comes to our reporter protein. This allows us to quickly add in an extra equation to turn our current output into fluorescence, LacZ output, or pigment production without having to worry about editing other parts of our model. Below is a copy of our code that we had used in our program. The resulting graph is the output of our Pu promoter.

     While we have yet to apply our modelling framework to the optimization of our sensor, we have created the foundation of a model that can be easily tweaked to test the effect of different variables on sensor performance. Through this we aim to do a sensitivity analysis of the various factors involved in our model in the future so that we can predict what to change for version 2 of our sensor. From there we can return to our model, feeding new data into it to create many future versions of our system, improving performance each time.


      ECOS was meant to be more than just a theory; more than just a project to play out at school- we wanted ECOS to have real world applications by the time we finished making our construct. We had one major problem: How were we going to test our product in an immediate and effective manner? To answer this we designed two prototypes to design and test. Unfortunately we were unable to finish the wet-lab portion of this project, but the designing is complete thanks to the Maya program via Autodesk. These prototypes are beneficial because they 1) are cheap to build and 2) relatively quick to test. Here are how they turned out:


      PROTOTYPE #1: We designed our first prototype so that it has a positive pressure source which creates a current for our xylene to travel from our sample, which is in a heated container so that the xylene is vapourized, to our ECOS container in which we bubble it through the E. Coli culture to get results. This was a plausible idea, as it would require fairly non-expensive materials and could be maintained by the average business person. However, we thought it could be more efficient in it's design, and so we kept looking for ideas. Our biggest problem with this was the fact that it is a relatively complex prototype. So we challenged ourselves to try and create a simpler prototype.


      PROTOTYPE #2: Another solution we theorized was to have our ECOS impregnated into alginate and then made into small spheres so that we can simply add them into a heated sample to get our results. In a container, we combine our soil sample and our alginate beads, mix them around, and then sift out the beads. The beads produce our indicator protein from the contamination levels of the soil. This design has been inspired by the Peking 2013 Team who used alginate encapsulation beads. Their reasoning and justification for using alginate beads stood out to us, as they stated that it was stable and inexpensive and easy to shape and manipulate. Upon further research into the use of these beads, we discovered that this prototype could be more efficient to use than our first prototype that required a few different pieces to the system. This revised prototype would also be easier for the average oil worker to use compared to the larger prototype.


     As time progresses with our project we plan to test and find what concentrations of m-Xylene trigger what kind of report. To do this we have purchased a couple containers of Xylene, each with a specified concentration, which we will apply to our prototypes. In-lab trials, in combination with our mathemantical model, will allow us to predict what [Xylene] is around and therefore what level of Benzene and Benzene derivatives are around as well. Once we manage to find this correlation between reporter and [Xylene] then we will be moving the project out to attempt some real-world application. Depending on our results here, ECOS may move away from rural Consort Alberta and out accross Canada and potentially the world. Of course there will always be things to fix- expecially when it comes to synthetic biology, so just like a computer programer we would try to fine tune and bring out newer version of this construct. The changes, of course, would come from our observations in-lab and customer suggestions as time progressed.


  1. The regulatory logic of m-xylene biodegradation by Pseudomonas putida mt-2 exposed by dynamic modelling of the principal node Ps/Pr of the TOL plasmid
  2. Calgary 2012 ECHEM Values, 
  3. Wiley Value References