iGEM 2019 — Team UPNAvarra_Spain

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# Team:UPNAvarra Spain/Notebook

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Notebook

---

July

![](https://static.igem.org/mediawiki/2019/b/b8/T--UPNAvarra_Spain--T_fondonotebook1.jpeg)

Week 1 : 1st ⇾ 7th

In the beginning, we received the theorical and practical training required to develop our project and to get on well in the laboratory. We also made the most of that week to become familiar with the lab equipment. We also prepared culture media and started working with different plasmids.

[Read More](https://2019.igem.org/Team%3AUPNAvarra_Spain/Notebook/Week1)

![](https://static.igem.org/mediawiki/2019/1/19/T--UPNAvarra_Spain--T_fondonotebook2.jpeg)

Week 2 : 8th ⇾ 14th

Due to the celebration of the San Fermin festival in Pamplona, our lab was closed for holidays. Therefore, we spent this week getting strength to continue with our project.

![](https://static.igem.org/mediawiki/2019/5/5f/T--UPNAvarra_Spain--T_fondonotebook3.jpeg)

Week 3 : 15th ⇾ 21th

We came back to our daily routine. This time we focused our efforts on digestions, ligations and colony PCR. After several attempts we finally obtained a significant and different progress for each construction.

[Read More](https://2019.igem.org/Team%3AUPNAvarra_Spain/Notebook/Week3)

![](https://static.igem.org/mediawiki/2019/6/6b/T--UPNAvarra_Spain--T_fondonotebook4.jpeg)

Week 4 : 22th ⇾ 28th

Once to this point, each construction was at a different stage. By the end of the week the nitrate constructions were nearly finished. However, the remaining constructions were at different points in the cloning process.

[Read More](https://2019.igem.org/Team%3AUPNAvarra_Spain/Notebook/Week4)

---

August

![](https://static.igem.org/mediawiki/2019/0/0a/T--UPNAvarra_Spain--T_fondonotebook5.jpeg)
![]()

Week 5 : 29th ⇾ 4th

It was our jinx week, when nothing seemed to work. But we finally found our mistakes and we were able to deal with the problem. At the end we had some positive results and the work carried on.

[Read More](https://2019.igem.org/Team%3AUPNAvarra_Spain/Notebook/Week5)

![](https://static.igem.org/mediawiki/2019/4/42/T--UPNAvarra_Spain--T_fondonotebook6.jpeg)

Week 6 : 5th ⇾ 11th

During this week we follow cloning our constructions and we started working on the induction of bacterial cultures.

[Read More](https://2019.igem.org/Team%3AUPNAvarra_Spain/Notebook/Week6)

![](https://static.igem.org/mediawiki/2019/4/43/T--UPNAvarra_Spain--T_fondonotebook7.jpeg)

Week 7 : 12th ⇾ 18th

We finished some of our constructions and we sent them to sequencing. With those results we could confirm that some fragments corresponding to RBS were missing in the composite construction, so we design a strategy to solve this problem and finish the constructions on time.

[Read More](https://2019.igem.org/Team%3AUPNAvarra_Spain/Notebook/Week7)

![](https://static.igem.org/mediawiki/2019/4/43/T--UPNAvarra_Spain--T_fondonotebook8.jpeg)

Week 8 : 19th ⇾ 26th

This week we received some parts kindly provided by Twist Bioscience. We want to thanked Twist for this contribution to our project. We used these fragments to obtain final composite parts which will make up our biosensor collection. At the same time, we performed some inductions to start characterizing our biosensors and tested their ability to detect nitrate, cooper, mercury or cadmium by the expression of different chromoproteins.

[Read More](https://2019.igem.org/Team%3AUPNAvarra_Spain/Notebook/Week8)

![](
https://static.igem.org/mediawiki/2019/9/9c/T--UPNAvarra_Spain--T_fondonotebook9.jpeg)

Week 9 : 27th ⇾ 31th

We spent the last lab week repeating inductions to adjust the assayed concentrations and induction times, and also taking pictures that would be used in the following modeling process. In addition, we have dedicated one day to pick up water samples from river basins and agro-industrial activities to validate our theorical model.

[Read More](https://2019.igem.org/Team%3AUPNAvarra_Spain/Notebook/Week9)

## Contact us

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# Team:UPNAvarra Spain/Experiments

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Experiments

---

Index

---

[1.Cloning](#1)
[2.Inductions](#2)

1.Cloning

To develop our project, we designed a series of plasmids (see design section) with the aim to detect the presence of nitrate, cooper, mercury and cadmium in water samples.

In order to achieve our goal, we have used the BioBrick assembly method. This approach is based on specific restriction enzyme assembly. Different DNA fragments (promoters, RBS, coding sequences…) have been standardized and called as “parts” adding identical flanking restriction sites that are not contained within the sequence (known as prefix and suffix sequences). In this way, parts can be combined together and the original flanking sites re-used for the next round of assembly and finally create new BioBricks with novel properties

![](https://static.igem.org/mediawiki/2019/b/b8/T--UPNAvarra_Spain--ExperimentsFigura2.jpg)

In general, our objective is to create composite parts with the following structure: Prefix-Promoter-RBS-CDS-Terminator-Suffix. The basic parts we used were coming directly from the iGEM distribution kit or were obtained by PCR amplification form different composite parts also present in the kit. As an example, we will explain the process that we followed to obtain the biosensor capable of detecting nitrate, considering that it was similar for the other plasmids.

![](https://static.igem.org/mediawiki/2019/d/d3/T--UPNAvarra_Spain--ExperimentsFigura1.jpg)

We start with the digestion of the plasmid containing the nitrate sensitive promoter with EcoRI-SpeI restriction enzymes. At the same time, the plasmid containing the RBS (BBa\_B0030) is linearized by EcoRI-XbaI enzymes. During ligation, the promoter is inserted into the plasmid containing the RBS, according to the complementarity of the enzymes: EcoRI-EcoRI and SpeI-XbaI. In the next step the new construction is digested with EcoRI-SpeI and ligate into the plasmid containing the blue chromoprotein digested with EcoRI-XbaI. The last step is the integration of the terminator (BBa\_B0015) downstream of the chromoprotein, repeating the same process of digestion and ligation. The plasmid used as backbone in all cases was pSB1C3, carrying chloranphenicol resistance. The general workflow is explained in the following video (and detailed in the protocols section).

[

](https://static.igem.org/mediawiki/2019/5/51/T--UPNAvarra_Spain--T_video1.mp4)

In order to check that the process works, colony PCR and miniprep digestions were performed to make sure that the sequences had the correct number of base pairs in each case. At the end of the process, all constructions were confirmed by sequencing.

Finally, we obtained five different plasmids ready to be transformed on E. coli cells:

* NITRATE-amilCP blue (BBa\_K2817007, or our twin BBa\_K3287000)
* NITRATE-amilGFP yellow (BBa\_K3287001)
* COOPER-amilCP (BBa\_K3287002)
* MERCURY-tsPurple (BBa\_3287003)
* CADMIUM-efordRed (BBa\_K3287004)

2.Inductions

Once we finished the constructions, we tested their ability to detect nitrate, cooper, mercury or cadmium. We performed nearly the same experiments several times to adjust the assayed concentrations and induction times.

**1st Day.** Streak the bacteria from the glycerol stock onto an LB agar plate (with chloranfenicol). Incubate it 24 hours at 37ºC.

![](https://static.igem.org/mediawiki/2019/8/87/T--UPNAvarra_Spain--ExperimentsFigura4.jpg)

**2nd Day.** Preinoculum: pick a single colony from the plate to grow in liquid medium (3 mL of LB-chloranfenicol). Grow the bacteria 24 hours at 37ºC shaking.
**3rd Day.** Inoculum: add 1/50 of preinoculum to liquid medium (LB-chloramphenicol) and grow at 37°C with orbital agitation. Measure the optical density at 600 nm. When it reaches 0.4, we start the induction adding different compounds at different concentrations for each construction, as follows:

+ Potassium nitrate (KNO3): 0, 1, 2, 4, 8 and 16 mM
+ Copper chloride: 0, 5, 75, 250 and 500 mg/L
+ Mercury chloride (HgCl2): 0, 6, 30, 60, 120 and 240 µg/L
+ Cadmium chloride: 0, 0.01, 0.1, 1, 10 and 100 µM

![](https://static.igem.org/mediawiki/2019/a/a4/T--UPNAvarra_Spain--ExperimentsFigura5.png)

We leave the cultures growing under agitation for 6 hours (Nitrate and cooper) or 24 hours (Mercury and cadmium).

After those incubation times, we take 2 ml of each culture, centrifuge them, remove the supernatant and take pictures of the remaining pellets. The imaging process is extremely relevant for the conditions of the dataset used in the mathematical models. We decided to take two sets of pictures, so as to evaluate two different conditions. The first dataset was taken at the same daytime (hence, with similar daylight) on standardized conditions (camera setting, tripod, background, etc). Static lightning setup was discarded as unnecessary. This first dataset is used to prove the linear relationship between concentration and color. A secondary dataset was generated taking images at different daytimes, with different camera settings. With this dataset we intend to simulate (part or all of) the variability of images that will be gathered in a real-world application. Hence, it is used to prove that the concentration-color relationship observed in standardized images can also be observed in condition-variable images.

After that, once we proved that the variation in the color of the bacteria relates to the concentration of the heavy metal or nitrate by means of a mathematical model, we used the biosensors to test the water samples following the same protocol (instead of adding the specific substance at known concentrations, we add the sample x).

![](https://static.igem.org/mediawiki/2019/3/3a/T--UPNAvarra_Spain--ExperimentsFigura6.jpg)

## Contact us

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# Team:UPNAvarra Spain/Results

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Results

---

Wet Lab

For our first involvement in iGEM competition, we constructed new expression vectors, containing sensitive promoters, an RBS, different chromoproteins and a transcription terminator. We used different sequences acting as sensitive promoters to detect the presence of nitrate and heavy metals, like cooper, mercury and cadmium (Figure 1). Those plasmids, transformed in *E. coli* cells, make up our system of bacterial biosensors.

![](https://static.igem.org/mediawiki/2019/f/f7/T--UPNAvarra_Spain--Design.jpg)

*Figure 1. Construction of nitrate and heavy metal biosensors vectors.*

For each construction different inductions were performed in order to standardize and optimize the expression of the chromoproteins. As it can be seen in the pictures, we obtained a gradual rise in the expression of the chromoproteins according to the concentration of each contaminant (Figure 2).

![](https://static.igem.org/mediawiki/2019/a/a6/T--UPNAvarra_Spain--Results2.jpg)

*Figure 2. Chromoprotein expression levels under increasing concentrations of each contaminant.*

It has to be noted that the results of the cadmium sensor showed no differences at first sight between the different concentrations (Figure 2). This might be due to the CadA weak promoter strength or most likely, the need of other regulatory elements. So, for the usage of this sensor, it has to be optimized.

Mathematical Model

The mathematical model generated from the experimental results obtained in the lab might look like a classical regression model, but it is not. Normally, regression models are used to predict results from an a priori unknown set of data. Hence, the underlying assumption is that the data is differentiable (predictable), and the quest is to produce a mathematical model able to do it.

In the present experiments, our quest is rather the reverse. We intend to proof that the data produced in the experiments is in fact learnable. This made our focus not on making an as-complex-as-possible mathematical model, able to predict future data. Instead, we intend to prove that the (imaging) data we have gathered in the lab is in fact learnable by simple regression models.

This somehow non-standard idea has driven our experiments. We have, in the data analysis part of the process, attempted to learn the data we have gathered using a simple regression model. We have opted out by a standard Least-Square error (linear) regression model, which has been run on the dataset obtained in the imaging part. This dataset consists of the average RGB color in the colored part of the pellets used at different concentrations of each heavy metal or nitrates.

For each color, we have subselected the channels that we are interest for the problem. That is:

* Nitrate (blue): Red channel
* Nitrate (yellow): Blue channel
* Copper: Red and Green channels
* Mercury: Red and Green channels
* Cadmium: Red, Green and Blue channel

Hence, the problem shifts from 1D regression to 3D regression, depending on the material we are working with at each specific experiment. For each of the experiments, 5 or 6 samples have been used:

![](https://static.igem.org/mediawiki/2019/4/41/T--UPNAvarra_Spain--BronzeFigure2.png)

*Figure 3. Modeling a Nitrate biosensor. A) Input data; B) Regression model.*

![](https://static.igem.org/mediawiki/2019/1/19/T--UPNAvarra_Spain--Results4.jpg)

*Figure 4. Modeling a Nitrate biosensor. A) Input data; B) Regression model.*

![](https://static.igem.org/mediawiki/2019/f/fd/T--UPNAvarra_Spain--Results5.jpg)

*Figure 5. Modeling a Cooper biosensor. A) Input data; B) Regression model.*

![](https://static.igem.org/mediawiki/2019/1/11/T--UPNAvarra_Spain--Results6.jpg)

*Figure 6. Modeling a Mercury biosensor. A) Input data; B) Regression model.*

![](https://static.igem.org/mediawiki/2019/d/d4/T--UPNAvarra_Spain--Results7.jpg)

*Figure 7. Modeling a Cadmium biosensor. A) Input data; B) Regression model.*

It can be seen how the data is easily learnable by linear regression models in the case of the Nitrate (Figure 3 and 4). Also, the accuracy in learning the Copper (Figure 5) and Mercury (Figure 6) models is rather good (it is to be noted that the figure above is a 1D representation of a 2D models, hence not displaying the linearity of the model itself). The model for Cadmium (Figure 7), however, was not successful. As it can be seen from the images, the changes in the coloring w.r.t. the concentration are hardly perceivable. Note also that the concentration learn is the squared root of the actual concentrations, in order to better distribute the measures over the testing range.

As it can be seen from the models displayed above, the error in the model training is rather small. Specifically, the average error is the following:

| Contaminant | Avg. Error | Testing concentration range |
| --- | --- | --- |
| Nitrate (Blue) | 0.18 | [0,4] |
| Nitrate (Yellow) | 0.17 | [0,4] |
| Cooper | 1.88 | [0,22.36] |
| Mercury | 0.96 | [0,12] |
| Cadmium | 0.38 | [0,10] |

*Table 1. Error in the model training .*

We can observe that the data is hence learnable from both the perspective of the visual model (Figures 3-7) and from the results in the error measurement (Table 1). Even in the case of the Cadmium the error seems to be low, although that is misleading, since the data is clearly not learnable from the visual display in Figure 7.

Note that the data could be better fit if using other types of regression, specifically higher order regression. However, this would hamper our main point here, which is proving that the data is learnable by simple means.

Machine Learning Model

We have proved that data can be learnable by a simple linear regression model (OLS), but to obtain more accurate predicted values, we should train our model using more data to be more precise. These extra data are extracted from images taken from pellets induced by a known concentration of each specific substance.

We will consider every channel for each specific substance, since we don´t have a great amount of data and we consider that a dimensionality reduction is not necessary in order to reduce the computational complexity of our model. Nevertheless, in order to present a complete study, we will apply a dimensionality reduction (Principal Components Analysis) in the next section.

In order to test our model, we have applied a cross validation. We divided our data in *n* partitions, each one with 25% of the data. Our training data will be *n-1* partitions whereas one will be our *test data*. Every partition is selected once to be the *test data* so the training and testing is done 4 times.

For each specific substance we have collected data from 4 different samplings, under different imaging conditions, of the same concentration. Hence, the number of data is 24 for Nitrate (Blue), Nitrate (Yellow) and Mercury, with 6 different concentrations, and 20 for Cooper with 5 different concentrations:

![](https://static.igem.org/mediawiki/2019/8/87/T--UPNAvarra_Spain--ModelFigure2.png)

*Figure 8. Modeling a Nitrate biosensor.*

![](https://static.igem.org/mediawiki/2019/c/c5/T--UPNAvarra_Spain--ModelFigure3.png)

*Figure 9. Modeling a Nitrate biosensor.*

![](https://static.igem.org/mediawiki/2019/1/10/T--UPNAvarra_Spain--ModelFigure5.png)

*Figure 10. Modeling a Cooper biosensor.*

![](https://static.igem.org/mediawiki/2019/2/27/T--UPNAvarra_Spain--ModelFigure4.png)

*Figure 11. Modeling a Mercury biosensor.*

Applying our model the R2 (mean of the 4 samplings R2) and the average error (difference between the actual value and the predicted value) are the following:

| Contaminant | R2 | Avg. Error | Testing concentration range |
| --- | --- | --- | --- |
| Nitrate (Blue) | 0.930 | 0.289 | [0,4] |
| Nitrate (Yellow) | 0.949 | 0.236 | [0,4] |
| Cooper | 0.833 | 2.520 | [0,22.36] |
| Mercury | 0.760 | 2.137 | [0,15.5] |

*Table 2. Error in the model during cross validation.*

We can conclude that providing our model with more data, we could predict the specific substance concentration obtaining reliable results. In our case, the results in the error measurement (Table 2) are due to the lack of more concentration values uniformly distributed. Nevertheless, we demonstrated that the data is easily learnable by linear regression models (Figures 8-11), obtaining accurate predicted values.

Conclusion

As a general conclusion, we have made our point in illustrating how the data gathered in the experiment is learnable, hence the product (colored material) is sensitive to (and gradual w.r.t.) the concentration in water. This means that our biological system can be used to detect contamination in water, as well as to measure the severity of such contamination.