Tracing the route to get to work


The objective of the experiment is to characterize and validate the semi-quantitative and quantitative response of our biosensor. The proposed experimental design consists of 5 phases.

The quantitative validation of the biosensor will be carried out by comparing interlaboratory results. [1] The results reported by the biosensor will be compared to those of a traditional method: "Atomic absorption spectrometry with hydride generation". [2] In other words, the environmental samples will be analyzed in parallel.

Other details for the sampling process

Thanks to previous studies, carried out by Bolivian scientists in 2014 and 2019, we identified 18 contaminated wells with arsenic in the municipalities of Cercado and Colcapirhua in the city of Cochabamba, designated as Base Territorial Organizations (TBOs). In these studies, the arsenic concentrations (ppb) in each well were determined by atomic absorption. [3][4] For our project, we selected six of the eighteen wells considering the concentration interval established for the operation of our biosensor (0 - 100 ppb As).

Table 1. Arsenic concentrations of the selected wells. 2019 updated data. Provided by the scientist Ph.D Ramiro Escalera.

Figure 1. Geographical location of the selected wells.

Each well sample will be subjected to different determinations:
  • Total arsenic content : Universidad Privada Boliviana, Cochabamba, Bolivia.

  • Physicochemical parameters : C.A.S.A facilities. Cochabamba, Bolivia.

  • Determination of total arsenic content by biosensor: Universidad Franz Tamayo Cochabamba, Bolivia.

Well water sampling will follow the protocol elaborated by our team.


COVID-19 presented a challenge to our team by limiting our access to facilities, reagents, and sampling campaigns. For this reason we were not able to materialize the biosensor yet. However, with the help of the gathered literature we next describe the expected results for each of the 5 phases of our experimental design with their corresponding differences with the biosensor designed by Wang et al. [3]

I. Arsenic toxicity and fitness cost of genetic constructs over bacterial chassis

We expect that the toxicity exerted on the biosensor will be minimal, since the concentrations we plan to use are several orders of magnitude lower than the reported concentrations as toxic to the chassis. Nadra et al. tested a biosensor that uses chromoproteins in concentrations of arsenic as high as 1000 ppb withoutreporting a significant toxicity [1], the same way, Zhuang et al. reported that the bacterial chassis started to be affected by toxic effects of arsenic on concentrations above 1874 ppb. [2]

Figure 1. Arsenic toxicity over bacterial chassis development.

We expect that in our design the As0 and As2 constructs will present a higher metabolic load for the bacteria in comparison with As4 and As5 The metabolic load that genetic constructs will have is closely related to their complexity and to the length of the amplification cascade. Wang et al. noticed a high metabolic load when the cascade was composed of three amplifiers. In contrast, by reducing the use of one amplifier, the construct will represent a lower load. It was also stated that a certain combination of amplifiers (i.e HrpRS-RinA) will result in lower metabolic effort for the microorganism. [3]

Figure 2. Metabolic load of our genetic constructs over bacterial chassis represented as a OD600 decrease.

II. Characterization of the biosensor's semi-quantitative response

The theoretical limit of detection (LOD) of the constructs are ≥ 0.5 ppb for As0, ≥ 3 ppb for As2, ≥ 10 ppb for As4 and ≥ 50 ppb for As5. At this point and without the use of any instrument, the experiments will give us semi- quantitative results. we expect that the reported LODs will be considerably higher than the theoretical ones, and the data collected will be helpful for later tests. We also expect that the background noise levels to be higher than those reported by Wang et al. since our design does not contain additional ArsR binding sites (ABS) for mitigation [3]. To inmobilize microorganisms, we will perform the lyophilization of bacteria on paper strips along protective solutions that will ensure the viability up to 2 months after processing, we expect minimal loss of their sentitivity to arsenic under the appropriate storage conditions. [4]

Figure 3. Paper strip with the 4 biosensors lyophilized over the surface generating traffic light patterns after arsenic exposure.

III. Characterization of the biosensor's quantitative response

At this stage, we will work with the constructs that present an optimal response at concentrations ≥ 5 - 20 ppb of arsenic, being As2 and As4 possible candidates that fulfills this criterion. As mentioned before, we expect that the background noise will be considerably high in comparison to constructs without amplifiers, especially those with transcriptional amplifiers due to the absence of additional ABS sites [3], As2 and As4 output is expected to be linearly correlated to arsenic concentration within the range of 5 - 100 ppb. We expect the sensitivity at this point will be lower than the one that traditional methods present, but the repeatability is yet to be confirmed by the generation of calibration curves.

Figure 4. Expected calibration curve for As2/As4 construct response

IV. Biosensor quantitative response validation

In the attempt to test the specificityto arsenic of the respons, we will expose the biosensor to three different metals (Fe, Hg, Mg) looking for any type of non-specific response. We expect no response above the background noise against these 3 metals. [3] The only reported case of non-specific results in this type of biosensor is caused by antimony that could generate responses even higher than arsenic itself depending on the design employed. [5]

Figure 5. Specific arsenic response, reporter induction by other metals is negligible.

V. Biosensor response with real samples

Initially, we expect that the concentrations reported by the biosensor will be lower than those reported by spectrometric methods because our biosensor points to bioavailable arsenic in contrast with traditional methods that detect total arsenic.[6] But bioavailability of arsenic depends on several intrinsic physicochemical factors of the sample and the culture medium resulting in possible biased results related to the nature of the matrix. For instance, we expect the bioavailability to be lower in samples with high concentration of dissolved organic matter. Similarly, if the phosphate concentration is high, the uptake of As(V) will also be significantly reduced [7] , which would result in a considerably lower reported concentration than the spectrometric methods.

On the other hand, an advantage that our biosensor has is the speciation of arsenic in drinking water collected from wells. This water contains mainly As (III) because of the reduced contact of the well with oxygen in contrast to surface waters, whole-cell biosensors are especially sensitive to this species because it doesn't need an extra reduction step. [7]

VI. Tackling the background noise problem:

As mentioned before, one of the principal expected drawbacks is the background noise (leakage) of our biosensor, especially in constructs with transcriptional amplifiers. We propose 2 potential solutions:

  • Adding an extra ABS sequence into the inducible promoter pArs: using specially designed primers in a PCR and considering that the ABS sequences are relatively short (24 bp).

  • Coupling with a degradation tag-TEV protease system: which is a special approach that takes the advantage of natural protein degradation systems of the bacterial chassis. A short TEV cleavage site and a degradation tag is added at the end of the reporter (mRFPViolet) coding sequence. Additionally, an arsenic responsive construct, responsible for the TEV protease expression, is coupled. Without arsenic in the medium the reporter is immediately degraded by bacterial proteases because it still has the degradation tag lowering the background noise. With arsenic in the medium, the TEV protease is expressed and cuts the degradation tag allowing normal expression of the reporter.


Experimental Design

[1] B. Magnusson and U. Örnemark (eds.) (2016) Eurachem Guide: The Fitness for Purpose of Analytical Methods – A Laboratory Guide to Method Validation and Related Topics, (2nd ed. 2014). ISBN 978-91-87461-59-0. Available from

[2] Morand, E.; Giménez, M.; Benitez, M. & Garro, O. (2021). Determinación de arsénico en agua por espectrometría de absorción atómica con generación de hidruro (HG-AAS).

[3] Escalera, R, & Ormachea, M. (2017). Hidroquímica de la presencia natural de arsénico en aguas subterráneas de Cochabamba-Bolivia y evaluación de la viabilidad técnica de procesos de remoción. Investigación & Desarrollo, 1(17), 27-41. Recuperado en 15 de septiembre de 2021, de.

[4] Escalera, R.; Ormachea, M.;, Ormachea, O. & Heredia, M. (2014). Presencia de arsénico en aguas de pozos profundos y su remoción usando un prototipo piloto basado en colectores solares de bajo costo. Investigación & Desarrollo, 2(14), 83-91. Recuperado en 15 de septiembre de 2021, de

Expected Results

[1] Barone, F., Dorr, F., Marasco, L. E., Mildiner, S., Patop, I. L., Sosa, S., Vattino, L. G., Vignale, F. A., Altszyler Lemcovich, E. J., Basanta, B., Carlotto, N., Gasulla, J., Giménez, M., Grande, A. V., Nieto Moreno, N., Bonomi, H. R., & Nadra, A. D. (2017). Design and evaluation of an incoherent feed-forward loop for an arsenic biosensor based on standard iGEM parts.

[2] Barone, F., Dorr, F., Marasco, L. E., Mildiner, S., Patop, I. L., Sosa, S., Vattino, L. G., Vignale, F. A., Altszyler Lemcovich, E. J., Basanta, B., Carlotto, N., Gasulla, J., Giménez, M., Grande, A. V., Nieto Moreno, N., Bonomi, H. R., & Nadra, A. D. (2017). Design and evaluation of an incoherent feed-forward loop for an arsenic biosensor based on standard iGEM parts.

[3] Hu, Q., Li, L., Wang, Y., Zhao, W., Qi, H., & Zhuang, G. (2010). Construction of WCB- 11: A novel phiYFP arsenic-resistant whole-cell biosensor. Journal of Environmental Sciences, 22(9), 1469-1474.

[4] Wan, X., Volpetti, F., Petrova, E., French, C., Maerkl, S. J., & Wang, B. (2019). Cascaded amplifying circuits enable ultrasensitive cellular sensors for toxic metals. Nature Chemical Biology, 15(5), 540-548.

[5] Stocker, J., Balluch, D., Gsell, M., Harms, H., Feliciano, J., Daunert, S., Malik, K. A., & van der Meer, J. R. (2003). Development of a Set of Simple Bacterial Biosensors for Quantitative and Rapid Measurements of Arsenite and Arsenate in Potable Water. Environmental Science & Technology, 37(20), 4743-4750.

[6] Kaur, H., Kumar, R., Babu, J. N., & Mittal, S. (2015). Advances in arsenic biosensor development – A comprehensive review. Biosensors and Bioelectronics, 63, 533-545.

[7] Pothier, M. P., Hinz, A. J., & Poulain, A. J. (2018). Insights Into Arsenite and Arsenate Uptake Pathways Using a Whole Cell Biosensor. Frontiers in Microbiology, 9.