On this page we describe the dry lab activities related to our project, such as the softwares we are using, a brief description about the modeling of our toehold switch structures, and the creation of our toehold switch library. Our dry lab module is closely connected to the design and modeling of our project, as well as the wet lab.


At the very core of our project design is the modeling of toehold switches. Our detection mechanism relies on the function of toehold switches, which control the expression of the reporter protein. The RNA secondary structure of the toehold switch prevents the protein expression. When a trigger sequence is presented into the system, the secondary structure changes and reveals the start codon, where the ribosome can now bind and initiate the translation of the reporter protein. More about modeling the toehold switches can be found on our Modeling page. On the same page there is also information on how and based on which parameters we chose our toehold switch structures.


We created a code for NUPACK (Wolfe et al, 2017; Wolfe et al, 2015; Zadeh et al, 2011; Dirks et al, 2004) to design toehold switches (the code can be found on our GitLab page). It is possible to input several sequences into the program at the same time as a csv file, in which the sequences are arranged in their own rows and numbered. The program divides each sequence into 36 nucleotide length subsequences. Furthermore, it runs 10 independent simulations for each subsequence creating the toehold switches. If NUPACK creates several identical toehold switches, the extra ones are removed. After the toeholds are created, the results are output into an excel file ranking the models based on a score assigned by our model. The bigger the score the better toehold switch we have.

More about the design of the toehold can be found from our Design page, and more about the methods, optimization and the scores of the toeholds can be found at our Modeling page).

Toehold switch library

To demonstrate further the modularity and the global relevance of our system, we created a toehold switch library containing toe hold switches designed to detect different pathogens. On this page we also describe the workflow of generating the toehold switch library.

While choosing which pathogens to include in our toehold switch library, we kept in mind the global relevance. We included toehold switches for pathogens that are economically important in different parts of the world. Some of the pathogens were also chosen based on our discussions with professionals (see Integrated human practices). Each pathogen is described in our Design page.


Here we shortly present the workflow for creating our toehold switch library.

1. Compiling a list of genomes

To create a toehold switch capable of specifically detecting pathogens worldwide, we collected each pathogen's complete genomes from the NCBI database. The genomes of some pathogens have been collected from a smaller geographical area, such as Europe.

2. Multiple sequence alignment

A multiple sequence alignment for each pathogens complete genomes was performed using Jalview’s alignment tool Muscle with defaults (Troshin et al, 2011; Troshin et al, 2018).

3. Determining conserved areas

The multiple sequence alignments were analyzed using Jalview (Waterhouse et al, 2009). Conserved regions with length more than 36 nucleotides were determined from the multiple sequence alignments.

4. Toehold switch desing with NUPACK

The conserved regions were then run in NUPACK with a code we prepared for this purpose. As a result, we got toehold switch structures and a score to rank them with. The first version of the code programmed NUPACK to go through only one sequence at a time. A modification on the code allowed it to run through a whole file of sequences as well as to create a file of results ranking the toehold switches based on the score. Despite the modifications, the program often stopped running when encountering a single error. Once again, we modified the code. This time when encountered by an error, the same line would be runned again and fortunately the problem was solved. This design code was optimized with our partneships, teams TAU and IISER-Tirupati (read more from our partnership page).

5. Search for stop codons

After creating the toehold switches, we had to check whether there were any stop codons in the reading frame after the starting codon. Stop codons would have stopped protein synthesis and for this reason those toehold switches containing them had to be discarded.

6. Creating a potential structure for the inactive and active toehold switch forms

Finally, we modeled the expected structures of the toehold switches by using NUPACK’s analysis tool.

Finalized toehold switch library

Dry Lab created a toehold switch library containing 59 toehold switch structures for 12 plant pathogens. The final toehold switches can be found on our parts page.

Golden gate design

Toehold sensor plasmids were constructed with Golden Gate assembly. We designed flanking sequences for each part of the sensor plasmids based on iGEM Type IIS standards with minor modifications. To prevent frame-shifting, we needed scarless assembly between the toehold switch and the reporter fragment, but the resulting junction site corresponds to the AATG specified in the standards, as the last nucleotide of toehold switches used in our lab is an A. To minimize the risk of incorrect assembly, we also changed the junction site between the cds and the terminator. In the standard, this site has the sequence GCTT, which shares three consecutive nucleotides with the CGCT. We decided to modify this to GGTT. All assemblies were designed and computanionally modeled using Benchling.


Wolfe, B. R., Porubsky, N. J., Zadeh, J. N., Dirks, R. M., & Pierce, N. A. (2017, February 13). Constrained Multistate Sequence Design for Nucleic Acid Reaction Pathway Engineering. Journal of the American Chemical Society, 139(8), 3134–3144. https://doi.org/10.1021/jacs.6b12693

Wolfe, B. R., & Pierce, N. A. (2014, October 20). Sequence Design for a Test Tube of Interacting Nucleic Acid Strands. ACS Synthetic Biology, 4(10), 1086–1100. https://doi.org/10.1021/sb5002196

Zadeh, J. N., Wolfe, B. R., & Pierce, N. A. (2010, August 17). Nucleic acid sequence design via efficient ensemble defect optimization. Journal of Computational Chemistry, 32(3), 439–452. https://doi.org/10.1002/jcc.21633

Dirks, R. M. (2004, February 23). Paradigms for computational nucleic acid design. Nucleic Acids Research, 32(4), 1392–1403. https://doi.org/10.1093/nar/gkh291

Benchling [Biology Software]. (2022). Retrieved from https://benchling.com.

Troshin PV, Procter JB, Barton GJ (2011) Java bioinformatics analysis web services for multiple sequence alignment–JABAWS:MSA. Bioinformatics 27 2001-2002. ( doi:10.1093/bioinformatics/btr304)

Troshin PV, Procter JB, Sherstnev A, Barton DL, Madeira F, Barton GJ (2018) JABAWS 2.2 Distributed Web Services for Bioinformatics: Protein Disorder, Conservation and RNA Secondary Structure. Bioinformatics 34 1939–1940. (doi:10.1093/bioinformatics/bty045)

Waterhouse AM, Procter JB, Martin DMA, Clamp M, Barton GJ (2009) Jalview Version 2 - A multiple sequence alignment editor and analysis workbench. Bioinformatics 25 1189-1191. (doi:10.1093/bioinformatics/btp033)