Team:FCB-UANL/Poster

Team:FCB-UANL/Poster

SYNBIOFOAM

Abstract
Mexico's northern biogeographic region is home to a wide biodiversity, which is at risk due to the frequent forest wildfires. One of the strategies to combat fires involves the use of firefighting foams, but these usually contain fluorosurfactants (PFOs), which pose an environmental threat. This inspired us to produce an eco-friendly alternative to this type of threatening substances.
To do so, we plan to employ synbio tools to produce four of the Ranaspumin proteins present in the bubble nests of Leptodactylidae frogs, and regulating B. subtilis’ natural complex metabolic pathway in charge of synthesizing its biofilm’s matrix components for us to substitute PFOs as foaming agents in firefighting mixtures. To achieve this, we conducted a thorough analysis of the biological components involved, and we also executed several models which helped us understand how our system and foam behaved.
As well, we performed outreach activities that included science communication, policy revision, and entrepreneurship to connect our product with the outside world. Not only will our project aid the environment, but it will also allow underfunded fire departments to access these tools because, as there are no foam producers in this country, current foams are very expensive due to importation costs.

Authors
Alvarez-Robledo Daniela
Solís-Pacheco Alejandro Alfonso
Hernández-Sánchez Abel Adonaí
González-Garza Leonardo Uriel
Flores-Coronado José Alonso
Valenzuela-Chapa Cynthia Elizabeth
Meléndez-Calvo Julieta Monserrat
From Team FCB-UANL

Stakeholders
-Parques y Vida Silvestre Nuevo León: “Your fire educational activities should also focus on homes, and not only forests”
-M.Sc. Gloria Vallejo: “When using foams, more does not usually mean better” (because foams have a certain concentration at which they work).
-Instructor Lucia Florido: “Fire Retardants are not usually used because of their high costs and their pollutant features”

Circuit Design

E.coli

Ranaspumins
Ranaspumin-2 will be produced in one strain while Ranaspumins 3-5 will be produced in a different one. For both strains, the expression of the proteins will be controlled by a well characterized vanillate inducible promoter, pVANcc. This promoter is regulated by VanRAM, which is further placed under the control of the promoter PlacIQ and on the other hand, each ranaspumin´s RBS were designed with the RBS calculator tool developed by Salis (2011). All these proteins carry a 6xhis tag with a thrombin cleavage site for further purification with nickel columns. The terminator used is rrnb T1 (BBa_B0010).

B. subtilils

Surfactin
We plan to produce surfactin, another foaming agent, by overexpressing PhrC (also known as CSF) and Sfp under the control of a xylose-inducible promoter. Overexpression of PhrC would allow us to increase expression of the srf operon -which is in charge of producing the necessary machinery to make surfactin- since it inhibits RapC, a protein that negatively regulates ComA~P, which induces transcription of the srf operon. At the same time, we plan to overexpress Sfp because its gene product activates the surfactin synthetase enzyme (which is produced by the srf operon). The construct also contains Spo0E, a delayer of sporulation, and cat, a chloramphenicol resistance gene.


Biofilm
We expect to increase the biofilm production to stabilize our foam by expressing the proteins AbbA and SinI through a xylose-inducible promoter, which is regulated by XylR. These two proteins, AbbA and SinI, hinder the activity of AbrB and SinR, respectively, which are biofilm inhibitors. The construct also contains Spo0E, a delayer of sporulation, and cat, a chloramphenicol resistance gene.



Stakeholders
PhD Leonardo Rios: “You should study more deeply the genetic regulation networks and molecular interactions”

Expression Modeling

Ranaspumin model
We modeled the expression and production of the ranaspumin proteins in E. coli. The model is based on the expression model from the iGEM UANL 2019 Team, and the objective is to use this information to calculate the right expression rate to avoid cellular toxicity. We found out that the best range of concentrations to test with the engineered strains for the production of Rsn-2 is from 0.001 to 0.01 μM.

Surfactin model
Now, to study how the engineered RapC-PhrC system is going to influence B. subtilis natural network, we made SMBL modeling (A programming language for systems biology modeling) with the iBioSim 3 tool (Watanabe, L. et. al, 2019). After executing it, we obtained the following results:





It can be seen that srfA has a slight 2-fold increase in its expression during the first 16.6 minutes, and then it returns to equilibrium state concentrations, even though xylose concentration is kept constant. This result shows a highly resilient system in which our components might not increase the surfactin as much as we expected based on reports. However, we need more data to get a bigger picture of the circuit and parameters’ sensibility.

Biofilm Model
On the other hand, we also used the same approach to model the expression of the sinI protein, a key regulator that enhances biofilm production. After running the program, we obtained the following results:





As it can be seen in the graphs, we expect an increase of concentration of tapA in 32.5%; still, the curve returns to the equilibrium point at 100.5 nM, i.e., at a 7.5% increase of production compared to the initial state. This means that we may not have the biofilm exopolysaccharide concentration needed for the formulation of the foam.

Methodology

Experimental plan
The general strategy we will follow within the laboratory is summarized in the following diagram, which shows an analytical view of the building, production, testing, analyzing and feedback loops processes for each one of the components for every strain.

Once the strains are successfully built, the following steps for each one consist of the compounds’ production optimization, purification, and quantification. Then, we need to make a series of analyses to test the characteristics of each component (shown in the fifth row of square of the diagram). These data will be useful to establish a math model for the optimization of the formulation of the foam. The model for the formulation of the foam and the experimental results will complement each other and undergo feedback loops to set the right parameters. For the proof of concept, we will go ahead with the optimization of the production yield at the same time as the testing and characterization of the properties of the foam produced by the components.
Once we get a formulation that meets our expectations, we will go ahead with professional tests for firefighting foams (shown in the seventh row of squares of the diagram) in collaboration with a firefighting foam production company (Amerex). The opportunity areas of our foam will be identified, and the results obtained from these final tests will be integrated with the math model and the experimental results for further optimization of the formulation.

Upscaling

We are aware that our project must not stay within the walls of a laboratory, so that is the reason why we plan to upscale the project once we start advancing in the experimental part of it. First, for the technical part of the upscaling, we plan to have three production lines, each one consisting of bioreactors producing separately the three components of the foam: the ranaspumins, the surfactin and the biofilm. This process would give us the foam concentrate as the final product.

We recognize that all of this procedure must meet some safety measures to prevent people from getting hurt. That is the reason why a series of guidelines, related to the bioreactors’ operation and maintenance, as well as microorganism management, will be undertaken. As well, the regulatory process according to national legislation will be followed.

Last but not least, another very important aspect for the implementation of the project is the entrepreneurial component of it. Here, we have considered our final users, from which one of the principals are firefighting departments.

But, how are we going to get to them? We plan to introduce ourselves into existing production processes of firefighting foams -obviously conducting a series of adjustments that match to the elaboration of our foam-. Therefore, we have contacted key stakeholders, and one of them is Amerex, an American firefighting equipment manufacturer and distributor that has shown interest in our proposal and reached an agreement with us to help the team with the testing of the quality of our foam.

Besides, we have continued to establish very important aspects about our product, the marketing, and strategies, which are described in the Business Model we wrote. In addition to this, we have participated in different competitions from which TigerTank and Biohunt stand out, because we are participating in an incubation process to mature our idea in the former and we won third place in the Star category in the latter. We must outline that this could not have been possible without the help of our advisors, César Sánchez and Marco Vergara, who have guided us throughout this journey.

Stakeholders
-AMEREX: “There are no firefighting foam producers in Mexico”
-Eng. Marco Vergara: “Cold Fire (protein based firefighting foam) is about to retrieve from Mexico”
-Director César Sánchez: “You should check the certifications to which your product could apply”

Physical Modeling

To measure the stability of the foam, we decided to simulate the drainage of the liquid that makes up a bubble. This phenomenon is generated by the force of gravity, causing a partial loss of the total liquid of the bubble and, consequently, a decrease in its stability. This is something we do not want to happen since our bubble would be more likely to burst due to external forces. This model is based on the model made by Schwartz & Roy (1999).
For a better understanding of the situation, we solved a series of 3 equations to determine some specific characteristics of the film to be considered in the simulation. To do so, we used the FORTRAN programming language to code the model which helped us comprehend how three variables behave: thickness, slip velocity and surfactant concentration.

Results
The green line represents a surfactant concentration at 2000 and the purple line a concentration at 8000 (to simplify analysis, we must mention that the variables are in their dimensionless form). Here, Graph 1a represents the distribution of the surfactant’s concentration in the bubble, while Graph 1b represents the velocity at which the bubbles lose liquid (or drain). If we compare the curves, it can be observed that as the drainage velocity increases (green curve), the concentration of the surfactant is more uniformly distributed. On the other hand, as the drainage velocity decreases, the distribution of the surfactant’s concentration is less uniform. With this mathematical model, we can conclude that the stability of each bubble depends on the concentration of surfactant we use, and that, the higher the concentration, the longer the bubble’s duration. Knowing this, we can obtain a range of ideal quantities for the creation of the foam and optimize its production.

Outreach

We took as many opportunities as we could to share what SynBio and iGEM mean to us, not only in our university, but also in many different places around the world. Thus, we participated in several conferences, interviews, and symposiums, where we presented and answered questions about our project.

As part of our initiative to communicate science, we also organized the first ever HISPAM Meet-Up. In this event, we hosted several conferences in which we saw the participation of more than 300 speakers and attendants from most of the Latin American countries, regardless if they were members of an iGEM team or not. As a contribution, we decided to make all conferences available on YouTube.

After connecting with the regional community, we looked to maintain this strong bond. To do so, we became part of Biotec Latina as organizing members of the Cultural Exchange Committee. Biotec Latina is a Latin American initiative that aims to break the barrier between Spanish and Portuguese-speaking countries in the region through science and cultural diversity activities.

Keeping in mind that we’ve only discussed how we approached people that understand or work with science, we knew that educating those who are not that related to the topic is crucial. Despite this year’s pandemic has limited our range of activities, we saw online education as a key opportunity to continue with this task. Taking this into consideration, we decided to approach a broader audience by making short videos featuring Dana la Rana , our team mascot, to inform people about topics ranging from science to fire safety. Besides, we also teamed up with the Gliksys (TecMonterrey_Gdl) team. Along them, we worked with Estación Meiquer, a Mexican education company, on a series of activities about microbiology with an empirical approach.

Finally, as part of our outreach activities, we also became deeply interested in changing the public’s opinion on biotechnology from the policy perspective. We analyzed national regulations with the help of our advisors Daniel Domínguez, Ediner Fuentes and Ricardo Chávez; and we even presented a draft of white paper on how to combat Mexico’s misconception revolving around native maize and modern biotechnology, a work for which we were given the first place in iGEM’s first ever Policy Hackathon.

Advisors
-Eng. Daniel Domínguez: “There are many ideological aspects in the political powers, that affect biotech development”
-M. Sc. Ediner Fuentes: “There are many international protocols, like Cartagena, that are not up to date”

Attributions

Team:
Our team is a multi-disciplinary one, ranging from physicists, to biotechnologists, biologists and engineers. During the year, we worked in 6 different committees: Safety, Math, Dry, Wet, Human Practices/Education and Entrepreneurship. We all worked hard during this season to develop our project.

PI’s:
They guided us throughout the year, helping us with the management(monitoring) and integrity of our project.

External Help:
Collaborators - They helped us during the year to enrich and share our project. Advisors - Group of professionals that helped us with the understanding of certain aspects and to look at the bigger picture when we were developing our project.

Finally we want to give a huge THANK YOU, to each and every person that helped us through the development of this project, without their help we would not be standing right here. And a special thanks to our sponsors, for believing in us, and giving us the opportunity to share our project with the world. Now, we are one step closer to migrating into a sustainable society.

References
Salis, H. M. (2011). The Ribosome Binding Site Calculator. Methods in Enzymology Synthetic Biology, Part B - Computer Aided Design and DNA Assembly, 19-42. doi:10.1016/b978-0-12-385120-8.00002-4.

Schwartz L.W.; Roy R.V. (1999). Modeling draining flow in mobile and immobile soap films. Journal of Colloid and interface Science 218 (1):309-323.

Watanabe, L., Nguyen, T., Zhang, M., Zundel, Z., Zhang, Z., Madsen, C., Roehner, N. & Myers, C. (2019). iBioSim 3: A Tool for Model-Based Genetic Circuit Design. ACS Synthetic Biology, 8(7), 1560-1563. doi:10.1021/acssynbio.8b00078.