This year our team contributed to the field of synthetic biology in many significant ways. We made [1] a software contribution (developing a highly customizable data-driven toolkit for chassis selection), [2] modeling contributions (incorporating a synthetic circuit into a genome-scale metabolic model and detailing the mathematical models behind our software), [3] wetlab and data contributions (improving a stationary phase detection circuit, generating original 16S rRNA sequencing data local to our campus, characterizing a 16S database with associated metadata, and providing data for an international measurement study), [4] education contributions (creating two innovative educational synthetic biology games, writing a literature review about chassis selection targeted at synthetic biologists, producing a synthetic biology activity booklet for students and teachers, producing an educational video on chassis selection, and creating a synthetic biology picture book for young students), and [5] contributions in enhancing diversity and inclusion in synthetic biology (creating a guide for recruitment and selection of a diverse iGEM team, hosting inclusive educational events, and developing a gut microbiome therapeutic inclusivity optimizer).
1. Software Contribution
Development of chassEase
Synthetic biology constructs with enormous potential to mitigate global problems are often unable to be deployed in their target environments due to a number of ‘fieldability roadblocks,’ including the selection of an appropriate chassis. Our team has taken a large stride to overcoming this chassis selection challenge by developing our data-driven software, chassEASE. This toolkit, which is freely available on GITLAB, allows researchers to input parameters from their target environment, and, utilizing multivariate regression, genome scale metabolic models (GEMs), and artificial intelligence, estimates which chassis will best survive in that location. Since chassEASE is currently designed for predictions in soil, water, air, and human gut microbiomes, it will aid in the effective deployment of constructs ranging from pollution degradation, greenhouse gas sequestration, food security, and even personalized medicine. Our human gut microbiome tool, which we colloquially call our ‘inclusivity optimizer,’ encourages production of therapeutics that will work in the most diverse range of gut microbiomes possible. Our other three tools, focused on soil, water, and air, encourage production of fieldable constructs specialized for the areas that they are needed. Together, our chassis selection toolkit is an invaluable asset to the field of synthetic biology, as no other computational tool exists to assist researchers with the vitally important step of chassis selection for fieldable environments. Not only will this toolkit streamline part of the design process, but also it will embolden researchers to create fieldable circuits. To learn more about our software, please visit our project description page.
You can access the gitlab here: chassEASE.2. Modeling Contributions
Incorporation of a Synthetic Circuit into a GEM Model
Genome Scale Metabolic Models (GEMs) are the future of in situ synthetic biology, enabling detailed computational simulations of all the metabolic processes occurring within a single cell via flux balance analysis. Since “mechanistic GEMs can predict microbial behaviors in untested conditions,” (Leggieri 2021) they have the ability to hugely expand fieldable synthetic biology. From our literature review, our team was shocked at how little work has been done to incorporate synthetic circuits into existing GEM models. Since we hope to expand our software to include this feature, as a proof of concept, we partnered with GastonDay-Shangde iGEM. They provided us with information about their construct, a circuit that enhances production of cinnamaldehyde from L-phenylalanine in Escherichia coli by upregulating transcription of three enzymes: phenylalanine-ammonia lyase, 4-coumarate-CoA ligase, and cinnamoyl-CoA reductase (Bang et al., 2016). We coded these reactions into COBRA model iJO1366 of the BIGG Database (King et al., 2016), enabling us to successfully model the metabolic pathways of their iGEM project. Since this is a largely unexplored area of synthetic biology, we have documented the code we used, and hope that synthetic biologists will use it as a guide for their future projects. To see our code and learn more about our partnership with GastonDay-Shangde iGEM, please visit our partnership page.
Detailed Description of Models Behind chassEASE
One of our goals for this year’s project is to advocate for increased use of computer science and data science in synthetic biology, particularly to enable fieldable applications. To this end, we have documented in detail the models behind our software. To access this documentation of our mathematical models, please visit our modeling page.
3. Wetlab and Data Contributions
Improvement of Part BBa_J45995 to Create a Highly Effective Growth Arrest Detection Circuit
When testing fieldable synthetic constructs, it is important to know whether or not your circuit is still being expressed. Bacteria have two main life states: Exponential Growth, when the circuit is typically expressed with ease, and Growth Arrest, during which most non-essential protein production is ceased. Growth Arrest is often triggered by inopportune environments, such as those which lack important metabolites or contain stress factors (Jaishankar 2017). Thus, the ability to assay which growth state your bacteria is in is very important to fieldable synthetic biology. Because of this, we decided to improve a circuit designed by MIT in 2006 (BBa_J45995) which fluoresces only once the cell has entered the Growth Arrest phase. This circuit uses an osmY promoter, as osmY production is shut off during Exponential Growth and induced by rpoS during Growth Arrest (Chang 2002). We created two constructs, a Superfolder Green Fluorescent Protein construct (BBa_K4174002) and a Red Fluorescent Protein construct (BBa_K4174001), both of which are more fluorescent than the original circuit. To read more about our success in improving a part, please visit our parts pages on the iGEM parts registry or our Improve a Part page.
Generation of 16S rRNA Sequencing Data on Local Soil Types
While building our model, our team was frustrated by the lack of 16S data that had associated metadata. While a large abundance of 16S studies exist, only in a small portion do researchers record the environmental parameters of the collection site. This lack of paired metadata makes reusing data for new purposes, such as in our predictive model, impossible. We collected our own 16S data from soil samples local to William and Mary in order to have unique samples to test our models. In order to contribute even more largely to the field, we have published the well organized 16S data with associated metadata for any researchers to use in their models. To learn more about our 16S study, please visit our Proof of Concept page.
Creation of Well-characterized 16S Database with Associated Metadata
To further contribute to alleviating the lack of well-characterized 16S data explained in the last point, our team has made our database with thousands of 16S samples with associated metadata, which we are using for our software, public on our GITLAB. This will greatly contribute to future computational efforts pertaining to fieldability, as assembling this database was extremely time consuming. You can access the gitlab here: chassEASE.
Participation in the InterLab Study
The International InterLaboratory Measurement Study, hosted annually by iGEM, is a large-scale experiment analyzing the reproducibility of procedures as well as the inherent measurement errors present in biological studies. Researchers from around the world follow the exact same protocol, and then submit their results to be analyzed for discrepancies. This year, the sixth iteration of the InterLab Study, focused on fluorescence testing of both single- and dual- colored constructs in Escherichia coli DH5-alpha using a plate reader. We elected to participate alongside many other iGEM teams in order to contribute data to this study, which is typically published in scholarly journals and widely distributed. We are very grateful for this opportunity to contribute to the larger understanding of error and variability present in biological studies, and look forward to the results being published.
4. Education Contributions
Production of Two Innovative Synthetic Biology Games
In order to improve public perception of synthetic biology, create an educational tool for students, and raise awareness for major global problems, we designed two innovative, collaborative synthetic biology games based on the board game Terraforming Mars designed by Jacob Fryxelius (with graphic design by Isaac Fryxelius, and artwork by Isaac Fryxelius and Daniel Fryxelius). Our first game is an expansion pack to be added into the original game, which incorporates synthetic biology into the mission to make Mars habitable. Since the pre-existing game has an avid fanbase, we wrote this pack to integrate seamlessly into the game. Our second game is a stand alone spin-off entitled Re-Terraforming Earth. As our planet is being destroyed by climate change, our game advocates for players to collaboratively utilize synthetic biology to decrease greenhouse gas emissions, decrease pollution, and increase food security. This game requires players to build circuits out of promoter, RBS, coding region, and terminator cards, and then play those cards into a chassis and deploy them into the environment. Other cards reference important aspects of fieldable synthetic biology, such as horizontal gene transfer, safety, and kill switches. Re-Terraforming Earth was highly successful at teaching high school students about synbio when we tested it: on the pre-quiz, none of the 15 students could name a single part of a synthetic circuit (average of 0), but on the post-quiz, and an average of 3.6/4 parts were named. We have uploaded all of the materials to physically create the game, as well as the rules, onto our wiki, so that anybody can download and print these educational materials. To see the games and learn more about them, please visit our Communication page.
Creation of a Chassis Selection Literature Review for Synthetic Biologists
A major goal of our project is to encourage synthetic biologists to choose fieldable chassis for their circuits so that their systems can easily be implemented outside of the lab. To that end, we have written a comprehensive literature review on chassis selection targeted towards practicing synthetic biologists. Detailing the past, present, and future of synthetic biology, we hope to publish our review in a scholarly journal in order to widely promote chassis selection for fieldable synthetic biology. The review is freely available on our wiki; to read it, please visit the Communication page.
Production of Synthetic Biology Booklet for Middle and High School Students
In order to encourage young students from across disciplines to engage with synthetic biology, we have created a primer booklet discussing the basics of molecular biology, genetic engineering, and how to get involved in synthetic biology. It has been uploaded on our wiki for educators to download and distribute. The booklet also contains advice for educators, with examples of hands-on, engaging synthetic biology activities to incorporate into lessons. To view this booklet, please visit our Communication page.
Production of Educational Video on Chassis Selection
We created a white-board style educational video on chassis selection targeted towards middle and high school students. It explains the central dogma of biology, the basics of synthetic biology, and why chassis selection is important. The video also heavily emphasizes the importance of computer and data science to the field of synthetic biology, which we hope encourages multifaceted and interdisciplinary involvement in synthetic biology. We produced this video as part of a video series in collaboration with Hopkins iGEM and East Coast BioCrew iGEM, so this video was posted on the Johns Hopkins youtube channel. For more information about this video, please visit either the Collaboration or Education page.
Production of Synthetic Biology Picture Book
Another education deliverable created by our team was inspired by the collaboration workshop we attended at this year’s MidAtlantic Meetup. Our team developed a story to introduce the general concept of synthetic biology to young children. The main character of the story, Rumi, uses a microscope to observe bacteria, and readers are taken on a journey where they observe bacteria in multiple settings, such as soil, water, and air. The general concept of DNA is introduced through the story as well, which explains how the DNA of bacterial cells is different from that of other organisms. Examples are given to demonstrate how engineering bacterial DNA can provide bacteria with new, useful abilities such as the ability to fluoresce, the ability to remediate water sources, and the ability to counter viral infections. To view this picture book, please visit our Communication page.
5. Diversity and Inclusion Contributions
Creation of Guide for Inclusive Team Recruitment
It is crucial that the field of synthetic biology become more inclusive. To us, a clear way to help bring about this change is by starting with our own team. Each year, we intentionally build a diverse team in terms of race, gender, discipline, and experience. In order to assist other teams with inclusive recruitment and diverse team building, we have created a guide on our recruitment strategies. To access this guide and read more about the importance of inclusive iGEM team recruitment, please visit our Inclusivity page.
Inclusive and Accessible Educational Events
Synthetic Biology, like all disciplines, will be dramatically improved by increasing the diversity of perspectives in the field and, to reach that end, the creation of accessible educational materials. The educational materials listed above (both of our games, our literature review, our educational booklet, our educational video, and our picture book) all have been uploaded for free on our wiki, where anybody can access them. Creating free educational resources promotes equitable access to information. Additionally, we hosted many educational events with groups historically underrepresented in biology, including but not limited to hosting a field trip with camp EAGER (a STEM summer camp for marginalized students) and presenting research at William and Mary’s Women’s Weekend. To learn more about our educational events, please visit our Education page.
Gut Microbiome Therapeutic Inclusivity Optimizer
While our software allows researchers to investigate chassis for four different environments, the gut environment aspect is unique as it is designed to help researchers develop more inclusive therapeutics. Our software helps researchers select a chassis that is present in a wide range of different gut microbiomes. For each demographic category (age, country of origin, sex, or BMI) researchers can choose their parameters. For example, they could set age to 20-90 and sex to female, while leaving country of origin and BMI set to include all samples. The software program then searches across all of the samples that meet these characteristics and returns the 10 most dominant bacterial species and genera across these samples. This tool allows researchers to find chassis that are widely inclusive across humans, or within a categorical group of people, while also being specific to the limits or needs of their genetic system.
References
Bang, H. B., Lee, Y. H., Kim, S. C., Sung, C. K., & Jeong, K. J. (2016). Metabolic engineering of Escherichia coli for the production of cinnamaldehyde. Microbial Cell Factories, 15(1).
Chang, D. E., Smalley, D. J., & Conway, T. (2002). Gene expression profiling of Escherichia coli growth transitions: an expanded stringent response model. Molecular microbiology, 45(2), 289-306.
Jaishankar, J., & Srivastava, P. (2017). Molecular basis of stationary phase survival and applications. Frontiers in microbiology, 8, 2000.
King, Z. A., Lu, J., Dräger, A., Miller, P., Federowicz, S., Lerman, J. A., Ebrahim, A., Palsson, B. O., & Lewis, N. E. (2016). BiGG Models: A platform for integrating, standardizing and sharing genome-scale models. Nucleic acids research, 44(D1), D515–D522.
Leggieri, P. A., Liu, Y., Hayes, M., Connors, B., Seppälä, S., O'Malley, M. A., & Venturelli, O. S. (2021). Integrating systems and synthetic biology to understand and engineer microbiomes. Annual Review of Biomedical Engineering, 23, 169-201.