We have satisfied the Bronze Medal Requirements as detailed below
1. Competition Deliverables: We have completed the Wiki Page, the Project Promotion Video and the Judging Form.
2. Attributions: Our Attributions page outlines the contributions of team members, advisors, principal investigators, experts, stakeholders, funders, sponsors, and other contributors to our project. For team members, this includes contributions such as the development of our software, improvement of part BBa_J45995, conducting interviews for human practices, writing, coding, and creating graphics for our wiki pages, participating in our partnership with GastonDay-Shangde iGEM, and creating our educational deliverables, including our educational board games, chassis selection literature review and video, and synthetic biology activity booklet. On our page, we recognize all the time and support provided to us by our principal investigators, faculty members who provided their feedback on our software and took measurements for our soil samples, environmental, microbiology and modeling experts who we consulted for human practices, funders within our university, external sponsors, W&M Research Computing which provided us with resources and technical support, and others. For more information, please visit our Attributions page.
3. Project Description: Our Our Description page discusses the importance of optimal chassis selection to the design of fieldable synthetic biology systems. We explain how our computational approach to chassis selection allows for the efficient selection of ideal chassis for a given environment and is highly customizable. We provide an overview of our software’s design, including the 16S rRNA sequencing data on which it is based, and incorporation of regression, neural networks, and genome-scale metabolic models. We describe the environmental parameters that serve as the inputs for our software, as well as the outputs, which include a ranked list of the most optimal chassis for an environment. We include informative graphics and references to the relevant sources and scientific literature that served as a foundation for our project. For more information, please visit our Project Description page.
4. Contribution: Our major contributions include: 1) development of a highly customizable software program, providing a tool for chassis selection in a wide range of environments and the design of more fieldable synthetic biology systems, 2) generation of 16S rRNA sequencing data on local soil types, increasing the data available on the abundance of microbial species in specific soil environments, 3) creation of innovative synthetic biology games based on the game “Terraforming Mars,” 4) a literature review for synthetic biologists on chassis selection, summarizing the current state of chassis selection in synthetic biology, 5) a synthetic biology activity booklet for middle school and high school students, 6) an educational video on chassis selection, 7) participation in the 2022 iGEM InterLab Study, 8) improvement of part BBa_J45995, 9) incorporation of a genetic circuit into a genome-scale metabolic model, and 10) detailed documentation of the extensive design-build-test process we used in developing our software. For more information, visit our Contribution page.
Silver Medal
We have satisfied the silver medal requirements as detailed below:
1. Engineering Success:
This year our team project concludes several engineering cycles that involve designing, building and testing. Our project has different aspects and each aspect required constant refining and testing. Our chassis prediction model, a linear regression model, was first designed to have a low prediction accuracy. We kept refining the model by doing statistical operations such as variable selection based on stepwise regression. As our dataset is large and composed of more than 80 vectors of different environmental variables, correlation between independent variables can play a vital role in impacting our regression model. Operations such as normalizing variable values were needed to optimize our model. To get a better prediction result, we tried different models such as Random Forest, KNN and Neural Network. We repeated each different model with different optimization methods and model parameters to get a desired prediction result. We also describe our creation of a new part, part
BBa_K4174001. For more information, please visit our Engineering Success page.
2. Collaboration: Our major collaborations include: 1) Our partnership with GastonDay-Shangde iGEM, which involved modeling a genetic circuit developed by their team to convert L-phenylalanine into cinnamaldehyde. Through incorporating this circuit into a genome-scale metabolic model (GEM), we were able to provide their team with a model of their system and experiment with methods of accounting for circuits within GEMs. 2) Our collaboration with Johns Hopkins and East Coast BioCrew iGEM, in which we contributed a video on chassis selection to their educational video series. 3) Our attendance at a conference hosted by Pui_Ching Macau and UM Macau iGEM in which we proposed a method for improving their aquaponic system. 4) Our participation in the 2022 iGEM InterLab Study. Along with this, we completed minor collaborations such as participating in UNSW iGEM’s compilation of synthetic biology articles, taking part in IISER-Pune iGEM’s World Environment Day challenge, and completing surveys. For more information, please visit our Collaboration page.
3. Human Practices: As our project focused on fieldability, our team had to confirm that (1) fieldability of synthetic biology constructs will have a positive impact on the world and (2) that our software is useful for designing fieldable constructs. Our team spoke to several individuals who stated that fieldable synthetic biology systems could positively impact their lives or their work. These individuals ranged from a person conducting research on coral samples to a person operating a community garden in Williamsburg. To confirm the usefulness of our software, we spoke to several experts in the field. During these conversations, it became clear that chassis selection remains a challenge in the field of synthetic biology. All three synthetic biology experts that we interviewed stated that our software program would be useful for their work. These experts included synthetic biologists working in many different areas, illustrating that our software would assist researchers across the field. For more information, please visit our Human Practices page.
4. Proposed Implementation: We uploaded our software code to our wiki and on GITHUB so that it is free to use by any researcher or educator, and plan on disseminating it via publication and direct communication with researchers and companies. We will reach out to IHP contacts who expressed interest in our software, synthetic biologists we have seen in our literature reviews, and future iGEM teams to encourage use of our software as a crucial part of circuit design. Additionally, we hope to publish our models, code, and 16S results in a scholarly journal and present our project at numerous academic conferences and forums. Furthermore, we have outlined steps we wish to take to improve our software after this iGEM season, such as adding more data into the models and adjusting our parameters, and we also hope to incorporate feedback from the synthetic biology community, as our software is used, into future iterations. For more information, please visit our Proposed Implementation page.
Gold Medal
We have satisfied the gold medal requirements as detailed below:
1. Integrated Human Practices: Based on IHP we made the following changes to our project: 1. switched to a purely computational project, 2. added a linear regression into our model, 3. incorporated temperature and moisture content as software parameters, and 4. integrated sink reactions into our circuit model. Mr. Marken stressed the importance of computation to synthetic biology. In response, we switched to an entirely computational project, something new to our team. After changing our project direction, we received lots of feedback on our project design and suggestions for improving accuracy. We incorporated a linear regression into our software after Mr. Marken suggested it might improve results. We also included temperature and moisture content as parameters for our software as recommended by Dr. Franzluebbers and Dr. Adams. Using Dr. Kunjapur’s advice to add sink reactions into our circuit model, our team was able to effectively model the impact of circuit addition using GEMs. For more information, please visit our IHP page.
2. Improvement of an Existing Part: Below are the part numbers for the part we have improved and the new part we created. For more information, please visit our Improved Parts page.
3. Project Modeling: Our team uses several models to predict the relative abundance of bacteria in real environmental conditions based on the presence of certain metabolites. Using thousands of measurements from real field data, we used multivariable regression, Neural Network, Random Forest, and KNN models to make informed predictions about the abundance of bacteria in soil. Additionally, outside of empirical data, we used Genome Scale Metabolic Models to predict the bacteria’s growth rate based on the intrinsic properties of individual bacteria. We use these two abundance analysis techniques to create a toolbox that predicts the abundance of bacteria species in varying conditions and subsequently the optimal chassis. Our team collected 16s data which was then input back into our system to create a positive feedback loop of increasingly accurate data. For more information, please visit our Modeling page.
4. Proof of Concept: For our proof of concept, our team needed to demonstrate that we could develop data-driven software that can take real-world inputs and produce a suggestion for the optimal chassis species. Since our software takes environmental parameters as inputs, our team conducted 16S sequencing of soil samples in our local area to collect actual data. Accordingly, we collected environmental parameters from these soil samples to be used as test inputs for our software. With these results, our team will be able to prove that our software is capable of taking in real world inputs and producing a suggestion for an optimal chassis for that environment. For more information, please visit our Proof of Concept page.
5. Partnership: Our partnership with GastonDay-Shangde iGEM took place throughout the season and furthered both of our projects. GastonDay-Shangde iGEM’s project involves engineering E. coli to produce cinnamaldehyde from L-phenylalanine to counter the effects of attenuated nonketotic hyperglycinemia. We modeled their system through incorporation of their cinnamaldehyde-producing circuit into a genome-scale metabolic model (GEM) for E. coli. Using a COBRA toolkit, we added three reactions and four metabolites to the GEM, and accounted for the growth conditions of their chassis. We communicated through email and virtual meetings to discuss the enzymes encoded within their circuit, confirm the reactions involved in the conversion of L-phenylalanine to cinnamaldehyde, share relevant articles, give suggestions for potential improvements of their system, and provide them with the code for our model of their system. This experience provided our team the opportunity to experiment with ways of incorporating circuits directly into GEMs, a future direction for our project. For more information, please visit our Partnership page.
6. Education and Communication: We centered education with 1) an educational game, 2) in-person events, and 3) targeted guides. 1) We developed a game, called “Re-Terraforming Earth”, adapted from the game “Terraforming Mars”. It teaches how SynBio can help the Earth, along with a SynBio expansion pack for the original Mars game. We tested it with students to receive feedback on how to improve it. 2) We had three events where we brought in middle or high schoolers, teaching gel electrophoresis, giving a lab tour and circuit demonstrations, and play-testing our game Re-Terraforming Earth. 3) We wrote an educational booklet that explains the field and applications of SynBio at a level appropriate for middle and high school students, and its accessibility online makes it a resource for a much wider audience than young students. Our chassis selection review can inform our peers in iGEM and the field on the importance of chassis selection. For more information, please visit our Communication page.
Special Prizes
Additionally, we would like to be considered for the following prizes:
1. Education: Inclusivity and Two-Way Communication were the keystones of our numerous educational projects this year. To enhance inclusion in synthetic biology, we created many freely accessible educational materials, including but not limited to a board game (Re-Terraforming Earth), a literature review and educational video on chassis selection, and an educational booklet for students and educators. We also hosted many events with diversity and inclusion in mind, such as our field trip with Camp EAGER (a STEM summer camp for kids from groups underrepresented in the sciences) and our lab tour for our college’s ‘Women’s Weekend’. Two-Way communication was also vitally important to us. We learned from our target audiences by assessing two of our educational materials (Re-Terraforming Earth and our chassis selection video), as well as hosting a multitude of in person educational events where we were able to directly engage with the communities we were working with. For more information, please visit our Education page.
2. Inclusivity: Inclusivity drove every aspect of our project, seen in six ways. 1, The product of our project itself, our software tool, centers on inclusivity. As one of our environments of focus is the human gut, we build this aspect of the project to allow researchers to search for a chassis that fits a wide range of people’s gut microbiomes, to help them develop more inclusive therapeutics. 2, Hosting educational events for underrepresented groups in the sciences. 3, Producing education materials that are accessible online and aimed at a broad range of audiences. 4. Recruiting a diverse group of individuals for our team, 5. Creating materials to make computation education more accessible, and 6. Striving for inclusion of different scientific disciplines in the field of synthetic biology. For more information, please visit our Inclusivity page.
3. Integrated Human Practices: IHP shaped our project by 1. changing our project direction, 2. influencing our project design, 3. directing our educational efforts, and 4. inspiring our inclusivity work. First, IHP motivated our team to switch from the foundational advance track to the software track. Mr. Marken emphasized the importance of computation to synthetic biology, which motivated us to want to build a computational tool. Second, we incorporated several suggestions from IHP interviewees into our project design, such as adding soil moisture and temperature as software parameters. Third, Dr. Stephens inspired us to create visual aids for education, leading to the development of our Re-Terraforming Earth game and our Terraforming Mars Expansion Pack. Fourth, Dr. Adams highlighted the importance of including other fields in synthetic biology, which motivated our inclusivity work and led us to attempt to get our university’s synthetic biology class to count as an elective for the Environmental Science Major. For more information, please visit our Integrated Human Practices page.
4. Model: 1) We provide a new solution to the chassis selection problem by integrating statistical regression and bacteria data in different environments. Our model is highly effective and accurate. Moreover, our model is easy to operate and highly flexible. Because of the nature of statistical models, we can always improve our prediction results progressively. 2) Specific bacterial reads are given for specific conditions. 3) We utilized a variety of models such as Random Forest, KNN and linear regression, which gave us different insights into our data, leading to optimization of our models. One crucial aspect of using a model is to give mathematical insights of our project. In our case, using various models provides valuable feedback about the feasibility of our software. For more information, please visit our Model page.
5. Sustainable Development Impact: During the 2022 season, the SDGs impacted our (1) project design, (2) educational efforts, and (3) collaboration work. First, we incorporated SDGs Number 14: Life Below Water and 15: Life on Land into our project design. Our team is tackling fieldability as our project. Fieldable systems have a multitude of benefits for both aquatic and terrestrial systems as we learned through our IHP efforts and our review of the literature. Second, we also incorporated SDG 4: Quality Education into our project. Our team created several educational materials, including a board game and an educational booklet, and hosted several in-person educational events. Third, through our collaboration work, we were able to address SDG Number 17: Partnerships for the Goals. We participated in several different collaborations that incorporated SDGs Number 4, 14, and 15. For example, we collaborated with iGEM teams from McGill, Cornell, and Queens to produce an educational picture book. For more information, please visit our Sustainable page.