Team:Rice/Meta Analysis

Inspiration Raw data Process Case Studies Findings Future Actions

Idea Inspiration

We first started thinking about the concept of our meta-analysis when we talked to the Oxford iGEM team about how we came up with our projects. It became clear that there were different inspiration factors at play, as we approached problems differently. Our project of genetically modifying rhizobacteria, even though it would not affect the plant's DNA, was not an project that the Oxford team would work on as their stricter regulations made genetically modifying foods a closed off avenue for their team.

Raw Data Analysis

Looking at the 2019 breakdown of tracks across the world, it is interesting to see how the most popular tracks are the same across the globe. Regions with fewer teams stuck to more common tracks such as environment, therapeutics, and foundational advance and did not participate as much in special tracks.

Looking at the 2012-2018 breakdown of tracks in North America specifically, the relatively low changes in percentage of teams participating in tracks is interesting. Some of the older tracks a such as health & medicine and food & energy no longer exist, but their two new branched off groups maintain similar numbers.

The 2019 region distribution shows that the majority of teams are from Asia and Europe. As we learned from talking with Oxford, European teams may have different perspectives that have been shaped by their regulations.

The 2019 track breakdown with numbers of teams shows the large number of teams participating in the same few groups. However, looking at the tracks it makes sense as tracks such as information processing and software could be a difficult task for many teams.

The following 2 graphs compare the size breakdown between the high school division and the other divisions. There are many more undergrad and overgrad teams so it is hard to make a comparison between the two but the average team size is slightly larger for the high school section.

Looking at the number of teams in the high school division from 2012-2019, it is nice to see an increase in size over the years because as iGEM grows it becomes more popular and possible across the world.

Process

We essentially have two components to the analysis: a purely data based analysis for the high school section vs the undergrad and overgrad sections and in depth case studies. We used the iGEM website as a resource to get the team list data as seen in the graphs above.

For our case studies, we wanted to find a way to document the conversations that we had with teams regarding why they chose their projects. We created a set of open ended questions so as to allow teams to fully explain their answers. Even though from a data analysis standpoint we knew that longer answers would be difficult to process, longer answers could offer us more insight.

While we would have loved to contact every team in the world, it was unfortunately not feasible taking into regards our team size and the amount of time we would be able to put into the meta-analysis. Additionally, as we were expecting longer answers we did not want to overburden ourselves and be unable to process the data So, we decided to focus on the United States and broke up the states into 4 distinct regions as indicated by this map.

Regions of the United States that we contacted

We decided to work with the United States because we are more familiar with the different regions and we felt more comfortable drawing conclusions from the responses. As our initial inspiration came from differences in regulations, we were familiar with US regulations and also regional differences.

We randomized our team choosing process by dividing up the US teams by region and separating out the teams. We then assigned numbers to the different teams and used a random number generator to randomly select a team. While this did lead to difficulties in communication with the teams, as not all the teams had easy to access modes of communication, we did not want to discriminate against teams that were not active on social media. Because of this we understood that our response rate may not be ideal. For some regions we had to randomly choose more teams after a certain number of weeks had passed in order to get some responses from a region.

Disclaimer: We know that a couple of case studies cannot be used to make generalizations about iGEM teams overall. Each iGEM team is unique in its own way. This meta-analysis is only the tip of the iceberg but it is an important start. Yes there needs to more manpower to ask these questions so that there can be a greater outreach and more language options to help ease of access. But this meta-analysis is a start and most importantly it poses questions that concern different aspects of iGEM.

Case Studies

Team: WLC Milwaukee iGEM

WLC Milwaukee was very thorough in their answers, making their thought process very clear in their answers which we greatly appreciate. Their project brainstorm process was interesting as they start planning for their next competition cycle at the Jamboree, so their iGEM program is continuous. The amount of time that they spent carefully choosing their project is obvious as they start early and flesh out the different aspects of their project ideas. In the end, the project they ended up choosing was closely tied to the lead contamination problems in their community. It was admirable to hear from a team that was trying to solve a problem that hit close to home.

Team: Colorado Boulder iGEM

Colorado Boulder's thorough answers helped to point out some answers that came up from multiple teams. Their project was limited by time frame and resources because in the end their project had to be feasible. It was also interesting to see how they felt more comfortable participating in the therapeutics track so that is the track that they were aiming to compete in. Therapeutics is a track that they participated in multiple past years. Additionally circumstances for the team further pushed them in that direction, ending with them choosing Antibody Switch.

Team: Copenhagen iGEM

Copenhagen's responses showed similar trends to other schools. It seems that there are a considerable amount of teams that take the time to brainstorm ideas for potential projects and use voting as a method of narrowing it down. Voting was how our own team chose our final project this year, so it was interesting to see the same processes at work in different parts of the world.

Team: Cairo University iGEM

Cairo's answers provided great insight into how their team focused on local problems for project inspiration. As a first year team, they were already at a lack of experience and they highlighted overall limited lab equipment in Egypt and lack of synthetic biology expertise. From the beginning their project was intended to be somehow targeted to Egypt, so they had to have thorough brainstorming sessions. In the end their project was heavily dependent on their local environment as they highlighted their real water crisis (which exists even though Egypt has the Nile).

Team: TUD iGEM

TUD's responses highlighted how not all projects have to be community based. They detailed how they saw a shortcoming in diagnosing heritable diseases and how it is closely linked to accessibility and affordability. So their team decided to tackle a problem that affects people across the world, as they had a more global focus. They did have more ideas that they wanted to proceed with in the beginning but experimental issues limited their project scheme.

Team: UGA iGEM

UGA's answers showed a strong regional impact as their project related to peanut production. Their PI was already familiar with plant pathology, so they combined the expertise of their team with the needs of their community. Connecting synthetic biology and local agriculture played to the strengths of their team. They knew how important peanut production was to Georgia and the potential to significantly enhance peanut production for their state.

Team: RHIT iGEM

RHIT's thorough answers gave us insight to multiple aspects of project brainstorming. Their brainstorm process initially allowed them to be as creative as possible, leading to a diverse set of possible project ideas. However the limited time span of iGEM and also their team's realistic work hours made some projects unfeasible. Their final project idea played to their team members' strengths, was feasible with their team situation, and also though initially based off of local problems -- could be more broadly applied across the world.

Team: UT Austin iGEM

UT Austin's responses were helpful because they helped us to understand what sort of responses to expect. We were able to talk to them in person about their responses and we were able to discuss the significance of their answers. Their project was based off of interests and experiences of their advisors because then they would be able to have the best mentorship. Additionally, they decided to build off of their 2015 team project. The solid foundation of their project made an obvious impact in the progress of their project as already in July they were testing parts from the iGEM kit with their functional burdenometer.

Team: Texas Tech

Texas Tech's responses were also discussed in person at the Texas iGEM meetup. Our discussion with Texas Tech mainly revolved around how they had additional hurdles in iGEM because they didn't have as many resources as other teams. They had limited advisors so that limited their project scope, even when they based it off of their PIs lab. Trying out novel experiments or having completely new projects were simply not feasible for them. Additionally, lack of equipment, funding, and intense computational experience were also limiting.

Team: UPRM

UPRM's responses highlighted the work that goes into being a new iGEM team. The UPRM team is the first iGEM team from the Caribbean, and unfortunately they do not currently have much of a synthetic biology experience. They had to compromise with a project that was possible with their lack of resources and experiences. So instead their team decided to tackle aspects of iGEM that they could work in and help their local community with. While the wetlab portion of their project may not be directly related to their local community, their human practices and engagement are targeted towards their community. With the future in mind, their team focused their efforts on promoting synthetic biology in their community.

Findings

Looking at the tracks that the teams we talked to competed in brought to attentions some interesting finds. There are multiple teams that show schools sticking to a track for multiple years.

The teams that we talked to had general minimal fluctuations but at times the changes were much larger. Many teams had years where there was a drastic team size drop but it is nice to see how that was not discouraging and the teams continue.

Observation 1: Small, new, or less well-funded teams may be limited in their choice of topic by several factors such as lack of expertise amongst the advisors which may drive them to look into local problems (e.g. WLC) or topics related to their PI’s area of research (e.g. UGA).

Observation 2: Duration is often a factor that limits the pursuit of more ambitious projects or biological systems which operate or grow on larger/slower timescales. This led to some teams choosing to continue previous projects (e.g. UT Austin) so as to be able to make more progress.

Observation 3: The number of completed surveys compared to requests (~25%) could be associated with the phrasing or formatting of our survey responses. The number of completed surveys was less than desired (50%) and subsequently resulted in a smaller sample size than we would have liked.

Observation 4: The purely qualitative nature of the survey reduced our ability to interpret the results meaningfully.

Future Actions

Team up! We encourage different iGEM teams to look into participation as joint teams between experienced teams or those with more resources and smaller teams or those with less iGEM experience. Joint teams would allow for the pursuit of more ambitious projects and give smaller teams the ability to tackle a larger range of problems. This would also contribute enormously to iGEM’s goal of ‘the advancement of synthetic biology, education and competition, and the development of an open community and collaboration” by fostering closer relationships between different teams while also ensuring that new teams obtain the mentorship required to advance iGEM and synthetic biology at their educational institutions. As such, we hope the iGEM foundation will be able to provide incentives that would promote the formation of such joint teams in the future too.

Upon browsing through the different survey responses, we also realized that our questions were not phrased in a way that would be conducive to data collection and analysis. To this end, we designed a new survey which allows for more quantitative responses and could be used in similar studies in the future. The questions in our new survey also appear simpler and should require less time to complete; it is our hope that this new format would encourage more teams to respond and increase the sample size of our data. We have attached our new survey below and hope that it can be utilized in the future.

We were also only able to sample a small group of teams due to our limited resources. We encourage the iGEM foundation to look into surveying a larger number of teams in the future to obtain a more complete picture of the motivations of different iGEM teams, which could allow the iGEM foundation to come up with more tailored ways of aiding different teams in their projects.