Team:Aalto-Helsinki/Questionnaire
Main findings
Models are expected to aid in understanding factors that affect the biological system, in predicting outcomes of experiments, focusing wetlab efforts and figuring out if an idea can be realized at all
Biggest issues in collaboration between modelers and experimentalists are: lack of knowledge of the other field, lack of common terminology and differences in ways of thinking
Both modelers and biologists need to understand the basics of the other field to be able to effectively collaborate.
Having experimentalists and modelers work close together is beneficial. One approach generally found successful is to have some biologists involved in modeling to ensure models are connected to reality and useful for the project.
Regular team meetings for presenting and discussing progress and issues of every field take time, but ensure all team members stay informed and can voice their insights
Having one person do both modeling and experimentation alone is one way to avoid collaboration issues, but works only in very small projects
Students with a mathematical educational background are underrepresented in iGEM teams as compared to professional synthetic biology groups. On the other hand, there are relatively many biotechnology students taking part in iGEM.
While modeling is most often done by students with a background from mathematical sciences and experimentation by students with biological background, biotechnology students often stand somewhere in between, acting as mediators between the approaches.
Materials and methods
The questionnaire was built in Google Forms and contained the following questions, with each question except number 7 (multiple choice question) having an open text box for the answer:
What team are you in?
What is your field of studies?
How long have you been studying?
Is your team doing any modeling? If yes, is there an assigned group for this task?
What kind of roles do you have in your project?
What were your expectations for combining modeling with biology? Was there something surprising?
What guided your model construction? (Your own prior knowledge, a team member's prior knowledge, your own research (research articles etc.), advisor's prior knowledge, other (please specify))
What challenges were there in terms of collaborating between fields?
How did you approach the collaboration challenges? Did you feel your approach worked? Why, why not?
What kind of communication was there between modeling and laboratory practitioners? Were there any challenges in this communication?
How did the modeling efforts have an effect on the wetlab work? If there was no effect, why not?
Do you think that your models depict the real phenomena, or do you treat them rather as mere mathematical tools?
Now that you know better, is there something you would have done differently?
Comments
We kept the questionnaire open from July 22nd to August 6th for a total of 15 days. The questionnaire was first published on our Facebook page and Twitter account. We also advertised the questionnaire during the Nordic iGEM Conference. Furthermore, we contacted 7 iGEM teams through Facebook and 41 teams by e-mail and asked their individual team members to respond to our questionnaire. In total we personally contacted 48 iGEM teams: 22 from Europe, 7 from North America, 4 from Latin America and 15 from Asia.
iGEM team compositions
We went through iGEM teams from 2014 and collected their fields of study. We chose to study approximately one third of the 250 teams registered with geographically proportionate amount of teams. The majors of participants were derived from individual team wikis. Similar fields were categorized according to table 1.
Biology |
Molecular biology/cell biology/developmental biology, Biology/biological sciences, Life Sciences, Microbiology, Neurobiology, Biochemistry, Chemical Biology, Genetics, Plant Biology, Biomaterials, Agriculture, Aquaculture, Environmental Science, Marine Biology, Biomedicine, Biomedical Laboratory Science, Medicine, Health Sciences, Microbiology and Immunology, Biopharmacy, Immunology |
|---|---|
Biotechnology |
Biotechnology, Applied Biology, Life Science Engineering, Genomic Biotech, Bioengineering, Biochemical Engineering, Environmental Engineering, Nanotech, Engineering of Medical Biotechnology, Biomedical Engineering, Pharmaceutical Engineering |
Mathematics | Mathematics, Statistics |
Computer Sciences | Computational Biology, Computer Science/Computational Science, Informatics, Bioinformatics, Systems Biology, Information Systems Engineering |
Physics | Physics, Engineering Physics, Statistical Physics |
Chemistry | Chemical engineering, Chemistry |
Engineering | Electrical engineering, Mechanical Engineering, Metallurgical Engineering, Process Engineering, Material Engineering, Vehicle/Aerospace Engineering, Industrial Engineering, Communication Engineering, Civil Engineering, Engineering |
Other | Integrated sciences, Business, Economics, Arts/Design/Painting, History of Science and Technology, Psychology, Communications, English/Languages, Other, Industrial Design |
Results
Analysis of the respondents
Teams and their geographical location
We received in total 38 responses, 4 of which came after the deadline. 20 responses came from Europe, 12 from Asia and 3 from Latin America. 3 respondents did not specify their team. The responses came from 16 different teams: 8 from Europe, 6 from Asia and 2 from Latin America.
Team |
Responses |
TU Delft |
7 |
ITB Indonesia |
6 |
Stockholm |
3 |
Chalmers-Gothenburg |
3 |
ETH Zurich |
2 |
Aachen |
2 |
Brasil USP |
2 |
NCTU Formosa |
2 |
Edinburgh |
1 |
Birkbeck |
1 |
Uppsala |
1 |
UI-Indonesia |
1 |
HokkaidoU Japan |
1 |
UB Indonesia |
1 |
CGU Taiwan |
1 |
UFSCar Brasil |
1 |
Unspecified team |
3 |
Total |
38 |
Fields of study
The respondents mainly came from biological sciences. In total we received 39 answers to the field of study -question, with one respondent studying two fields. We categorized the different fields according to table 1, as we did for the 2014 iGEM teams. The share of different categories can be seen in figure 1.
Years studied
Typically a respondent had studied for 3 or 4 years, with these groups representing over half of the respondents. About 25% of respondents had studied for 1 or 2 years and 20% for 5 years or more. More detailed information can be seen in figure 2 (below).
Modeling organization
Out of the 16 teams that answered modeling was not a part of the project for only one team, represented by a single respondent. Of the 15 teams that are doing modeling, 12 have a specific group assigned for modeling and 3 teams don’t have one. Results are gathered in figure 3.
Roles of respondents
In total 14 respondents are taking part in the modeling efforts of their team. The division of fields is shown in figure 4. 24 respondents didn’t mention doing any modeling. Of the 14 people doing modeling 4 were doing it as their main task. These people were studying physics, physical engineering, computer science and chemistry. Of the 10 people that were involved in modeling, but not as their main task, 5 had background in biotechnology, 2 in biological sciences and one person had studied both. One part-time modeller had background in chemistry and one in management of technology. On the other hand, respondents not working on modeling mostly had bioscience background.
Analysis of the answers
Expectations for modeling
“Modeling could help to make your choices within biology more rational.”
- A 4th year life sciences student
Modeling was seen as a way to gain theoretical insight into the biological system: to explore the limits of a system, the space where it can work and its predicted dynamics. Some respondents hoped that modeling would give insight to whether their system could function at all. One respondent mentioned modeling as a way to reach a conclusion if experimentation is not possible. Many respondents expected modeling to give predictions about how the biological system functions.
It was hoped that these predictions would help with focusing the experimental work and thereby minimize the quantity of required experiments. For instance, modeling was seen as a way to explore the bottlenecks and critical parameters of a system. In practical terms it was hoped that it could give guidance on for example which RBS or promoter to choose to make the biological system function as desired.
Some hoped that the modeling could precisely represent the biological system, but more often respondents were rather sceptical of modeling biological systems due to lots of unknown variables and many aspects that are difficult to model.
Issues in cooperation
“It is sometimes difficult to communicate, however, more often than not I think collaboration between fields is just an advantage.”
- A 1st year Biomedicine student
Cooperation between modelers and people doing the experiments is a major challenge in applying modeling to synthetic biology. Very many respondents considered communication between these groups difficult. Three reasons to communication problems were mentioned multiple times.
Firstly, as modelers often have a mathematical background and experimentalists a background in life sciences, the knowledge gap makes cooperation difficult. Biologists have a better understanding of how life works, but on the other hand they have a hard time understanding modeling, whereas modelers are in the opposite situation. Especially respondents doing mostly modeling stated that understanding other fields sufficiently takes a lot of time.
Second difficulty mentioned was the different vocabulary used by modelers and experimentalists. For instance, two people may refer to the same experiment or concept with different names, creating confusion and making cooperation more difficult.
A third and according to some respondents the hardest barrier was that modelers and experimentalists have a different way of thinking, a different perspective.
Solutions to cooperation issues
“We have been dealing with this by talking with no hesitation, exchanging ideas and experiences and accepting that our knowledge completes each others.”
- A physics student working mainly on modeling
General meetings
To resolve the challenges in cooperation between modelers and experimentalists and enhance communication, teams had different methods. In some teams questions and problems of all fields were discussed as a team, which was also seen as a good way of getting all team members to better understand different fields. Some teams have weekly meetings where issues are presented and discussed, other teams have meetings each day. Thoroughly explaining models or the biological systems to people not very familiar with the question at hand was considered time-consuming, though.
Modeling meetings
Some teams have separate meetings for modeling. In these cases, having experimentalists take part in the modeling meetings and vice versa was seen as a good way to enhance communication. Sometimes instructors also take part in the meetings.
Working together
In quite many teams, the cooperation was enhanced and the knowledge gap narrowed by taking the modelers to perform the experiments in together with the biologists, in some cases only for a short time in the beginning of the project. Some teams have people who work both in the laboratory and in the modeling team with full-time modelers to answer their biology-related questions. This was also seen as a way to avoid communication problems between the modelers and the rest of the team. In some teams, the modelers and experimentalists work tightly together. One respondent mentioned that it would have been helpful to have had the experimentalists involved in the modeling right from the beginning.
One person doing both modeling and experimentation
In some cases, same people did both experiments and modeling, so there was no need for cooperation at all. Whether this is possible depends on the case: if a single person can tackle the question at hand, cooperation is not necessary. One respondent pointed out however, that while having the same person doing both experiments and modeling reduces communication issues, it can increase the likelihood of errors due to the lack of discussion.
Integration of modeling and experimentation
“The choice of promoters, RBS sites and plasmids depended greatly on the model data obtained.”
- A 5th year biomedical engineering student
Sufficient integration of modeling and experimental work done in the laboratory was considered difficult but important. About half the teams were able to get modeling results that affected the work done in the laboratory. As often was the expectation, modeling helped some teams in focusing their time and energy on certain designs. For instance, it helped teams in deciding what constructs to create and influenced the choice of promoters, RBS and plasmid backbones. Some teams applied protein degradation according to models they built. One respondent also noted that modeling helped in thinking about the possible influences in the system and in improving experimental setups. Some teams performed experiments to gain parameters for their models and adjusted their models according to experimental results.
When there was no effect or the effect was smaller than desired, it was often a question of time. Building a model takes time, but due to the tight timeframe of the iGEM competition, work in the laboratory needs to start early as well. Therefore, the model might provide results only at a too late stage for it to have any effect in the experiments. Many would have preferred to start the modeling earlier on, had it been possible. One respondent replied that the modeling will not have any significant effect, as their team is doing so little modeling due to lack of expertise. In some cases there was simply not enough time to adjust or validate a model using experimental data, often due to difficulties in performing the experiments successfully. Due to the timing of our questionnaire approximately 6 weeks before the end of the competition, some respondents answered that they do not yet know whether there will be an effect, but were nevertheless mostly optimistic.
In addition to the problems of cooperation, many teams mentioned having problems with finding information required for modeling, for instance different constants. One team had difficulties in finding relevant questions to answer with modeling. To overcome these problems, teams seeked help from outside the team, either from other iGEM teams or from supervisors and advisors. It was also mentioned that having a supervisor specialized in modeling would have been helpful.
Analysis of the iGEM team compositions
General team composition
We collected data from total of 84 teams that took part in iGEM 2014. 23 teams were from Europe, 28 teams from Asia, 5 teams from Latin America and 28 teams from North America. Our sample consisted of 1063 majors. The majors were categorized under Biology, Biotechnology, Mathematics, Computer Sciences, Physics, Chemistry, Other Engineering and Other majors. All the majors under each category are shown in table 1.
The division of the fields is gathered in figure 5. Biggest fields are biology and biotechnology with 48.5% and 26% of students respectively. The fields actively doing modeling according to our survey are a clear minority, with computer science at 6.8% and physics at 2.6% of iGEM participants. Mathematical sciences, mathematics, physics and computer science, make up a total of 11.2% of the fields.
Composition of synthetic biology research groups
Very few research groups have clearly outlined what the exact background of their members are, making it hard to compare with iGEM teams. Systems biology and synthetic biology group in Edinburgh and MIT synthetic biology groups had a total of 83 majors with 24 people left unspecified. Majors were grouped as with the iGEM team composition and are presented in figure 6. About half of the researchers came from biology and the second half from other sciences.
Discussion
Who does modeling?
Unsurprisingly, modeling appears to rather strongly rest on the shoulders of students of mathematical sciences. All the physics and computer science students that answered to our questionnaire were doing modeling as their main task. Respondents studying biological sciences are very little involved in modeling, but biotechnology students often do it in addition to other tasks, perhaps acting as mediators between modeling and experimentation.
Expectations for modeling and its results
The expectations students have for modeling appear to rather well meet the results they are getting from it. Modeling aids in getting theoretical insight of the biological system, understanding factors that influence the system and predicting the outcome of an experiment. Models help experimentalists focus their efforts and reduce the amount of experiments required and sometimes predict whether an idea is possible or reasonable to realize at all.
The expectations were met rather well, though the tight timeframe of iGEM makes it difficult to get practical benefits from modeling. Often the modeling team needs some time in the beginning to get familiar with biology and building the models itself takes a lot of time. Due to the tight schedule of the competition, the experiments also need to be started early on, so many decisions that modeling could have affected are already made when the modeling is able to catch up. When the models are ready, there can be little time left to adjust or validate the model using experimental data.
Approaches to organizing modeling
One person doing everything
In some iGEM teams the modeling challenges were approached by having the same person do both experimental work and modeling. This is an obvious way to avoid the difficulties in interdisciplinary communication. However, drawbacks to this approach were also recognised. It was mentioned that by having the same person do experimentation and modeling can more easily lead to errors due to the lack of discussion.
It was often very time-consuming for people with a biology background to learn even the basics of modeling and for modelers to learn basics of biology. Especially respondents doing mostly modeling reported the difficulty of learning other fields, perhaps pointing out that more often a person with a modeling background starts learning biology rather than biologists taking the time to learn modeling. If a person divides her or his time in learning and doing both, the level of understanding is inevitably lower than a specialized person could have. Therefore, having one person doing everything significantly limits what can be achieved within the project. Similar advantages and disadvantages of bimodal modelers have also been recognised in professional groups (MacLeod & Nersessian, 2014).
Cooperation
For more ambitious goals in applying modeling to synthetic biology and larger projects, it is necessary for more specialized people to work together. Rather than trying to do all the tasks themselves, people with a decent understanding of both biology and modeling could act as mediators between specialists of the two fields. However, as the responses indicate, this cooperation between specialists is often challenging.
Challenges in cooperation
The most significant challenge in cooperation appears to be communication, hindered by the knowledge gap, different vocabularies and different ways of thinking between modelers and experimentalists. Communication between modelers and biologists has also been recognised to be a significant problem in professional research organizations (Calvert 2010). Lack of knowledge means a lack of understanding, and not understanding what the other person is talking about obviously makes cooperation difficult. Without training or hands-on experience, modelers have problems understanding the constraints of experimental work and experimentalists difficulties in understanding why modelers need what they do (MacLeod & Nersessian, 2014). Lack of a common vocabulary and using different terminology to refer to the same concept creates confusion, contributing to the difficulties of cooperation. Differences in the way of thinking between students with a mathematical background and a biological background is considered by some to be the biggest obstacle in interdisciplinary cooperation. This problem has also been recognised in professional research environments, and is often referred to using the metaphor of language: the other field seems to “speak a different language”, implying a different way of thinking (Calvert, 2010). The same expression of speaking a different language can also be taken more literally, referring to technical words (e.g. “polymerase” or “stochastic model”) and thus to the two other problems in communication, lack of knowledge and common terminology.
Engaging the whole team in modeling was seen as difficult, perhaps due to the knowledge gap between modelers and biologists. However, it was considered important to have experimentalists take part in the model development to make sure that the modeling is connected to reality - if the model is disconnected from reality, it is of little use in the project.
Solutions for cooperation challenges
All common issues of cooperation iGEM teams faced seem to be solvable by taking the time to learn at least the very basics of the other field and with thorough communication. Only the essentials of the other field need to be learned to be successful (Nersessian et al. 2014). Besides studying textbooks of biology, modelers can for instance take some time to get familiar with the labwork in the beginning of the project. Likewise, it can be beneficial for biologists to get some hands-on experience in modeling. This approach has successfully been used also in professional research environments(MacLeod & Nersessian, 2014).
Regular meetings where progress and issues of every field are presented and discussed with the whole team are one option. These can take a lot of time, but ensure all team members stay informed and can voice their insights. Some teams hold meetings weekly, others daily. Brief daily meetings can be accompanied with more thorough weekly meetings
For facilitating communication, having the modelers and experimentalists working closely together is beneficial. This can be achieved by having the modelers take part in the experiments or the experimentalists work together with modelers to discuss the modeling and answer biology-related questions. To enhance interaction, simply situating the modelers in the lab can be helpful (MacLeod & Nersessian, 2014). Ideally, the experimentalists involved in modeling would spend some time to study the basics of modeling and take part in modeling meetings to complement the knowledge of the modelers. This helps modeling in taking into account all biologically relevant interactions and avoiding biologically unrealistic assumptions. In professional environments, on the other hand, modelers have been seen as more likely to be able to adapt to experimental contexts than vice-versa (MacLeod & Nersessian, 2014).
Team compositions
The division of fields in the 84 randomly chosen iGEM teams was similar to our survey, indicating that our respondents are a representative sample of iGEM participants.
Both professional synthetic biology research groups and iGEM teams have somewhat similar composition, with most of the team having biology backgrounds. However, professional groups have almost double the amount of mathematical fields (22% in professional research groups vs 11% in iGEM teams). Students and researchers from these fields usually do modeling, perhaps indicating that the role of modeling is more prominent in professional synthetic biology research groups.
Most of the mathematical majors in professional teams seemed to be traditional computer science with no biological background, whereas iGEM teams had majors that directly combined modeling and biology, such as systems biology or bioinformatics. It is of course likely that the computer scientists in synthetic biology had more or less learnt bioinformatics, systems biology, or other subspecialty by doing. The division may be caused by the fact that these fields are relatively young as separate majors. Thus, we may see more of them in the future within research as well.
Additionally, professional research groups have a lot less researchers with a biotechnology background (8%) compared to iGEM teams (26%). This could reflect the notion that technology and engineering students are less likely to work with academical research and more often work in companies. People with a biotechnology background appeared to work as mediators between biology and modeling. Bioengineers have also been noticed to work as boundary agents in both industry and academia, as they have been trained to be interdisciplinary and integrative (Nersessian et al. 2014). The fairly large amount of biotechnology students in iGEM teams could therefore improve communication between modelers with a mathematical background and experimentalists. This requires of course that there are people with a mathematical background in the team to begin with, which surprisingly often is not the case.
Based on the answers above we conclude that more diverse teams help with integration of modeling and experimental work. The proportion of mathematical students in iGEM teams is relatively low in comparison with professional research teams. To better incorporate the modeling aspect of synthetic biology in iGEM projects, it might be preferable to include more students with a strong background in mathematical sciences in teams. On the other hand, including biotechnology students could enhance the cooperation between modelers and experimentalists.
Future prospects
Studying individual teams to see how the composition affects the depth of their modeling efforts and the degree to which models are integrated to experimental work could be interesting. This could help find differences between teams of different compositions, for instance if teams with no students with biotechnology students as “mediators” have their modeling more detached from the experimental side of the project.
Acknowledgements
We thank Tarja Knuuttila, Leena Tulkki, Rami Koskinen, Miles MacLeod, Tero Ijäs and Anita Välikangas from the University of Helsinki for their valuable help with this study. We also thank the following 16 teams participating in iGEM 2015 for their answers: TU Delft, ITB Indonesia, Chalmers-Gothenburg, Stockholm, Aachen, Brasil USP, ETH Zurich, NCTU Formosa, Birkbeck, CGU Taiwan, Edinburgh, HokkaidoU Japan, UB Indonesia, UFSCar Brasil, UI-Indonesia and Uppsala.
References
Calvert J, Fujimura JH. 2011. Calculating life? Duelling discourses in interdisciplinary systems biology. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences.
Calvert J. 2010. Systems biology, interdisciplinarity and disciplinary identity. Collaboration in the New Life Sciences, Aldershot: Ashgate.
MacLeod, M., & Nersessian, N. J. (2014). Strategies for Coordinating Experimentation and Modeling in Integrative Systems Biology. J. Exp. Zool. (Mol. Dev. Evol.), 9999, 1-10.
Nersessian, Nancy J. and Wendy C. Newstetter. 2014. "Interdisciplinarity in Engineering". Cambridge Handbook of Engineering Education Research. J. Aditya and B. Olds. Cambridge: Cambridge University Press. (in press)