Team:Aalto-Helsinki/Questionnaire
Combining modeling and experimentation in iGEM projects
Main findings
Introduction
Materials and methods
Results
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 modellers and biologists. However, it was considered important to have experimentalists take part in model development to make sure that 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.
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 ensures 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 modellers 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 modellers. 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 it 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 University of Helsinki for their valuable help on 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.