# Interdisciplinarity

## Introduction

Interdisciplinarity can be loosely defined as the merging of different components from more than one discipline (area of study) [1-2]. It is different from “multidisciplinary”, where people from different disciplines work for the same goal, but do not communicate or exchange ideas.

Interdisciplinarity is believed to facilitate creative breakthroughs and provide new perspectives [1]. For example, drug discovery has benefitted from both chemistry and biology, by integrating knowledge from both areas [3].

We also discovered interdisciplinarity in SMORE. Our cell sorter is a fusion of biology, engineering and design, made by our biologist and product designer. This made us think: how important is interdiciplinarity in synthetic biology? Can we make synthetic biology more interdisciplinary, to facilitate creativity and innovation?

Our human practices start by exploring interdisciplinarity in synthetic biology. We conducted a skill exchange survey to find out the challenges in interdisciplinary collaboration. To study the interdisciplinary status of synthetic biology, we asked if more diverse teams perform better in iGEM? The results of both were thought-provoking, leading to our work in accessibility.

## Skill Exchange Survey

We began our investigation into interdisciplinarity with a simple question: how beneficial and difficult is it to work with someone from another background? In a skill exchange survey administered to our team and our iGEM collaborators, we developed a series of questions targeted to members involved in a “skill exchange”. We defined “skill exchange” as an event where one member taught another in an area previously untrained for the latter. For example, one of our biologist brought our product designer into the lab for training in basic protocols, including miniprep and agarose gel electrophoresis.

Overall, trainees were surprisingly positive about the learning experience. While they would often appear uneasy when approaching with unfamiliar work, they often reported the communication with the trainer was straightforward, and the trainer’s expectations were reasonable.

Particularly, a well-defined protocol has been identified to greatly help the trainee. The protocol provides clear and consistent terminology for both parties, and it gave a reference that the trainee can depend on, especially when they forget a task or technical terms. The trainers also reported the protocol aided them to perform demonstrations, which allowed the trainee to follow in a logical and precise manner.

Another aspect that the participants reported as beneficial was previous cooperation experience. While it may seem obvious, the results clearly pointed out when both parties were already friends or past collaborators, they find communication easier. In other words, practice makes perfect. Even if communication is initially difficult, it is worthwhile to continuously ironing out the kinks.

Nonetheless, as the survey identified, difficulties in collaboration remain. A common theme was that communication became difficult when too many technical terms were used by the trainer. Naturally, every discipline develops jargon. And despite the reported attempts of trainer to minimise their use of technical language, trainees still frequently complained that it hindered their understanding. Therefore, it is likely interdisciplinary collaborators were already aware of the drawback of technical language, but it should still be consciously and deliberately controlled.

Interestingly, there was a disconnect between trainer and trainee not before, but after the skill exchange. The last question of our survey asked both parties if the trainee can repeat the work independently. In every questionnaire, the answer from the trainer, with varying amounts of added qualifications, was positive. However, every trainee reported they would feel uncomfortable without guidance. Therefore, our questionnaire highlighted a disconnect in confidence. To illustrate, an experienced biologist deemed miniprep trivial, but every non-biologist that was taught in miniprep reported they liked further assistance to master the process.

Therefore, it is crucial to be reminded communication and collaboration are not one-time events. Instead, experts should consider them a long-term endeavour that requires multiple sessions before learners can fully impart the skill and knowledge.

[Skill exchange surveys from Bulgaria]
[Link for Skill exchange surveys from Edinburgh]
[Link for Skill exchange surveys from Israel]

## Systematic Review of Previous iGEM Teams

Given interdisciplinarity is thought to benefit academic research, investigated the status of interdiciplinarity in synthetic biology, more specifically, in iGEM. iGEM is open to students from all disciplines and is interdisciplinary by nature. For example, human practices and modelling represent social sciences and mathematical sciences, respectively. Moreover, iGEM indirectly qualifies projects in terms of medals and prizes, providing a model system to measure the benefit of interdiciplinarity.

To measure the interdisciplinarity of past iGEM teams, we recorded the discipline of each iGEM participant from 2013 to 2015. It is noteworthy that a diversity in disciplines does not always imply interdisciplinarity. Even in a diverse team, members may not exchange in skills or closely cooperate. But, diversity is a close and practical measure. The measurement of interdiciplinarity is correlated to their tracks, and medals to observe the relationship between interdisciplinarity and performance in iGEM.

## Measurement of Interdisciplinarity

Interestingly, the measure of interdiciplinarity is a product of interdisciplinarity itself. Diversity of disciplines is analogous to biodiversity, which ecologists have been studying. Here, we use an ecology-inspired interdisciplinarity index: Rao-Stirling index, to quantify the following aspects of interdiciplinarity [4]:

• Variety: how many different disciplines are within each iGEM team?
• Balance: are participants of each discipline of similar proportions?
• Disparity: how different are the disciplines in the iGEM team? E.g. the difference between linguistics and synthetic biology is far greater than that between cell biology and synthetic biology.

We chose the Rao-Stirling index due to its incorporation of all three aspects and its well-known uses in social sciences [5-6]. Moreover, Rao-Stirling index, similar to its ecological counterpart Rao’s quadratic entropy, behave mathematically in a fashion closely resembling the human intuition of diversity [7]. For example, the index is very sensitive to the addition of a highly different discipline and an increase in overall range of values covered.

Rao-Stirling Index: $$D = \sum _{ij,i \neq j} d_{ij}p_{i}p_{j}$$

where $D$ is the interdisciplinarity measure. $p_i$ and $p_j$ are proportions of discipline $i$ and discipline $j$ in the team, respectively. $d_{ij}$ is the difference between discipline $i$ and discipline $j$, measured in Euclidean distances. (Note: some versions of Rao-Stirling Index include parameters $\alpha$ and $\beta$ [6]. But, they are inapplicable to this study and are not discussed here.)

The measurement of disparity ($d_{ij}$) is less straightforward than variety $(i, j)$ and balance $(p_i, p_j)$. We devised a simple framework based on the Biglan's Axes, a well-established categorisation of disciplines, to semi-quantify each discipline in the following three qualities [8]:

• Foundational – Applicational: is the knowledge gained of pure academic interest (e.g. philosophy), or of applicational value outside academia (e.g. software engineering)?
• Hard - soft: if a paradigm exists to be followed (e.g. engineering) or if qualitative discussion is the only requirement (e.g. philosophy)?
• Life - non-life: is the discipline concerned with living organisms (e.g. biology) or is the discipline unconnected and unapplied directly to any living organisms (e.g. software engineering)?

We score each discipline from 1 to 7 for each of the three qualities. Euclidean distance $(d_{ij})$ is calculated by using the scores as three-dimensional coordinates. For example, a subject with scores 3, 6, 7 would have a coordinate (3, 6, 7) in a (x, y, z) format. We then used the data to calculate a diversity value for each team.

## Data Collection and Analysis

It should be noted that we have collected and analysed the data in the following way:

• iGEM teams with an ambiguous or incomplete record on disciplines of team members were excluded from the analysis
• iGEM teams that received a blocked medal were excluded from the analysis, as a blocked medal does not correlate with the quality of the project
• Datasets from different years were analysed separately, as the award criteria were not consistent throughout 2013-2015

We respect the achievements of every iGEM team and we promise that the sole purpose of the collection of data was to understand interdisciplinarity in synthetic biology. Data was collected from publicly available iGEM team websites.

## Results

Distribution of discipline diversity is grouped by medals (Gold, Silver, Bronze and None; Figure 1). Disappointingly, no significant difference in interdisciplinarity was detected between medal winners in any year (p > 0.1 for all combinations; ANOVA).

This was surprising as the interdisciplinary that benefitted on our team was not reflected in the data. Why is that? Here are some possibilities:

• While interdisciplinary may theoretically facilitate innovation and skill exchange, practical difficulties in communication and cooperation remain a great obstacle, as reflected from the skill exchange survey. The benefits were offset by poor integration of a diverse skillset.
• A diversity in discipline fails to capture the exchange of skill and knowledge within the cooperation – the essence of interdisciplinarity.
• iGEM is an interdisciplinary competition with a core of synthetic biology. Thus, a focus in biologists and engineers in team composition is often required for most tracks in iGEM. The optimal interdisciplinarity was a balance, rather than a complete diversity.

These results were thought-provoking, as we expected the potential of interdisciplinarity can be fully realised in iGEM. This expresses a dire need to improve the integration of interdisciplinarity in iGEM.

How can we better promote diversity and innovation in iGEM? We went back to our skill exchange survey to address the problems of interdisciplinary collaboration. We start by making SMORE a tool accessible to researchers from all backgrounds and disciplines, encouraging an influx of perspectives and ideas.

Take a look at how we did this on the Accessibility page.

Below are the codes for the interdisciplinarity analysis. The code is written in iPython, which needs special software for viewing. We have provided a guide on viewing and working with our iPhyton code. We also provided the text in .txt file but please note the content may have changed. Similarly, we have included the data of past iGEM teams in an excel file. Finally, we have also included a word document where scores along each Biglan Axis are defined.

Our iPython code for the analysis
Our guide to work with iPython
Our code for the analysis in .txt. file
Excel Data for the analysis
Definitions of Biglan Axes scores

# References

[1] Klein, J.T. 1990. Interdisciplinarity: History, Theory and Practice. Detriot: Wayne State University Press.

[2] Nissani, M. 1997. Ten Cheers for Interdisciplinarity: The Case for Interdisciplinary Knowledge and Research. The Social Science Journal 34(2):201–216.

[3] Drews, J. 2000. Drug Discovery: A Historical Perspective. Science 287(5460):1960–1964.

[4] Stirling, A. 2007. A general framework for analysing diversity in science, technology and society. Journal of the Royal Society Interface 4:707–719.

[5] Porter, A.L. and Rafols, I. 2009. Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scienometrics 81(3):719–745.

[6] Rafols, I. and Meyer, M. 2010. Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience. Scientometrics 82(2):263–287.

[7] Schleuter, A.D., Daufresne, M., Massol, F., Argillier, C., Schleuter, D., Daufresne, M., Massol, F. and Argillier, C. 2010. A user’s guide to functional diversity indices. Ecological Monographs 80(3):469–484.

[8] Biglan, A. 1973. The characteristics of subject matter in different academic areas. Journal of Applied Psychology 57(3):195–203.