Difference between revisions of "Team:Aalto-Helsinki/Questionnaire"
(clarified categorization, added mention that they are 2014 teams to analysis) |
m |
||
Line 83: | Line 83: | ||
<ul style="list-style-type:disc"> | <ul style="list-style-type:disc"> | ||
<li><p>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</p></li> | <li><p>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</p></li> | ||
− | <li><p>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</p></li> | + | <li><p>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</p></li> |
<li><p>Both modelers and biologists need to understand the basics of the other field to be able to effectively collaborate.</p></li> | <li><p>Both modelers and biologists need to understand the basics of the other field to be able to effectively collaborate.</p></li> | ||
− | <li><p>Having experimentalists and modelers work close together is beneficial. One approach generally found successful is to have some biologists | + | <li><p>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.</p></li> |
<li><p>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</p></li> | <li><p>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</p></li> | ||
− | <li><p>Having one person do both modeling and experimentation alone is | + | <li><p>Having one person do both modeling and experimentation alone is one way to avoid collaboration issues, but works only in very small projects</p></li> |
<li><p>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.</p></li> | <li><p>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.</p></li> | ||
<li><p>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.</p></li> | <li><p>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.</p></li> | ||
Line 105: | Line 105: | ||
<p>As is often the case with interdisciplinary work, cooperation between modelers and biologists is not always easy, something we also noticed in our project. If we had understood the potential pitfalls and known good practices of this cooperation, we would have saved a lot of time and been able to achieve more in our project. One could even say that the development of synthetic biology depends on sociology, reflecting the necessity of creating an environment where scientists with different expertises can effectively work as a team (Calvert & Fujimura, 2011).</p> | <p>As is often the case with interdisciplinary work, cooperation between modelers and biologists is not always easy, something we also noticed in our project. If we had understood the potential pitfalls and known good practices of this cooperation, we would have saved a lot of time and been able to achieve more in our project. One could even say that the development of synthetic biology depends on sociology, reflecting the necessity of creating an environment where scientists with different expertises can effectively work as a team (Calvert & Fujimura, 2011).</p> | ||
− | <p>Whereas interdisciplinary cooperation has previously been studied in professional research environments (Calvert 2010), iGEM offers | + | <p>Whereas interdisciplinary cooperation has previously been studied in professional research environments (Calvert 2010), iGEM offers a unique possibility to study how students perceive and approach the challenges of interdisciplinary work. To specifically study the relationship of mathematical modeling and laboratory work in iGEM teams and gather information about pitfalls, good practices, and the integration of modeling and laboratory work we created a questionnaire and sent it to a number of teams. We also studied the expectations students had for modeling, how the modeling efforts were organized and how well modeling and experimentation was integrated in iGEM teams.</p> |
<p>Having finished the questionnaire, we found the share of respondents from a mathematical background surprisingly low. To find out how well our sample represents iGEM teams we looked up the majors of students from 84 iGEM teams from 2014. We also compared the results to compositions of professional synthetic biology research groups to see if there are any differences.</p> | <p>Having finished the questionnaire, we found the share of respondents from a mathematical background surprisingly low. To find out how well our sample represents iGEM teams we looked up the majors of students from 84 iGEM teams from 2014. We also compared the results to compositions of professional synthetic biology research groups to see if there are any differences.</p> | ||
Line 137: | Line 137: | ||
</ol> | </ol> | ||
− | <p>We kept the questionnaire open from | + | <p>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.</p> |
<h4>iGEM team compositions</h4> | <h4>iGEM team compositions</h4> | ||
− | <p>We went through iGEM teams from | + | <p>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.</p> |
<figure id="fig1" style="margin-bottom:3%;"> | <figure id="fig1" style="margin-bottom:3%;"> | ||
Line 150: | Line 150: | ||
<td><p>Molecular biology/cell biology/developmental biology, | <td><p>Molecular biology/cell biology/developmental biology, | ||
Biology/biological sciences, | Biology/biological sciences, | ||
− | Life | + | Life Sciences, |
Microbiology, | Microbiology, | ||
Neurobiology, | Neurobiology, | ||
Biochemistry, | Biochemistry, | ||
− | Chemical | + | Chemical Biology, |
Genetics, | Genetics, | ||
− | Plant | + | Plant Biology, |
Biomaterials, | Biomaterials, | ||
Agriculture, | Agriculture, | ||
Aquaculture, | Aquaculture, | ||
− | Environmental | + | Environmental Science, |
− | Marine | + | Marine Biology, |
Biomedicine, | Biomedicine, | ||
− | Biomedical | + | Biomedical Laboratory Science, |
Medicine, | Medicine, | ||
− | Health | + | Health Sciences, |
− | Microbiology and | + | Microbiology and Immunology, |
Biopharmacy, | Biopharmacy, | ||
Immunology</p></td></tr> | Immunology</p></td></tr> | ||
<tr><th><h3 style="margin-top:0;">Biotechnology</h3></th> | <tr><th><h3 style="margin-top:0;">Biotechnology</h3></th> | ||
<td><p>Biotechnology, | <td><p>Biotechnology, | ||
− | Applied | + | Applied Biology, |
− | Life | + | Life Science Engineering, |
Genomic Biotech, | Genomic Biotech, | ||
Bioengineering, | Bioengineering, | ||
− | Biochemical | + | Biochemical Engineering, |
− | Environmental | + | Environmental Engineering, |
Nanotech, | Nanotech, | ||
Engineering of Medical Biotechnology, | Engineering of Medical Biotechnology, | ||
− | Biomedical | + | Biomedical Engineering, |
− | Pharmaceutical | + | Pharmaceutical Engineering</p></td></tr> |
<tr><th><h3 style="margin-top:0;">Mathematics</h3></th><td><p>Mathematics, | <tr><th><h3 style="margin-top:0;">Mathematics</h3></th><td><p>Mathematics, | ||
Statistics</p></td></tr> | Statistics</p></td></tr> | ||
<tr><th><h3 style="margin-top:0;">Computer Sciences</h3></th><td><p>Computational Biology, | <tr><th><h3 style="margin-top:0;">Computer Sciences</h3></th><td><p>Computational Biology, | ||
− | Computer | + | Computer Science/Computational Science, |
Informatics, | Informatics, | ||
Bioinformatics, | Bioinformatics, | ||
Systems Biology, | Systems Biology, | ||
− | Information | + | Information Systems Engineering</p></td></tr> |
<tr><th><h3 style="margin-top:0;">Physics</h3></th><td><p>Physics, | <tr><th><h3 style="margin-top:0;">Physics</h3></th><td><p>Physics, | ||
− | Engineering | + | Engineering Physics, |
− | Statistical | + | Statistical Physics</p></td></tr> |
<tr><th><h3 style="margin-top:0;">Chemistry</h3></th><td><p>Chemical engineering, | <tr><th><h3 style="margin-top:0;">Chemistry</h3></th><td><p>Chemical engineering, | ||
Chemistry</p></td></tr> | Chemistry</p></td></tr> | ||
<tr><th><h3 style="margin-top:0;">Engineering</h3></th><td><p>Electrical engineering, | <tr><th><h3 style="margin-top:0;">Engineering</h3></th><td><p>Electrical engineering, | ||
− | Mechanical | + | Mechanical Engineering, |
− | Metallurgical | + | Metallurgical Engineering, |
− | Process | + | Process Engineering, |
− | Material | + | Material Engineering, |
− | Vehicle/ | + | Vehicle/Aerospace Engineering, |
− | Industrial | + | Industrial Engineering, |
− | Communication | + | Communication Engineering, |
− | Civil | + | Civil Engineering, |
Engineering</p></td></tr> | Engineering</p></td></tr> | ||
<tr><th><h3 style="margin-top:0;">Other</h3></th><td><p>Integrated sciences, | <tr><th><h3 style="margin-top:0;">Other</h3></th><td><p>Integrated sciences, | ||
Business, | Business, | ||
Economics, | Economics, | ||
− | Arts/ | + | Arts/Design/Painting, |
− | History of | + | History of Science and Technology, |
Psychology, | Psychology, | ||
Communications, | Communications, | ||
English/Languages, | English/Languages, | ||
Other, | Other, | ||
− | Industrial | + | Industrial Design</p></td></tr> |
</tbody> | </tbody> | ||
</table> | </table> | ||
Line 371: | Line 371: | ||
<p>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.</p> | <p>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.</p> | ||
− | <p>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.</p> | + | <p>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.</p> |
<p>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.</p> | <p>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.</p> | ||
Line 383: | Line 383: | ||
<i><h5>General meetings</h5></i> | <i><h5>General meetings</h5></i> | ||
− | <p>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 | + | <p>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.</p> |
<i><h5>Modeling meetings</h5></i> | <i><h5>Modeling meetings</h5></i> | ||
Line 392: | Line 392: | ||
<i><h5>One person doing both modeling and experimentation</h5></i> | <i><h5>One person doing both modeling and experimentation</h5></i> | ||
− | <p>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 lack of discussion.</p> | + | <p>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.</p> |
<h4>Integration of modeling and experimentation</h4> | <h4>Integration of modeling and experimentation</h4> | ||
Line 416: | Line 416: | ||
</figure> | </figure> | ||
− | <p>The division of the fields is gathered in figure | + | <p>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.</p> |
<h4>Composition of synthetic biology research groups</h4> | <h4>Composition of synthetic biology research groups</h4> | ||
− | <p>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 | + | <p>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.</p> |
<figure> | <figure> | ||
Line 455: | Line 455: | ||
<p>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.</p> | <p>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.</p> | ||
− | <p> Engaging the whole team in modeling was seen as difficult, perhaps due to the knowledge gap between | + | <p> 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.</p> |
<h4>Solutions for cooperation challenges</h4> | <h4>Solutions for cooperation challenges</h4> | ||
− | <p>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 | + | <p>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).</p> |
− | <p>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 | + | <p>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</p> |
− | <p>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 | + | <p>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).</p> |
<h4>Team compositions</h4> | <h4>Team compositions</h4> | ||
Line 493: | Line 493: | ||
<h2>Acknowledgements</h2> | <h2>Acknowledgements</h2> | ||
− | <p>We thank Tarja Knuuttila, Leena Tulkki, Rami Koskinen, Miles MacLeod, Tero Ijäs and Anita Välikangas from University of Helsinki for their valuable help | + | <p>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.</p> |
<!-- Acknowledgements above --> | <!-- Acknowledgements above --> |
Revision as of 12:14, 11 September 2015