Difference between revisions of "Team:Aalto-Helsinki/Questionnaire"
(fixed navigation) |
(+figures, citation) |
||
Line 120: | Line 120: | ||
<h4>Fields of study</h4> | <h4>Fields of study</h4> | ||
<p>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 grouped the fields identically to the way we grouped the data from studying team compositions of 2014 iGEM teams (see table 1). The share of different categories can be seen in figure 1.</p> | <p>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 grouped the fields identically to the way we grouped the data from studying team compositions of 2014 iGEM teams (see table 1). The share of different categories can be seen in figure 1.</p> | ||
− | figure 1 | + | |
+ | <figure> | ||
+ | <img src="https://static.igem.org/mediawiki/2015/b/b6/Aalto-Helsinki_Figure_1_respondents_by_field_of_study.png" style="max-width:100%;" /> | ||
+ | <figcaption><b>Figure 1:</b> Respondents by field of study</figcaption> | ||
+ | </figure> | ||
<h4>Years studied</h4> | <h4>Years studied</h4> | ||
<p>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).</p> | <p>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).</p> | ||
− | figure 2 | + | <figure> |
+ | <img src="https://static.igem.org/mediawiki/2015/3/3b/Aalto-Helsinki_Figure_2_respondents_by_years_studied.png" style="max-width:100%;" /> | ||
+ | <figcaption><b>Figure 2:</b> Respondents by years studied</figcaption> | ||
+ | </figure> | ||
<h4>Modeling organization</h4> | <h4>Modeling organization</h4> | ||
<p>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 as gathered in figure 3.</p> | <p>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 as gathered in figure 3.</p> | ||
− | Figure 3 | + | |
+ | <figure> | ||
+ | <img src="https://static.igem.org/mediawiki/2015/9/9c/Aalto-Helsinki_Figure_3_modeling_organization_in_teams.png" style="max-width:100%;" /> | ||
+ | <figcaption><b>Figure 3:</b> Modeling organization in teams.</figcaption> | ||
+ | </figure> | ||
<h4>Roles of respondents</h4> | <h4>Roles of respondents</h4> | ||
<p>In total 14 respondents are taking part in the modeling efforts of their team. The division of fields is shown in figure 4. 23 respondents didn’t mention doing any modeling and one response was unclear. 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, 7 had background in biotechnology, three in biological sciences and one in management of technology. On the other hand, respondents not working on modeling mostly had bioscience background.</p> | <p>In total 14 respondents are taking part in the modeling efforts of their team. The division of fields is shown in figure 4. 23 respondents didn’t mention doing any modeling and one response was unclear. 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, 7 had background in biotechnology, three in biological sciences and one in management of technology. On the other hand, respondents not working on modeling mostly had bioscience background.</p> | ||
− | Figure 4 | + | |
+ | <figure> | ||
+ | <img src="https://static.igem.org/mediawiki/2015/f/fb/Aalto-Helsinki_Figure_4_respondents_by_field_of_study_and_involvement_in_modelling.png" style="max-width:100%;" /> | ||
+ | <figcaption><b>Figure 4:</b> Questionnaire respondents by their fields of study and involvement in modeling.</figcaption> | ||
+ | </figure> | ||
<h3>Analysis of the answers</h3> | <h3>Analysis of the answers</h3> | ||
<h4>Expectations for modeling</h4> | <h4>Expectations for modeling</h4> | ||
− | “Modeling could help to make your choices within biology more rational.” | + | |
− | - A 4th year life sciences student | + | <blockquote><i>“Modeling could help to make your choices within biology more rational.”</i><br/> |
+ | <cite>- A 4th year life sciences student</cite> | ||
+ | </blockquote> | ||
<p>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.</p> | <p>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.</p> | ||
Line 147: | Line 164: | ||
<h4>Issues in cooperation</h4> | <h4>Issues in cooperation</h4> | ||
− | “It is sometimes difficult to communicate, however, more often than not I think collaboration between fields is just an advantage.” | + | <blockquote><i>“It is sometimes difficult to communicate, however, more often than not I think collaboration between fields is just an advantage.”</i><br/> |
− | - A 1st year Biomedicine student | + | <cite>- A 1st year Biomedicine student</cite> |
+ | </blockquote> | ||
<p>Co-operation 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>Co-operation 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> | ||
Line 159: | Line 177: | ||
<h4>Solutions to cooperation issues</h4> | <h4>Solutions to cooperation issues</h4> | ||
− | “We have been dealing with this by talking with no hesitation, exchanging ideas and experiences and accepting that our knowledge completes each others | + | <blockquote><i>“We have been dealing with this by talking with no hesitation, exchanging ideas and experiences and accepting that our knowledge completes each others.”</i><br/> |
− | - A physics student working mainly on modeling | + | <cite>- A physics student working mainly on modeling</cite> |
+ | </blockquote> | ||
<h5>General meetings</h5> | <h5>General meetings</h5> | ||
Line 177: | Line 196: | ||
<h4>Integration of modeling and experimentation</h4> | <h4>Integration of modeling and experimentation</h4> | ||
− | “The choice of promoters, RBS sites and plasmids depended greatly on the model data obtained.” | + | <blockquote><i>“The choice of promoters, RBS sites and plasmids depended greatly on the model data obtained.”</i><br/> |
− | - A 5th year biomedical engineering student | + | <cite>- A 5th year biomedical engineering student</cite> |
+ | </blockquote> | ||
<p>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.</p> | <p>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.</p> | ||
Line 191: | Line 211: | ||
<p>We collected data from total of 84 teams with 23 teams 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].</p> | <p>We collected data from total of 84 teams with 23 teams 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].</p> | ||
− | + | ||
+ | <figure> | ||
+ | <img src="https://static.igem.org/mediawiki/2015/a/ae/Aalto-Helsinki_Figure_5_fields_of_igem_participants_by_category.png" style="max-width:100%;" /> | ||
+ | <figcaption><b>Figure 5:</b> Fields of iGEM participants by category (n = 1063).</figcaption> | ||
+ | </figure> | ||
<p>The division of the fields is gathered in figure 6. 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> | <p>The division of the fields is gathered in figure 6. 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> | ||
Line 198: | Line 222: | ||
<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 7. About half of the researchers came from biology and the second half from other sciences.</p> | <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 7. About half of the researchers came from biology and the second half from other sciences.</p> | ||
− | + | ||
+ | <figure> | ||
+ | <img src="https://static.igem.org/mediawiki/2015/7/78/Aalto-Helsinki_Figure_6_fields_of_synthetic_biology_researchers_by_category.png" style="max-width:100%;" /> | ||
+ | <figcaption><b>Figure 6:</b> Fields of synthetic biology researchers by category (n = 84).</figcaption> | ||
+ | </figure> | ||
<!-- Results above --> | <!-- Results above --> |
Revision as of 01:32, 3 September 2015