Difference between revisions of "Team:Aalto-Helsinki/Questionnaire"
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<li><a href="#" data-scroll="introduction"><h3>Introduction</h3></a></li> | <li><a href="#" data-scroll="introduction"><h3>Introduction</h3></a></li> | ||
+ | <li><a href="#" data-scroll="main findings"><h3>Main findings</h3></a></li> | ||
<li><a href="#" data-scroll="materials and methods"><h3>Materials and methods</h3></a></li> | <li><a href="#" data-scroll="materials and methods"><h3>Materials and methods</h3></a></li> | ||
<li><a href="#" data-scroll="results"><h3>Results</h3></a></li> | <li><a href="#" data-scroll="results"><h3>Results</h3></a></li> | ||
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<h1>Combining modeling and experimentation in iGEM</h1> | <h1>Combining modeling and experimentation in iGEM</h1> | ||
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+ | <!-- Introduction below --> | ||
+ | <section id="introduction" class="active" data-anchor="introduction"> | ||
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+ | <h2>Introduction</h2> | ||
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+ | <p>Mathematical modeling is a key component of synthetic biology, serving as a crucial link between the idea and realization of an engineered biological system. In our project, modeling played a central role in 1) helping us focus our bioengineering efforts on the rate-limiting steps of our synthetic metabolic propane pathway and 2) giving us useful predictions on whether our concepts could work.</p> | ||
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+ | <p>As is often the case with interdisciplinary work, cooperation between modelers and biologists is not always easy, a problem that has also been identified by other iGEM teams such as <a href="https://2011.igem.org/Team:Grenoble/HumanPractice/identifying" target="_blank">Grenoble 2011</a>. We faced this challenge in our project, too. 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. <a href="http://www.sciencedirect.com/science/article/pii/S1369848610001159" target="_blank">It has even been said</a> 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.</p> | ||
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+ | <p>Whereas interdisciplinary cooperation <a href="http://books.google.fi/books?id=30R7BgAAQBAJ&lpg=PA1949&ots=kdviu0F3bM&dq=Collaboration%20in%20the%20New%20Life%20Sciences%2C%20Aldershot%3A%20Ashgate.&hl=sv&pg=PT142#v=onepage&q&f=false" target="_blank">has previously been studied</a> in professional research environments, 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> | ||
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+ | <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> | ||
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<li><p>What kind of roles do you have in your project?</p></li> | <li><p>What kind of roles do you have in your project?</p></li> | ||
<li><p>What were your expectations for combining modeling with biology? Was there something surprising?</p></li> | <li><p>What were your expectations for combining modeling with biology? Was there something surprising?</p></li> | ||
− | <li><p>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 | + | <li><p>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)) |
</p></li> | </p></li> | ||
<li><p>What challenges were there in terms of collaborating between fields?</p></li> | <li><p>What challenges were there in terms of collaborating between fields?</p></li> | ||
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<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. | + | <p>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.</p> |
<figure> | <figure> | ||
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<h4>Solutions to cooperation issues</h4> | <h4>Solutions to cooperation issues</h4> | ||
− | <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/> | + | <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/> |
<cite>- A physics student working mainly on modeling</cite> | <cite>- A physics student working mainly on modeling</cite> | ||
</blockquote> | </blockquote> | ||
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<i><h5>Working together</h5></i> | <i><h5>Working together</h5></i> | ||
− | <p>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.</p> | + | <p>In quite many teams, the cooperation was enhanced and the knowledge gap narrowed by taking the modelers to perform the experiments in the lab 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.</p> |
<i><h5>One person doing both modeling and experimentation</h5></i> | <i><h5>One person doing both modeling and experimentation</h5></i> | ||
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<h4>Integration of modeling and experimentation</h4> | <h4>Integration of modeling and experimentation</h4> | ||
− | <blockquote><i>“The choice of promoters, | + | <blockquote><i>“The choice of promoters, RBSs and plasmids depended greatly on the model data obtained.”</i><br/> |
<cite>- A 5th year biomedical engineering student</cite> | <cite>- A 5th year biomedical engineering student</cite> | ||
</blockquote> | </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 | + | <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 adjusted the protein degradation in their gene circuits according to the 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>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.</p> | <p>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.</p> | ||
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<p>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.</p> | <p>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.</p> | ||
− | <p> | + | <p>Though the tight timeframe of iGEM makes it difficult to get practical benefits from modeling, the expectations were met rather well. 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.</p> |
<h3>Approaches to organizing modeling</h3> | <h3>Approaches to organizing modeling</h3> | ||
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<p>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.</p> | <p>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.</p> | ||
− | <p>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 | + | <p>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 <a href="http://onlinelibrary.wiley.com/doi/10.1002/jez.b.22568/abstract;jsessionid=A0B2D3278EA21E0838489E9C6CA7B461.f04t02" target="_blank">recognised in professional groups</a>.</p> |
<h4>Cooperation</h4> | <h4>Cooperation</h4> | ||
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<h4>Challenges in cooperation</h4> | <h4>Challenges in cooperation</h4> | ||
− | <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 | + | <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 <a href="http://books.google.fi/books?id=30R7BgAAQBAJ&lpg=PA1949&ots=kdviu0F3bM&dq=Collaboration%20in%20the%20New%20Life%20Sciences%2C%20Aldershot%3A%20Ashgate.&hl=sv&pg=PT142#v=onepage&q&f=false" target="_blank">has also been recognised</a> to be a significant problem in professional research organizations. 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, as <a href="http://onlinelibrary.wiley.com/doi/10.1002/jez.b.22568/abstract;jsessionid=A0B2D3278EA21E0838489E9C6CA7B461.f04t02" target="_blank">MacLeod and Nersessian point out</a>. 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 <a href="http://books.google.fi/books?id=30R7BgAAQBAJ&lpg=PA1949&ots=kdviu0F3bM&dq=Collaboration%20in%20the%20New%20Life%20Sciences%2C%20Aldershot%3A%20Ashgate.&hl=sv&pg=PT142#v=onepage&q&f=false" target="_blank">has also been recognised</a> 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. 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 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> | <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 | + | |
+ | <figure style="float:right;margin-left:20px;"> | ||
+ | <a href="https://static.igem.org/mediawiki/2011/5/54/B_for_M.png" target="_blank"><img src="https://static.igem.org/mediawiki/2011/5/54/B_for_M.png" style="width:500px;"/></a> | ||
+ | </figure> | ||
+ | |||
+ | <figure style="float:right;margin-left:20px;margin-bottom:20px;"> | ||
+ | <a href="https://static.igem.org/mediawiki/2011/9/91/M_for_B.png" target="_blank"><img src="https://static.igem.org/mediawiki/2011/9/91/M_for_B.png" style="width:500px;"/></a> | ||
+ | <figcaption><i>Synthetic Biology for Modelers</i> and <i>Modeling for Biologists</i>, by <a href="https://2011.igem.org/Team:Grenoble/HumanPractice/developing" target="_blank">iGEM Grenoble 2011</a>.</figcaption> | ||
+ | </figure> | ||
+ | |||
+ | <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, as <a href="http://www.clic.gatech.edu/papers/ID%20engineering_Nersessian&Newstetter_%202013.pdf" target="_blank">pointed out by Nersessian et al</a>. Grenoble 2011 iGEM team has made <a href="https://2011.igem.org/Team:Grenoble/HumanPractice/developing" target="_blank">flyers</a> specifically for this purpose, for both biologists and modelers to learn the basics of the other field. 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, as <a href="http://onlinelibrary.wiley.com/doi/10.1002/jez.b.22568/abstract;jsessionid=A0B2D3278EA21E0838489E9C6CA7B461.f04t02" target="_blank">Macleod and Nersessian explain</a>. </p> | ||
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<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>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 modelers to discuss the modeling and answer biology-related questions. To enhance interaction, simply situating the modelers in the lab can be helpful | + | <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 <a href="http://onlinelibrary.wiley.com/doi/10.1002/jez.b.22568/abstract;jsessionid=A0B2D3278EA21E0838489E9C6CA7B461.f04t02" target="_blank">can be helpful</a>. 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 <a href="http://onlinelibrary.wiley.com/doi/10.1002/jez.b.22568/abstract;jsessionid=A0B2D3278EA21E0838489E9C6CA7B461.f04t02" target="_blank">have been seen</a> as more likely to be able to adapt to experimental contexts than vice-versa.</p> |
<h4>Team compositions</h4> | <h4>Team compositions</h4> | ||
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<p>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.</p> | <p>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.</p> | ||
− | <p>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 | + | <p>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 through their work. 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.</p> |
− | <p>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 | + | <p>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 <a href="http://www.clic.gatech.edu/papers/ID%20engineering_Nersessian&Newstetter_%202013.pdf" target="_blank">have also been noticed</a> to work as boundary agents in both industry and academia, as they have been trained to be interdisciplinary and integrative. 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.</p> |
<p>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.</p> | <p>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.</p> | ||
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<h2>References</h2> | <h2>References</h2> | ||
− | <p> | + | <p > |
<ol> | <ol> | ||
− | <li><p>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.</p></li> | + | <li><p>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. <a href="http://www.sciencedirect.com/science/article/pii/S1369848610001159" target="_blank">Online text.</a></p></li> |
− | <li><p>Calvert J. 2010. Systems biology, interdisciplinarity and disciplinary identity. Collaboration in the New Life Sciences, Aldershot: Ashgate.</p></li> | + | <li><p>Calvert J. 2010. Systems biology, interdisciplinarity and disciplinary identity. Collaboration in the New Life Sciences, Aldershot: Ashgate. <a href="http://books.google.fi/books?id=30R7BgAAQBAJ&lpg=PA1949&ots=kdviu0F3bM&dq=Collaboration%20in%20the%20New%20Life%20Sciences%2C%20Aldershot%3A%20Ashgate.&hl=sv&pg=PT142#v=onepage&q&f=false" target="_blank">Online text.</a></p></li> |
− | <li><p>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.</p></li> | + | <li><p>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. <a href="http://onlinelibrary.wiley.com/doi/10.1002/jez.b.22568/abstract;jsessionid=A0B2D3278EA21E0838489E9C6CA7B461.f04t02" target="_blank">Online text.</a></p></li> |
− | <li><p>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. | + | <li><p>Nersessian, Nancy J. and Wendy C. Newstetter. 2014. "Interdisciplinarity in Engineering Research and Learning". Cambridge Handbook of Engineering Education Research. J. Aditya and B. Olds. Cambridge: Cambridge University Press. <a href="http://www.clic.gatech.edu/papers/ID%20engineering_Nersessian&Newstetter_%202013.pdf" target="_blank">Online text.</a></p></li> |
</ol> | </ol> | ||
</p> | </p> | ||
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Latest revision as of 19:10, 14 September 2015