Difference between revisions of "Team:Paris Bettencourt/Collaborations"
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| − | <p>Survey in Epicollect5 - filtered.</p> | + | <p> Survey in Epicollect5 - filtered.</p> |
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| − | <p><b>iGEM Networks of communication</b></ | + | <p><b>iGEM Networks of communication</b></p> |
| − | <p>Three main networks were analyzed:</ | + | <p>Three main networks were analyzed:</p> |
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<li>The Slack interaction network is built from replies, mentions, invitations and reactions to messages. </li></br> | <li>The Slack interaction network is built from replies, mentions, invitations and reactions to messages. </li></br> | ||
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<li>In all networks, links are weighted by the number of interactions, and node size reflects the level of interaction (sum of link weights). </li></br> | <li>In all networks, links are weighted by the number of interactions, and node size reflects the level of interaction (sum of link weights). </li></br> | ||
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| − | <p><b>iGEM Networks of communication</b></ | + | <p><b>iGEM Networks of communication</b></p> |
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<li>Community detection was run on the whole aggregated network collapsing information from the 3 sources, resulting in two communities.</li></br> | <li>Community detection was run on the whole aggregated network collapsing information from the 3 sources, resulting in two communities.</li></br> | ||
<li>Complementary data has been collected using proximity sensors, and is still awaiting further analysis.</li></br> | <li>Complementary data has been collected using proximity sensors, and is still awaiting further analysis.</li></br> | ||
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<p><b>Summary</b></br> | <p><b>Summary</b></br> | ||
| − | <p>The three networks give a complementary picture of the interaction networks. In particular, hubs in one network are not necessarily hubs in another (showcasing communication channel preference).</ | + | <p>The three networks give a complementary picture of the interaction networks. In particular, hubs in one network are not necessarily hubs in another (showcasing communication channel preference).</p> |
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<li>The Google group data, which is the most active communication channel, reveals a variable engagement between a core active group and a more peripheral one. It also reveals a strong collaborative nature of the team with a high density of interactions.</li></br> | <li>The Google group data, which is the most active communication channel, reveals a variable engagement between a core active group and a more peripheral one. It also reveals a strong collaborative nature of the team with a high density of interactions.</li></br> | ||
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<li>Comparison with the sensor based approach will allow to reveal a finer understanding of the underlying process</li></br> | <li>Comparison with the sensor based approach will allow to reveal a finer understanding of the underlying process</li></br> | ||
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Revision as of 03:11, 18 October 2018
The Cell-Free Interlab Study
Cell-free expression platforms allow the production of proteins from DNA without live cells by making use of modified cell lysates. Their application is increasingly popular in synthetic biology where they allow fast iteration of design-build-test cycles of genetic parts and gene circuits. Now is the time to prepare for the widespread adoption of cell-free methods by iGEM teams.
Following into the footsteps of the iGEM interlab study, we prepared a pilot-scale interlab study to test the variation of cell-free expression systems between labs. This was made possible by our generous sponsor Arbor Biosciences, who provided the myTXTL cell-free expression system to our team and others.
Results
Preparation of the cell-free interlab protocol
We selected a GFP expressing plasmid (BBa_I20270) from the iGEM 2018 distribution kit and used pTXTL-p70a-deGFP as an optimized plasmid for high cell-free expression of GFP. We prepared a standard protocol to distribute to participating teams. Our protocol makes use of fluorescein as a fluorescence standard, allowing measurements to be compared across labs.
Distribution of the cell-free interlab protocol
We recruited team GIFU iGEM from Japan to participate in this study with us. Thanks to GIFU iGEM for their patience and great work!
Interlab variation in cell-free expression yields
As observed from figure 1 (A) and (B) the high expression plasmid (pTXTL-p70a-deGFP) gives comparatively similar magnitude of fluorescence, indicating a low variability of the cell-free expression system. However we do see a difference in the magnitude of low expression GFP plasmid (BBa_I20270), which could be a result of difference in the way we cloned, isolated and prepared our plasmids.
Conclusion
Future interlab studies should make use of cell-free
Our mini-interlab experiment indicated that cell-free systems can be considered reliable when optimized and could give reproducible results upon following standardized protocols. However, we would propose that more teams participate in this experiment as it is in the case of iGEM-interlab. This would confirm the efficiency of cell-free expression system and the technical variability when executed via various teams with good reproducibility.
Collaboration with iGEM Tübingen 2018 team
In order to test deimmunization tool BERT in the context of a meaningful application, > iGEM Tübingen 2018 team < collaborated with us. Their project focused on the usage of antimicrobial peptides fused together with self-assembling scaffolding proteins as a potential alternative to classical antibiotic treatment.
Dissecting Science
As a pilot team we were honored to collaborate with Marc Santolini in his project “Dissecting Science”. In this project, he aims to study the making and the learning of Science in situ by using the iGEM scientific competition as a model. iGEM presents a unique opportunity to bring together groups of scientific thinkers in a competitive, collaborative, and scientifically rich way. The participating teams share their project and their lab book in open wiki websites.
The study of these wikis has allowed to pinpoint the role of student interactions in the success of teams. Yet, the dynamic nature of these interactions in the lab has remained elusive. How do students collaborate? How are subgroups formed? What is the frequency of interactions with mentors/PIs? How do these interactions lead to better learning (skill spreading), productivity (BioBricks/project size), creativity (project uniqueness) and success in the competition (medals and prizes)? To answer these questions, his team investigated for the first time the interactions in the lab for the our team as a pilot, with the end goal to reach out to a large number of teams. Such interaction was mapped by: 1) using MIT Rhythm badges in collaboration with Oren Lederman and Jordan Reedie from MIT Media Lab; 2) analysis of team interaction through the Gmail, Slack and self-report survey.
Interaction throught the Slack
Interaction throught the Google Group
Survey in Epicollect5
Interaction throught the Slack - filtered.
Interaction throught the Google Group - filtered.
Survey in Epicollect5 - filtered.
iGEM Networks of communication
Three main networks were analyzed:
iGEM Networks of communication
Summary
The three networks give a complementary picture of the interaction networks. In particular, hubs in one network are not necessarily hubs in another (showcasing communication channel preference).
paris-bettencourt-2018@cri-paris.org 
