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Latest revision as of 17:36, 29 October 2017
Team Hong Kong - HKUST
Collaborations
SYSU Software iGEM team visualizations
Our team began to scrape team data and abstracts from the iGEM database using a web scraper, with the aim of identifying common themes between different teams, so as to create a tool that could potentially help teams find others with similar topics in the future. We wish to further improve the database by converting the csv file, which contains a lot of keywords, into a visualization where the output will be a visualized diagram that is easier to search, more creative and more user-friendly. Because our modelling team is not expert in making visualization using JavaScript, we decided to post our collaboration request on iGEM collaboration page to seek help from iGEM teams to improve the visualization for interested iGEM teams to use in the future.
SYSU software team was enthusiastic about this collaboration. We have achieved our final goal for this finished product, which is to put the Network Visualization on our wiki page!
This is how we make the visualization happen: CLICK ME
Modeling and characterization collaboration with CUHK
For modelling collaboration, our modelling team bidirectionally cooperated with CUHK's modelling team in the early summer. We contributed two Python scripts for CUHK team. One of them was for simulating and plotting an ODE-based model using Euler's method. The other was for simulating and plotting a stochastic model using a Gillespie algorithm. Both of these scripts were used for them to model the proportion of active and inactive toehold switches in their project.
In the opposite direction, we received from CUHK iGEM team a Python interface implemented with Cython that would allow us to call functions from a library called ViennaRNA which is written in C. Since it can be quite difficult to use functions between different languages like Python and C, and CUHK team’s interface greatly simplified our workload for modelling. We used this interface to estimate the dissociation constant for the antisense mRNA and transcript mRNA in our model.
For characterization collaboration, the CUHK team helped us with characterisation to further verify our Sensor Constructs (with Antisense RNA) using GFP assay. Analysis of the results is provided in our Sensing Module page. We greatly appreciate their help!
