Collaborations
Sharing and collabhttps://2017.igem.org/oration are core values of iGEM. We encourage you to reach out and work with other teams on difficult problems that you can more easily solve together.
IISc Collaboration
Their team was trying to build a precise and time effective way to measure the cell count of yeast cultures. They wanted to collect a huge amount of data to train a machine learning algorithm that they had designed for counting these cells. Collecting such huge amount of data means growing hundreds of yeast cultures,collecting samples every hour and then imaging it under the microscope on hemocytometer. It is a tedious task to be done alone by a team. Thus, with this collaboration our team was expected to generate images for the algorithm to analyze by monitoring one growth curve for Saccharomyces cerevisiae.
An introduction to machine learning can be found here: https://www.sas.com/en_us/insights/analytics/machine-learning.html
We were asked to follow the following protocol-
Mentioned below is the protocol that we followed in which we have made certain modifications to the above protocol-
DAY 1: Primary Inoculum
- In a sterile hood, inoculate a colony of S.cerevisiae in 5 mL of YPD/YPAD( 2 replicates). (Since we didn’t have the lab strain of S.cerevisiae and this experiment needed only quantitative data for the machine learning algorithm ,we used common baker’s yeast for the culture.) Comment-This was something exciting!
- Incubate the culture at 30⁰C,overnight, at 180 RPM.
DAY 2: The Growth Curves
a) MAKING THE SECONDARY INOCULUM
- Add 2.5 mL of primary inoculum to 247.5 mL of YPD/YPAD in a 1L flask in sterile environment.
- Swirl flask gently.
- Take‘0’th time point sample for both replicates and store it at 4 degrees.
- Seal mouth of flask with a cotton plug, incubate at 30⁰C and 180 RPM.
- Take samples every one hour for the growth curve for 16 hours.(Comment-We literally spent the entire night in the lab)
b) TAKING MEASUREMENTS
Here are some suggestions for projects you could work on with other teams:
- Improve the function of another team's BioBrick Part or Device
- Characterize another team's part
- Debug a construct
- Model or simulating another team's system
- Test another team's software
- Help build and test another team's hardware project
- Mentor a high-school team