Introduction
Overall, our team's DNA assembly strategy has shown experimental results that are basically in line with our expectations, but there is still a lot of space for improvement. For example, our hardware system functions are not yet comprehensive, and there is still a certain distance from fully automated construction. So in the next year of iGEM, we will continue to optimize the hardware. At the same time, the generalizability of YeastFab can be further explored. As our team originally came to work with the SZPT-China team, we believe that an efficient DNA assembly strategy can facilitate the development of metabolic engineering, and efficient DNA assembly strategies are expected to help other iGEMers optimize metabolic pathways faster, easier, and more efficiently.
Machine Learning
High-quality, large-capacity and low-deviation data can be obtained by hardware, which is the most reliable for machine learning.
There are many reports that the strength of the promoter could affect metabolites. We have established a promoter library and we can rely on such an efficient DNA assembly strategy to enrich a large number of high-precision construction of promoter combinations by machine learning, and characterize the pathways of different promoter combinations. The linear relationship corresponding to the actuator combination and the characterisation results one by one is input into the neural network for machine learning, and the optimal starter combination is the optimisation project of the application and metabolic pathway of YeastFab technology.
Hardware Upgrading
In the hardware section, we will further debug and upgrade our prototype for the further improvement. The existing prototype cannot complete the pipette with high precision for the lacking of time. We will use higher-precision stepper motors in the robotic arm module and frame module to meet the requirements of higher-precision pipette. And large action groups will be refined to be more efficient and simplify operations. What’s more, we will also add visual recognition components to the robotic arm to increase applicability, and use electric pipette guns to increase the automation and convenience of our hardware.
Open Source Community
In the future, we will also organize our hardware construction strategy into a detailed operation manual. It includes not only how to build a hardware the same as ours, but also how to use this hardware for construction experiments in synthetic biology. Through the manual, we can better promote our hardware to meet up with different requirements . Whether an iGEM team or synthetic biology enthusiasts can obtain solutions or inspirations from our manuals to solve problems.
In the future, we hope to rely on this hardware platform and YeastFab construction strategy to create a cross-species and global part libraries for iGEMers or synthetic biology enthusiasts around the world to facilitate more efficient and convenient DNA assembly strategy.
