This year at iGEM Marburg, we decided to incorporate the AI and robot revolution into our cutting-edge Synthetic Biology research. With our OT2 pipetting robot that we won last year and advanced machine learning methods, our objective is to design automated protocols that will accelerate research in Synthetic Biology. This is realized in two ways namely by delegating mindless works to pipetting robots and by achieving a high degree of reproducibility. The latter being a point that has been a common complained in Synthetic Biology and can be mitigated by standardization, which will be very welcomed by the community (for more details see Integrated Human Practices). We envision a future of Synthetic Biology, where people exchange robot protocols on top of text description of an experiment to ensure a high degree of reproducibility. We believe that our approach of infusing the best of computing and robotic world into Synthetic Biology will elevate the field even further.
For a proof-of-concept, we concentrated on the hugely popular Golden Gate cloning method; more specifically on some crucial parts of it such as plasmid purification and colony picking. These aspects are currently the bottleneck of the whole process and there is a big demand from the community to relieve this (for more details see Integrated Human Practices). Moreover, we also applied advanced mathematical and statistical methods to further assist our wet-lab colleagues such as by evaluating the genome of UTEX 2973 in order to discover suitable gene integration sites that enable gene-insertion without disrupting the normal function of the organism. Our work extends even further to utilize cutting-edge bioinformatic tools to design useful terminators and their integrating sites, which have been long coveted by the community working with cyano-bacterias (also see Integrated Human Practices for more details).
In our plasmid purification project we designed a workflow that enables the utilization of Promega Wizard® MagneSil® plasmid purification product with OT-2. For this we needed to overcome the challenge to incorporate products from different manufacturers such as Opentrons magnetic module and Qinstrument shaker into our protocol. We achieved this by designing custom-made module holders with our 3D-printer. Another challenge is to create a flexible protocol that is up- and down-scalable i.e. with more or less samples amount ranging from 1 to 48 per run. All of which are implemented without human intervention in-between.
Colony picking is one of our toughest project due to its complexities, which requires sophisticated approach to manoeuvre. The first challenge is to collect sufficient data to train our algorithm, which we overcame with the help of the community via a colony picture competition. The competition was a big success, where we collected more than 300 valuable pictures that can be used for the training of our algorithm. We then had to research to find the most fitting machine learning algorithm that will enable high adoption rate from the community accessible to as many computers as possible (such as the Raspberry Pi). The algorithm of our choice is Faster R-CNN which has little computing cost so that it can be expanded into Raspberry Pi, and is available through the well supported, open source Google’s TensorFlow machine learning framework. Finally, the colony detected by the AI can be translated into a OT-2 movement via in-house algorithm conceived by our team.
Lastly, our team also designed a Graphical User Interface (GUI) that will allow biologists to easily design their own golden gate protocol for the OT-2. We understand that if we want to bridge the gap between forefront biology research and technology, we have to make it intuitive for every technical lay person. That is why we created the GUI which will also help with our vision of standardization.
In summary, the automation lab has helped elevating iGEM Marburg to another level by infusing high precision method of advanced robotics, mathematics, and machine learning to the workflow of our biology team. Our vision is to create a new trend of protocol exchange to kick start a world of biology research with the highest degree of reproducibility. As a proof-of-concept for our vision, we addressed some existing bottlenecks in biological workflows such as colony picking or plasmid purification using cutting edge approaches from fields outside of biology. We also used advanced mathematical, statistical, and bioinformatic tools to address problems that our wet lab faced; thus exhibiting the highest level of interdisciplinary research. Lastly aligned with the iGEM philosophy, all of our work will be published as open-source upon which other research labs can build. Our code and 3D designs will be available in a Github repository, which will be publicly available online. We envision that our protocols and approaches to be a pioneering work for a world of highly reproducible Synthetic Biology experiment.