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| <article> | | <article> |
| <p style="text-align: justify; margin-bottom: 1em;"> | | <p style="text-align: justify; margin-bottom: 1em;"> |
− | This year at iGEM Marburg, we decided to incorporate the AI and robot revolution into our cutting-edge | + | This year at iGEM Marburg, we decided to incorporate the AI and robot revolution into our cutting-edge Synthetic Biology research. |
− | Synthetic Biology research. With our OT2 pipetting robot that we won last year and advanced machine learning
| + | With the Opentrons OT-2 pipetting robot that we won last year and advanced machine learning methods, our objective was to |
− | methods, our objective is to design automated protocols that will accelerate research in Synthetic Biology.
| + | design automation protocols that accelerate research in Synthetic Biology. This was realized in two ways, namely by delegating |
− | This is realized in two ways namely by delegating mindless works to pipetting robots and by achieving a high
| + | mindless works to pipetting robots and by achieving a high degree of reproducibility. The latter being a point that has been |
− | degree of reproducibility. The latter being a point that has been a common complained in Synthetic Biology and
| + | a common complained in Biology and can be mitigated by standardization, which already generated great feedback from the community |
− | can be mitigated by standardization, which will be very welcomed by the community (for more details see
| + | (for more details see <a style="padding: 0" |
− | Integrated Human Practices). We envision a future of Synthetic Biology, where people exchange robot protocols
| + | href="https://2019.igem.org/Team:Marburg/Human_Practices">Integrated Human Practices</a>). We envision a future of Synthetic Biology, where people exchange robot |
− | on top of text description of an experiment to ensure a high degree of reproducibility. We believe that our
| + | protocols on top of text description of an experiment to ensure a high degree of reproducibility. We believe that our approach |
− | approach of infusing the best of computing and robotic world into Synthetic Biology will elevate the field
| + | of infusing the best of computing and robotic world into Synthetic Biology will elevate the field even further. |
− | even further.
| + | |
| </p> | | </p> |
| <p style="text-align: justify; margin-bottom: 1em;"> | | <p style="text-align: justify; margin-bottom: 1em;"> |
− | For a proof-of-concept, we concentrated on the hugely popular Golden Gate cloning method; more specifically on | + | 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
| + | some crucial parts, of it such as plasmid purification and colony picking. These aspects currently pose a major |
− | bottleneck of the whole process and there is a big demand from the community to relieve this (for more details
| + | challenge for a comprehensive automation of the cloning process and therefore create a great demand from the community. |
− | 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 <a style="padding: 0"
| + | |
− | href="https://2019.igem.org/Team:Marburg/Human_Practices">Integrated Human Practices</a> for more
| + | |
− | details).
| + | |
| </p> | | </p> |
| <p style="text-align: justify; margin-bottom: 1em;"> | | <p style="text-align: justify; margin-bottom: 1em;"> |
− | In our plasmid purification project we designed a workflow that enables the utilization of Promega Wizard® | + | In our plasmid purification project, we designed a workflow that enables the utilization of Promega Wizard® MagneSil® |
− | MagneSil® plasmid purification product with OT-2. For this we needed to overcome the challenge to incorporate
| + | plasmid purification product with the Opentrons OT-2. For the successful implementation of this step, it was essential |
− | products from different manufacturers such as Opentrons magnetic module and Qinstrument shaker into our
| + | to overcome the challenge to incorporate products from different manufacturers, such as Opentrons Magnetic Module and |
− | protocol. We achieved this by designing custom-made module holders with our 3D-printer. Another challenge is
| + | QInstrument D-30T elm shaker into our protocol. We achieved this by designing and printing custom-made hardware adapters |
− | to create a flexible protocol that is up- and down-scalable i.e. with more or less samples amount ranging from
| + | to guarantee stable and reproducible labware. We have also made it our aim to make OT-2 protocols more dynamic, |
− | 1 to 48 per run. All of which are implemented without human intervention in-between.
| + | which allows easy customization even for non-programmers. Specifically, it allowed us 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 fully automated without human intervention in-between steps. |
| </p> | | </p> |
| <p style="text-align: justify; margin-bottom: 1em;"> | | <p style="text-align: justify; margin-bottom: 1em;"> |
− | Colony picking is one of our toughest projects due to its complexities, which requires sophisticated approach | + | Colony picking is one of the toughest steps to automate due to its complex nature. |
− | to manoeuvre. The first challenge is to collect sufficient data to train our algorithm, which we overcame with
| + | After we have tested several methods such as hough circle transformation from OpenCV for the initial colony detection |
− | the help of the community via a colony picture competition. The competition was a big success, where we
| + | we discovered that the problem is even harder than we’ve originally thought. To address this issue, we talked to several experts |
− | collected more than 300 valuable pictures that can be used for the training of our algorithm. We then had to
| + | from the field of labautomation and computer vision, which led us to the decision to implement the colony detection using a |
− | research to find the most fitting machine learning algorithm that will enable high adoption rate from the
| + | convolutional neural network method. It was clear to us that we needed a method that was highly flexible, could run on as |
− | community accessible to as many computers as possible (such as the Raspberry Pi). The algorithm of our choice
| + | many machines as possible and that would ensure a fast and accurate detection of the naturally small colonies. |
− | is Faster R-CNN which has little computing cost so that it can be expanded into Raspberry Pi, and is available
| + | After several weeks of research, we decided to use the state-of-the-art Faster R-CNN method which has little computing |
− | through the well supported, open source Google’s TensorFlow machine learning framework. Finally, the colony
| + | cost and is available through the well supported, open source TensorFlow machine learning framework. |
− | detected by the AI can be translated into a OT-2 movement via in-house algorithm conceived by our team.
| + | As with any AI approach, the quality and quantity of the training data is of the utmost importance. |
| + | We overcame the data collection issue with the help of the incredible iGEM community via a colony picture competition. |
| + | The competition was a big success, where we collected more than 300 valuable pictures that were used for the training |
| + | of our algorithm. Finally, the colony detected by the AI were translated into robot movement via an in-house algorithm conceived by our team. |
| </p> | | </p> |
| <p style="text-align: justify; margin-bottom: 1em;"> | | <p style="text-align: justify; margin-bottom: 1em;"> |
− | Lastly, our team also designed a Graphical User Interface (GUI) that will allow biologists to easily design | + | Lastly, our team designed a Graphical User Interface (GUI) that allows biologists to easily design their own golden |
− | their own golden gate protocol for the OT-2. We understand that if we want to bridge the gap between forefront
| + | gate protocol for the OT-2. We understand that if we want to bridge the gap between forefront biology research and |
− | biology research and technology, we have to make it intuitive for every technical lay person. That is why we
| + | engineering, we have to make it intuitive for every technical lay person. That is why we created the GUI which will |
− | created the GUI which will also help with our vision of standardization.
| + | also help with our vision of standardization. |
| </p> | | </p> |
| <p style="text-align: justify; margin-bottom: 1em;"> | | <p style="text-align: justify; margin-bottom: 1em;"> |
− | In summary, the automation lab has helped elevating iGEM Marburg to another level by infusing high precision | + | In summary, the automation lab has helped elevating iGEM Marburg to another level by infusing high precision method |
− | method of advanced robotics, mathematics, and machine learning to the workflow of our biology team. Our vision
| + | of advanced robotics, mathematics, and machine learning to the workflow of our biology team. Our vision is to create |
− | is to create a new trend of protocol exchange to kick start a world of biology research with the highest
| + | a new trend of protocol exchange to kick start a world of biology research with the highest degree of reproducibility. |
− | degree of reproducibility. As a proof-of-concept for our vision, we addressed some existing bottlenecks in
| + | As a proof-of-concept for our vision, we addressed some existing bottlenecks in biological workflows such as colony picking |
− | biological workflows such as colony picking or plasmid purification using cutting edge approaches from fields
| + | or plasmid purification using cutting edge approaches from fields outside of biology and thus exhibiting the highest level |
− | outside of biology. We also used advanced mathematical, statistical, and bioinformatic tools to address
| + | of interdisciplinary research. Lastly aligned with the iGEM philosophy, all of our work will be published as open-source |
− | problems that our wet lab faced; thus exhibiting the highest level of interdisciplinary research. Lastly
| + | upon which other research labs can build. Our code and 3D designs will be available in a Github repository, |
− | aligned with the iGEM philosophy, all of our work will be published as open-source upon which other research
| + | which will be publicly available online. We envision that our protocols and approaches to be a pioneering work for a |
− | labs can build. Our code and 3D designs will be available in a Github repository, which will be publicly
| + | world of highly reproducible Synthetic Biology experiment. |
− | available online. We envision that our protocols and approaches to be a pioneering work for a world of highly
| + | |
− | reproducible Synthetic Biology experiment.
| + | |
| </p> | | </p> |
| </article> | | </article> |