https://2022.igem.wiki/goethe-frankfurt/
Similar to our project, it is focused on the detection of cadmium waste and the remediation/recycling of cadmium ions. However, with regard to the implementation of the system, the installation of filtration systems and the installation of UV fail-safe devices in the sewage systems of industrial production plants would be ideal for ensuring the safety of such devices. We used the kill switch to make sure it was safe.
https://2022.igem.wiki/xjtlu-china/
Although their project can handle more heavy metal ions, their application scenario is in the water environment, while our project's application scenario is agricultural land. They thought about solutions to the impact on the natural environment. Including whether the bacteria have a niche in the natural environment and cause some environmental uncontrollable mutations to occur. And our solution is a kill switch.
https://2022.igem.wiki/edinburgh-uhas-ghana/
Like the previous group, their bioremediation device isolates heavy metals through metallothionein-mediated binding, which is a functional and novel part of biofilm system design. But their group can also be used for PET biodegradation. The scope of treatment is not limited to heavy metals. In contrast, we designed an autonomous living material synthesizing system consisting of engineered cells with genetic circuits that synthesize nanomaterials.
https://2022.igem.wiki/rec-chennai/
Their project is to remove the metal cadmium, which will be introduced into the reactor via a biological carrier. After adding phosphatase substrate, acid phosphatase will be dephosphorylated. The available free phosphate ions will bind to cadmium in the wastewater and form metal phosphate. Metal phosphates are insoluble in aqueous solutions and lead to biological precipitation of cadmium ions, so cadmium can be easily removed from industrial emissions. The content of our group includes not only removal but also detection.
Through modeling, we have elucidated the feasibility of utilizing the T7 RNAP system for amplifying biological signals. We have discovered that under conditions of constant biological negative feedback strength, the positive feedback of T7 RNAP can be inhibited. If subsequent experiments yield suboptimal amplification results, it may be due to negative feedback within the transcription process itself. However, we have also found that by appropriately increasing the translation rate, the amplification effect of T7 can be significantly enhanced, thereby overcoming the inhibitory effects of negative feedback. This work will provide valuable insights for future teams working on enhancing biological signal amplification. (You can click [here](link to the Model page) to access more detailed information about our model.)
Furthermore, we have prepared a simplified usage guide for Colab-Alphafold, which can be referenced by teams needing to model proteins in the future:
- Enter your amino acid sequence in the column marked with red number 1, replacing the default amino acid sequence.
- Click the button marked with red number 2 to initiate the protein modeling process.
This usage guide will assist your team in protein modeling using Colab-Alphafold.
Click "Run All," which is labeled as option red 3.
