Our team's formation was quite unique this year in that the recruitment efforts were entirely the result of a student-led initiative started at the beginning of the year, all thanks to the Imperial College synthetic biology
society SynBIC. We came together for the first time in early January and found the selection process had produced a team not only with diverse backgrounds and degree areas, but also with creative talents and cross-disciplinary
interests. In numbers, we were 3 bioengineers, 3 biochemists, 2 physicists and 1 design engineer. Our initial ideas revolved around cutting edge topics such as viral vectors production, modular metabolic engineering, and phase
separation (using intrinsically disordered regions) for compartmentalisation within cells.
After the onset of the pandemic, our focus started shifting more towards a computational project quite organically, as we had always
wanted to incorporate combinatorial testing using Opentrons into our project. Some reshuffling of the team resulted in 3 new recruits with a computational background to drive our nascent design-to-robot program pipeline. During
this stage we outlined the core values and aims that would guide us throughout the project, namely accessibility, communication, and validation.
Accessibility is key in our tool, as there are many budding software tools
that are abstracting away the programming scripts for liquid handlers and emphasise dynamically defining a protocol through an interface, which are however still out of reach or practical use for even leading academic research
labs. The simplicity of our open-source pipeline brings automation directly into the hands of people lacking the technical capabilities or time to run automation equipment in their lab. Our incorporation of SBOL-based designs
as the input is unique to our automation pipeline and encourages the communication of data through SBOL. Being an application of SBOL, we further support the adoption of SBOL as a standard by integrating it into a pipeline.
Lastly, software written for biologists often does not take into consideration the needs of the scientist and cuts short on some crucial details. This is why we validated our web pipeline on our own work, simulated builds of
our real project on Tryptophan optimisation, and partnered up with another iGEM team to run our pipeline and learn from their experiences.
Enough about our project. Meet the team below!