Team:Imperial College/Partnership
Partnership
Our partnership with Hamburg started early on in our project, when we were developing our modelling starter pack. We realised during the German iGEM meet-up that our project goals may be compatible in some ways. They had a very specific
design that they needed to get working and then optimise the performance of, and we were trying to build a platform to help people optimise their genetic constructs by making the build process more efficient. They were also looking
to model their system, but had limited experience on the team. We had a modelling mentorship programme for 2020 iGEM teams. We therefore felt there was a huge opportunity for a mutually beneficial partnership.
Full SOAP Lab Test Case
The wet lab that we were set to conduct our work in was barred to students after cases continued rising in the UK, leading us to a crisis on the validation of our software and our Tryptophan optimisation project. While we managed to
find space in a private lab through the generous sponsorship of Opencell, it required us to source and transport labware and reagents, leading to weeks of set-up time late in the year. Hamburg were fortunately equipped with an
Opentrons robot and kindly offered to validate that our software works in their lab.
We held sessions where they tested our SBOL file design features and gave us their opinions on the use of SBOL as a standard, which helped us realise more ways that people may be looking to specify their intended design, and that this
may be somewhat different to the way things get assembled through our software. For example, their first instinct in the tool design was to put all their BioBricks parts into one design, which was not reflective of the first step
of the BioBrick assembly they were putting together (shown in Figure above). We split up their BioBricks assembly design so that each one would create the appropriate assembly and edited our BioBricks script generation to be more
restrictive on BioBricks design choices.
When Hamburg helped us to test the script generation itself on Opentrons, we additionally identified potential sources of contamination in the way our assembly protocol ran and forced
us to find a way to strike a balance between alleviating errors in volume pipetting and wasting material through dead volume. This specifically occurred in the assembly stage of mixing DNA and reagents, where reagents were pipetted
into wells that already contained DNA, leading us to switch up the order of the pipetting steps. This reduced tip usage and also prevent unwanted DNA contamination if pipette tips were retained between mixes.
Not only did this
present immense value to our software tool itself, but proving that another iGEM team could implement our pipeline in an afternoon made an incredible case for the trustworthiness, usability, and user-friendliness of our software.
After our own validations, this was the first true step to bringing our software into the real world and open-source synthetic biology community.
Modelling mentorship
As Hamburg planned out the molecular biology of their project, their inexperience with the modelling of genetic circuits led them to seek our help. They were one of the five teams who participated in our modelling mentorship program.
See our Collaborations for more details. We helped them to continuously improve their model's predictions and their understanding of how their design would work.
In our first meeting, they presented their project and the dynamics of their system. We then gave a tutorial on the essential basics of modelling in Synthetic Biology. We first described their system using a flow diagram, illustrating
transcription and translation, the interactions between different molecules and degradation. We then elaborated on how they could use Mass Action kinetics, or more generally a Deterministic modelling framework, to model this system.
Finally, We followed up with some bespoke python code which could simulate their system using arbitrary constants.
Below is an example of the graphs which this code can produce:
We also gave them a prototype of the Introduction to Mathematical Modelling in Synthetic Biology document to provide more in-depth discussion and guidance on the theory and application of modelling.
The
goal of this initial meeting was to provide a solid foundation for them to build more complexity on. For example, in this initial model, we suggested assuming the ribozyme cleaving process was a simple, instantaneous reaction,
not a dynamic process. Through additional research, they could (if they saw fit) add complexity and accuracy to their model by integrating this process.
In a follow up meeting, we discussed compartmentalisation, as in their
system, they have two compartments with a partially permeable membrane between them. We suggested using a simplified version of Fick's law to model this.
In being our mentees, we were able to test our Introduction to Modelling
package on them. Our live calls acted as test tutorials, which we recorded later in the year. They also provided feedback on the document, and aided in it's development. It was thanks to their query about compartmentalisation that
we determined diffusion modelling should be added as a sub-section.
