Explain your model's assumptions, data, parameters, and results in a way that anyone could understand.
Models and computer simulations can help us understand the function and operation of BioBrick Parts and Devices. Simulation and modeling are critical engineering skills that can contribute to project design or provide a better understanding of the modeled process. These processes are even more useful and/or informative when real-world data are included in the model. This award is for teams who build a model of their system and use it to inform system design or simulate expected behavior before, or in conjunction with, experiments in the wetlab.
Visit the Special Prizes page for more information.
Mathematical models and computer simulations provide a great way to describe the function and operation of Parts and Devices. Synthetic Biology is an engineering discipline, and part of engineering is simulation and modeling to determine the behavior of your design before you build it. Designing and simulating can be iterated many times in a computer before moving to the lab.
The section below is a separate, independently-written draft, produced by a team member outside the main documentation process. Kept here for reference and comparison; not yet fact-checked by team leadership.
Every modelled number elsewhere on this wiki comes from one place: a small kinetic simulation of PET → MHET → TPA + EG. The model is intentionally simple, on the reasoning that every additional parameter is a degree of freedom that can't be constrained without more wet-lab data.
Two coupled Michaelis–Menten layers — PETase (surface) and MHETase (cytoplasmic) — with a substrate-saturation term accounting for the solid PET surface, treated as quasi-constant on the timescale of the experiment.
| Symbol | Value | Source |
|---|---|---|
| kcat,P | 0.04 s⁻¹ | Tournier 2020 · WT PETase |
| KM,P | 132 µM | Austin 2018 |
| kcat,M | 0.6 s⁻¹ | Knott 2020 · MHETase |
| KM,M | 17 µM | Knott 2020 · MHETase |
| [EP] surface | 2 µM · effective | Estimated in-house |
The dominant parameter is kcat,P: a 2× change moves the predicted 14-day conversion from 87% to somewhere between 56% and 99%. KM is comparatively unimportant within an order of magnitude. The model would not survive kcat,P being off by 10×.
Three places this model is known to be wrong, left uncorrected because current bench data isn't precise enough to constrain the fix: PET surface area is treated as constant when it actually decreases as the polymer degrades; there's no mass-transfer term for the broth around the cell; and product inhibition by TPA on MHETase, which has been reported elsewhere, is ignored.
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