In synthetic biology, modeling is a powerful tool that uses theoretical models and computational approaches to predict, improve and further understand experiments. This year, modeling turned out to be one of the crucial cornerstones of our project due to the cancelled lab time.
Over the course of the project, it became increasingly clear that we wouldn’t be able to test out our project in the laboratory, which meant for us to adapt to the current situation and focus on different aspects of our project such as the modeling part. In this context, we built a total of three distinct models to gain further insights into the underlying biochemistry of our wastewater treatment approach:
We used the Rosetta Commons Software developed by Baker Lab as well as the computational power of the Lichtenberg high-performance-computer at the TU Darmstadt to predict enzyme structures, test their stability and predict enzyme-ligand-interactions[1].We wrote a python program based on the work of B. Qin et al. to simulate the growth mechanics and -conditions of our biofilm in cooperation with the modeling team from iGEM Hannover as well as an ODE-based MATLAB-model to conceptually represent the functionality of our kill switch[2,3].
Since we weren’t able to include any self-generated data into our models this year, we hope that future iGEM teams can be inspired to use and fill them with life by expanding on them and implementing their own parameters. Our biofilm model can be used to describe biofilms built by other bacteria than B. subtilis simply by utilizing the values that describe those best. This also holds true for the kill switch model, which can be adapted to different biofilm-related systems if given the right data.
To make it short, through our modeling work we were able to predict that the most important aspects of our project which are listed below will work as intended and we layed the ground work for further optimizations.
The described models can be further examined on the following pages:
Click the monitors to get to different parts of our modeling.
We defined how diclofenac most likely binds to the laccase CotA We were able to define a starting point for enzyme optimization We predicted a structure for the esterase EreB We predicted structures for the TasA-CotA as well as TasA-EreB fusion proteins and proved their stability We provided a software tool for the prediction of growth, density and stability of biofilms We simulated the growth kinetics of a B. subtilis biofilm We created a model for the simulation of the comXQPA system, which can be used for the optimization for our kill switch
We defined how diclofenac most likely binds to the laccase CotA