Modelling Overview

Modeling as an iterative process: The design-build-test cycle

One of the core aspects of synthetic biology is the iterative design-build-test-learn cycle, where the information gained from one experiment is used to design the subsequent experiments.

Within the learn stage of the design-build-test cycle, models can be used as a way to interpret data, and, more crucially, make predictions about the system that can be used in the design stage of the cycle. Modeling is itself an iterative process, where the predictions from the model are used in experiments. Constraints on time have put a limit on the number of iterations of the design-build-test cycle that we could perform. However, by using modelling we have been able to make predictions on how the expressed proteins of our biobricks would behave in an experimental setting. Furthermore, we have been able to feed the model with some data extracted from our own experiments.

Two models - same input

In our project, we had two different modelling objectives.

The first objective was to apply enzyme kinetics in order to predict the optimum amount of time for the blood-sample to be in contact with our ScAvidin-linker-LacZ part (BBa_2355313), or in other words, the incubation time.

We did this by calculating the rate of cleavage of our substrate by different snake venoms, and thereby calculated the expected concentration of β-galactosidase being eluded into the a secondary reaction chamber, containing ONPG. We would expect the use of β-galactosidase as a reporter molecule to lower the detection time of our device, compared to if we use a colorimetric reporter, such as AmilCP.

The second objective, was to model the data from our substrate screening assay. The goal was to fit the data in order to find central parameters such as reaction rate, and compare those parameters across the 360 different substrates that we have assayed.