Team:ESBS-Strasbourg/Results/Modelling

1. Introduction
2. Behavioral model
3. Signal flow model
4. Conclusion

To automate the process of creation of new synthetic bio-systems an interesting point is to develop a specific design flow like in microelectronics. One of the main assets in microelectronic design flow for digital systems is the ability to describe a system at different abstraction levels.

For our system we focus the modeling at two different level of abstraction. The first one introduced is a high-level model and then the second one is a low-level model. For each model we show the simulation results and as conclusion we discuss the potential of the purposed models.

As in digital electronics, multiple abstraction levels can be defined. The highest consists in the function of the system. This model, also called logical model, because it is based on logical equations, is a high level model. It is interesting to use high-level models, with fast simulations, to validate the concept of a bio-system.

The specifications of our system are the followings: • At a 730 nm light, the system is in an inactive state and doesn’t degrade the TAG-protein • At a 660 nm light, the system is active and degrade the TAG-protein

Based on these specifications, we deduce a group of logical equations. To make the model we use VHDL language, which is a hardware description language (HDL). The following VHDL code corresponds to our model:

We simulate this VHDL code with Dolphin SMASH 5.12 and we obtain followings results:

We see that when the light has a 730 nm wavelength the TAG-protein is not degraded. When the light passed to 660 nm, the TAG-protein is degraded after a 10 second delay, which corresponds to the time of phytochrome activation and is arbitrarily fixed in the model.

This model uses mathematical tools to calculate the levels of species involved. This has some advantages: fast simulations, use of Boolean algebra and tools existing in electronics, such as logic synthesis or formal checking tools.

Otherwise, it presents drawbacks. First, concentrations of proteins are not taken into account, which causes accuracy problems. Then, it is too basic compared to the complexity of a bio-system, but it quickly allows us to get an idea of the outcome of such a system without launching slow and complex simulations.