Which gene goes were?

• The key element to understand is that fliA stands as an accelerator. The first influence is caused by flhDC, and fliA gives the last burst. fliA also has a great influence in the fading phase, since it introduces a delay. This is what determined our choice in the order of fliL, flgA and flhB. We wanted to put in first the gene that was the least influenced by fliA, and in the end the gene the most influenced by fliA.

• In fact, this enabled us to force the curves into crossing themselves. In addition, the delay imposed by fliA in the decreasing phase, would permit us to widen the delays between the first and the last gene expression, thus getting better conditions to observe the FIFO in the wet lab.

Understanding the dynamics

• Theory introduction

The classical approach consists in finding the equilibrium state, by setting

Then, we evaluate the jacobian matrix, so as to put the system under its linearized form:

Where [EnvZ]* represents the equilibirum state of [EnvZ] and :

which gives :

Then, we want to find the eigenvalues of the jacobian matrix, because they make us understand the behavior of the system. Here is the theoretical explanation :

The λ and γ coefficients make the convergence. In our case they are negative terms, which explains the fact that we have a quick convergence for the system. Ideally, it could be convenient to find which parameter influence this coefficient, so as to play with it and be able to propose a better control of the convergence.
The μ coefficient is responsible for the oscillations. In fact, in another base, with only real matrix, this would beget cosinuses and sinuses terms. Furthermore, it is strongly linked to the period of the oscillations.

• What about the periods

We obtained three solutions :

We are only interested in positive real solutions. We can note that 0.4764 corresponds well to the experimental equilibrium value. Then we evaluated the eigenvalues for the jacobian :

We can note that the λ and γ coefficient are negative, which corroborates the fact that we obtain a convergence.

• Understanding the attenuation

Furthermore, this theoretical study helps us understand that reducing the attenuation is tough. In fact, whatever the base in which you consider a matrix, the trace is conserved. Here, we obtain :

Since λ and γ are assumed (thanks to simulations) to be negative, we cannot avoid to have their sum to be (with absolute value) superior to 2. The only chance we have would be that the weight goes in the λ parameter because this parameter plays a role in the third dimension, in which we are not interested. However, for every simulation we made, we found the opposite. Trying to evaluate with non-numerical calculus does not provide relevant material.

However, we now know that β_FliA influences the attenuation. We ran a simulation with β_FliA=0.02

This confirms what we saw previously. In this second case, the system is therefore less attenuated, and some very tiny damped oscillations can be observed.

Last but not least, one should not forget that we chose a normalized interpretation of the system. Thus, even though the attenuation phenomenon remains unchanged, the tiny oscillations we see may indeed be observable.

Impact of simulation methods

This section wil just be made of some simulations to show the influence of the solver used. Indeed, when it comes to detect oscillations, some weird phenomenon can just be produced by simulation algorithms' aterfact. We have just obtained the following curves for the same initial system of ODEs.

• Numerical simulations with Euler algorithm

(The second picture is just a zoom, as defined by the red box)

• Numerical simulations with variable-step methods

(The second picture is just a zoom, as defined by the red box)

One can notice that some oscillations seem to occur in the second case, which is not the case in simulations made with constant step. Therefore, the precise tuning of the algorithm used have to be undertaken in order to avoid false detection of oscillations.