Unclocklike biological oscillators with frequency memory

Christian M. Denis, Paul François


On this page, you will find some interactive simulations of some of the models presented in this preprint. Here we are simulating the system $$ \begin{cases} \dot{\phi} = 2\pi f(x) + k \sin(\theta - \phi) \\ \dot{\theta} = 2 \pi \omega \\ \dot{x} = -\alpha \left(x - \frac{\dot{\phi}}{2\pi}\right) \end{cases} $$ Although this model could potentially be applied to other systems, it was initially motivated by work done on the entrainment of the segmentation clock. More specifically, it was motivated by the results from this paper. In this context, the variables represent:

Experimental schematic

The motivating biological system corresponds to the oscillations in the Notch signalling pathway inside of the mouse embryo tailbud. These oscillations are part of a plethora of interacting oscillations collectively forming the segmentation clock. After applying periodic pulses of DAPT (a Notch inhibitor) in this system through a microfluidics chip, we observe the Notch oscillations being entrained to the external perturbation. Experimental evidence suggests that the segmentation clock adapts its intrinsic frequency to the one of the entraining oscillator.

Coupling function

The \( f\) function used in the simulation is almost linear. It takes the form \( f(x) = x + \sigma \), where \(\sigma\) is a sigmoidal function centered around \( 1 \).

\( c_1= \)

\( c_2= \)

Simulations

Here we show some simulations of the system.

We use different parameters that you can tweak for the simulations below. These are:

On top of these parameters, there are also some initial conditions for each of the three variables of the system.

Model:

Parameters:

\( \omega= \)

\( k= \)

\( \alpha= \)

Init. Cond.:

\( \phi(0)= \)

\( \theta(0)= \)

\( x(0)= \)

Time:

Hysteresis simulation

Here we demontrate how sensitive the system is to dynamical changes in its parameters. This phenomenon is at the core of the hysteretic effects.

k = 1.6
ω = 1.0
α = 1.0