A Canonical 3-D P53 Network Model that Determines Cell Fate by Counting Pulses

Abstract

From a system theory perspective, p53 network dynamics is interesting since it can exhibit three dynamical modes of p53, namely low-level equilibrium, oscillation, and high-level equilibrium. Each of these modes are associated with different cell fate outcomes: cell survival, cell cycle arrest, and apoptosis. The literature reveals that a high level (apoptosis) is seen only after ending the oscillation phase, so called two-phase dynamics, which provides the decision of apoptosis depending on the oscillation duration. This paper proposes that a negative feedback can keep time by counting the pulses of oscillation to take the decision of apoptosis or cell survival. P53DINP1, which is the mediator of this feedback, is added as a variable to a 2-D oscillator model of the p53 network. The resulting canonical 3-D model successfully replicates the two-phase dynamics. That is, it possesses temporary oscillatory behavior, in which first oscillations (first phase) and then high level state (second phase) are observed. By introducing a new variable to the core oscillator in the p53 network, this study demonstrates that p53 network can be considered a modular structure, which consists of an oscillator and other variables that control this oscillator to contribute to cell fate determination.

Coupling of cell fate selection model enhances DNA damage response and may underlie BE phenomenon

“…The effect of accumulating pro‐apoptotic factors can be embedded via decreasing the parameter with proportional to the time integral of the pulse [34], whilst the repair process can be embedded via decreasing the damage severity parameter according to a model of the repair mechanism [33]. However, this requires at least two additional model variables for every ith cell and complicates the model.…”

“…1, changing from to (indicating the induction of DNA damage) with fixing causes oscillations. Then, decreasing the parameter to a sufficiently low value whilst is still (indicating the long duration of repair) causes a sustained high level [34].…”

Bifurcation analysis of bistable and oscillatory dynamics in biological networks using the root‐locus method

“…The eigenvalues of the toggle switch model are then determined by finding the roots of the characteristic equation given in (11)…”

“…These dynamics are modelled and analysed experimentally and computationally to get insights into the functioning of complex biological mechanisms in gene regulatory networks and signalling pathways [3,[5][6][7][8]. The mathematical models of biological systems, which might be derived based on chemical interactions or directly from experimental data by employing signal processing/machine learning methods, are vital not only for understanding the underlying mechanism of complex dynamics but also for predicting the behaviour of these systems via computational studies [8][9][10][11][12][13][14][15].…”