A limit cycle oscillator model for cycling mood variations of bipolar disorder patients derived from cellular biochemical reaction equations

We derive a nonlinear limit cycle model for oscillatory mood variations as observed in patients with cycling bipolar disorder. To this end, we consider two signaling pathways leading to the activation of two enzymes that play a key role for cellular and neural processes. We model pathway cross-talk in terms of an inhibitory impact of the first pathway on the second and an excitatory impact of the second on the first. The model also involves a negative feedback loop (inhibitory self-regulation) for the first pathway and a positive feedback loop (excitatory self-regulation) for the second pathway. We demonstrate that due to the cross-talk the biochemical dynamics is described by an oscillator equation. Under disease-free conditions the oscillatory system exhibits a stable fixed point. The breakdown of the self-inhibition of the first pathway at higher concentration levels is studied by means of a scalar control parameter ξξ, where ξξ equal to zero refers to intact self-inhibition at all concentration levels. Under certain conditions, stable limit cycle solutions emerge at critical parameter values of ξξ larger than zero. These oscillations mimic pathological cycling mood variations that emerge due to a disease-induced bifurcation. Consequently, our modeling analysis supports the notion of bipolar disorder as a dynamical disease. In addition, our study establishes a connection between mechanistic biochemical modeling of bipolar disorder and phenomenological nonlinear oscillator approaches to bipolar disorder suggested in the literature.

► We derive a nonlinear oscillator model for cycling bipolar disorder patterns.
► The oscillatory dynamics results from crosstalk between two cellular pathways.
► The disease pattern emerges from a Hopf bifurcation as a dynamical disease.