Timing and synchronization mechanisms are ubiquitous in living systems, and in many cases involve switch-like regulators that control complex molecular pathways and cellular functions. The switching of such regulators is often irreversible and controlled by the co-activation of a set of concurrent independent enabling events. Despite the random nature of each individual switch, the timing in the onset of the controlled process has a rather small variability. This note introduces a mathematical framework for the description of the collective behavior of populations of interconnected stochastic switches. The main contribution of this note is to explain how the connecting mode (series/parallel) of switches affects the behavior of the entire switch population and in particular the degree of synchronization. We describe the switch model for the G1/S phase transition in yeast and briefly discuss the general utility of this class of models in systems biology.

Modeling biological timing and synchronization mechanisms by means of interconnections of stochastic switches

Costanzo Manes;Pasquale Palumbo;
2018

Abstract

Timing and synchronization mechanisms are ubiquitous in living systems, and in many cases involve switch-like regulators that control complex molecular pathways and cellular functions. The switching of such regulators is often irreversible and controlled by the co-activation of a set of concurrent independent enabling events. Despite the random nature of each individual switch, the timing in the onset of the controlled process has a rather small variability. This note introduces a mathematical framework for the description of the collective behavior of populations of interconnected stochastic switches. The main contribution of this note is to explain how the connecting mode (series/parallel) of switches affects the behavior of the entire switch population and in particular the degree of synchronization. We describe the switch model for the G1/S phase transition in yeast and briefly discuss the general utility of this class of models in systems biology.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/122660
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