Abstract
Complex dynamical fluctuations, from intracellular noise, brain dynamics or computer traffic display bursting dynamics consistent with a critical state between order and disorder. Living close to the critical point has adaptive advantages and it has been conjectured that evolution could select these critical states. Is this the case of living cells? A system can poise itself close to the critical point by means of the so-called self-organized criticality (SOC). In this paper we present an engineered gene network displaying SOC behaviour. This is achieved by exploiting the saturation of the proteolytic degradation machinery in E. coli cells by means of a negative feedback loop that reduces congestion. Our critical motif is built from a two-gene circuit, where SOC can be successfully implemented. The potential implications for both cellular dynamics and behaviour are discussed.
Highlights
Complex dynamical fluctuations, from intracellular noise, brain dynamics or computer traffic display bursting dynamics consistent with a critical state between order and disorder
These authors used the framework of queueing theory (QTH) to study the dynamics of different proteins that are processed by a downstream set of enzymes that play the role of servers
The concept and its implementation have been controversial and even sandpiles have been found to achieve criticality only under very slow driving and when some microscopic properties are properly tuned[8,34,35,36]. We introduce these minimal conditions of self-organized criticality (SOC) dynamics in living cells by engineering the interaction between order and control parameters in a simple two-gene network design
Summary
From intracellular noise, brain dynamics or computer traffic display bursting dynamics consistent with a critical state between order and disorder. It has been shown that many complex systems seem to be poised close to so-called critical points separating ordered from disordered states[5,6,7,8]. In a nutshell, both living and non-living systems organize at the boundary separating regular (predictable) from random (disordered) behaviours. One way to criticality based on tuning key parameters is well illustrated by enzymatic queueing processes in Ref. 26 These authors used the framework of queueing theory (QTH) to study the dynamics of different proteins (the customers in QTH) that are processed by a downstream set of enzymes that play the role of servers. Scaling sc is found to occur instead in the distribution of latencies, i. e. the time required from the production to the final processing of each particle[23]
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