The capacity of cells URLanisms to respond in a repeatable manner to challenging conditions is limited by a finite number of pre-evolved adaptive responses. Beyond this capacity, exploratory dynamics can provide alternative means to cope with a much broader array of conditions. At the population level, exploration is implemented by mutations and selection over multiple generations. However,  within the lifetime of a single cell ,the mechanisms by which exploratory changes can lead to adaptive phenotypes are still poorly understood . Here, we address this question by developing a network model of exploration in gene regulation. The model we propose demonstrates the feasibility of adapting by temporal exploration. Exploration is initiated by failure to comply with a global constraint and is implemented by random sampling of available network configurations. It ceases if and when the system converges to a stable compliance with the constraint. Successful convergence of this process depends crucially on network topology and is most efficient for scale-free connectivity , typical of gene regulatory networks. For such networks, convergence to an adapted phenotype can be achieved without fine tuning of initial conditions or other model parameters, thus making it plausible for biological implementation  .
The capacity of cells URLanisms to respond in a repeatable manner to challenging conditions is limited by a finite number of pre-evolved adaptive responses. Beyond this capacity, exploratory dynamics can provide alternative means to cope with a much broader array of conditions. At the population level, exploration is implemented by mutations and selection over multiple generations. However, it is not known how exploration can lead to new phenotypes within the lifetime of a single cell . Here, we address this question by developing a network model of exploration in gene regulation. This model demonstrates the feasibility of adapting by temporal exploration. Exploration is initiated by failure to comply with a global constraint and is implemented by random sampling of available network configurations. It ceases if and when the system converges to a stable compliance with the constraint. Successful convergence depends crucially on network topology and is most efficient for scale-free connectivity . Convergence to an adapted phenotype in this class of networks is achieved without fine tuning of initial conditions or other model parameters, thus making it plausible for biological implementation . Experimental results have indeed shown that gene regulatory networks are characterized by this type of topology, suggesting a structural basis for exploratory adaptation .
A
it is not known how exploration can lead to new phenotypes
D
,the mechanisms by which exploratory changes can lead to adaptive phenotypes are still poorly understood
R
The model we propose
This model
D
of this process
R
, typical of gene regulatory networks. For such networks, convergence
. Convergence
R
can be
in this class of networks is
A
. Experimental results have indeed shown that gene regulatory networks are characterized by this type of topology, suggesting a structural basis for exploratory adaptation