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 .
The capacity of cells URLanisms to respond to challenging conditions in a repeatable manner is limited by a finite repertoire of pre-evolved adaptive responses. Beyond this capacity, cells can use exploratory dynamics to cope with a much broader array of conditions. However, the process of adaptation by exploratory dynamics within the lifetime of a cell is not well understood. Here we demonstrate the feasibility of exploratory adaptation in a high-dimensional network model of gene regulation. Exploration is initiated by failure to comply with a constraint and is implemented by random sampling of network configurations. It ceases if and when the network reaches a stable state satisfying the constraint. We find that successful convergence (adaptation) in high dimensions requires outgoing network hubs and is enhanced by their auto-regulation. The ability of these empirically-validated features of gene regulatory networks to support exploratory adaptation without fine-tuning, makes it plausible for biological implementation .
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to challenging conditions
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to challenging conditions
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number
repertoire
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exploratory dynamics can provide alternative means
cells can use exploratory dynamics
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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
However, the process of adaptation by exploratory dynamics
R
single cell . Here, we address this question by developing a
cell is not well understood. Here we demonstrate the feasibility of exploratory adaptation in a high-dimensional
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exploration in
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This model demonstrates the feasibility of adapting by temporal exploration.
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global
D
available
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system converges to a stable compliance with
network reaches a stable state satisfying
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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
We find that successful convergence (adaptation) in high dimensions requires outgoing network hubs and is enhanced by their auto-regulation. The ability of these empirically-validated features of gene regulatory networks to support exploratory adaptation without fine-tuning, makes
D
. Experimental results have indeed shown that gene regulatory networks are characterized by this type of topology, suggesting a structural basis for exploratory adaptation