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Exploratory adaptation in large random networks
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DOI：10.1038/ncomms14826.png)
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Abstract
En 中文
The capacity of cells and organisms 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.
Keywords:
SCALE-FREE TOPOLOGY
REGULATORY NETWORK
EVOLUTIONARY ADAPTATION
ENVIRONMENTAL-CHANGES
COMPLEX NETWORKS
NEURAL-NETWORKS
FIXED-POINTS
DYNAMICS
YEAST
GENOME
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