System level mechanisms of adaptation, learning, memory formation and evolvability: the role of chaperone and other networks

TL;DR

This paper explores how network flexibility and modularity in protein and neuronal systems underpin adaptation, learning, memory formation, and evolvability, emphasizing the role of chaperone networks in these processes.

Contribution

It introduces a network-based framework linking protein chaperone interactions to mechanisms of adaptation, learning, and memory formation across biological systems.

Findings

Flexible networks facilitate learning states.

Rigid networks support memory storage and protection.

Network rigidity changes correlate with organism age and learning capacity.

Abstract

During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here, we describe first the protein structure networks of molecular chaperones, then characterize chaperone containing sub-networks of interactomes called as chaperone-networks or chaperomes. We review the role of molecular chaperones in short-term adaptation of cellular networks in response to stress, and in long-term adaptation discussing their putative functions in the regulation of evolvability. We provide a general overview of possible network mechanisms of adaptation, learning and memory formation. We propose that changes of network rigidity play a key role in learning and memory formation processes. Flexible network topology provides "learning competent" state. Here, networks may have much less modular boundaries than locally rigid, highly modular networks, where the learnt information has already been consolidated in a memory formation process. Since modular boundaries are efficient filters of information, in the "learning competent" state information filtering may be much smaller, than after memory formation. This mechanism restricts high information transfer to the "learning competent" state. After memory formation, modular boundary-induced segregation and information filtering protect the stored information. The flexible networks of young organisms are generally in a "learning competent" state. On the contrary, locally rigid networks of old organisms have lost their "learning competent" state, but store and protect their learnt information efficiently. We anticipate that the above mechanism may operate at the level of both protein-protein interaction and neuronal networks.