Hidden long evolutionary memory in a model biochemical network.

TitleHidden long evolutionary memory in a model biochemical network.
Publication TypeJournal Article
Year of Publication2018
AuthorsAli, MZulfikar, Wingreen, NS, Mukhopadhyay, R
JournalPhys Rev E
Volume97
Issue4-1
Pagination040401
Date Published2018 Apr
ISSN2470-0053
Abstract

We introduce a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutational algorithm, and apply the model to study neutral drift in networks that yield oscillatory dynamics. Starting with a functional core module, random evolutionary drift increases network complexity even in the absence of specific selective pressures. Surprisingly, we uncover a hidden order in sequence space that gives rise to long-term evolutionary memory, implying strong constraints on network evolution due to the topology of accessible sequence space.

DOI10.1103/PhysRevE.97.040401
Alternate JournalPhys Rev E
PubMed ID29758653
PubMed Central IDPMC5973509
Grant ListR01 GM082938 / GM / NIGMS NIH HHS / United States