Hidden long evolutionary memory in a model biochemical network

Md Zulfikar Ali, Ned S. Wingreen, Ranjan Mukhopadhyay

Research output: Contribution to journalArticle

2 Scopus citations

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.

Original languageEnglish (US)
Article number040401
JournalPhysical Review E
Volume97
Issue number4
DOIs
StatePublished - Apr 20 2018

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics

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