Predicting evolution using regulatory architecture

Philippe Nghe, Marjon de Vos, Enzo Kingma, Manjunatha Kogenaru, Frank J. Poelwijk, Liedewij Laan, Sander J Tans*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)
238 Downloads (Pure)

Abstract

The limits of evolution have long fascinated biologists. However, the causes of evolutionary constraint have remained elusive due to a poor mechanistic understanding of studied phenotypes. Recently, a range of innovative approaches have leveraged mechanistic information on regulatory networks and cellular biology. These methods combine systems biology models with population and single-cell quantification and with new genetic tools, and they have been applied to a range of complex cellular functions and engineered networks. In this article, we review these developments, which are revealing the mechanistic causes of epistasis at different levels of biological organization¤mdash¤in molecular recognition, within a single regulatory network, and between different networks¤mdash¤providing first indications of predictable features of evolutionary constraint.

Original languageEnglish
Article number49
Pages (from-to)181–197
Number of pages17
JournalAnnual Review of Biophysics
Volume49
DOIs
Publication statusPublished - 6-May-2020

Keywords

  • epistasis
  • regulation networks
  • evolutionary constraint
  • gene regulation
  • pleiotropy
  • prediction
  • EMPIRICAL FITNESS LANDSCAPES
  • GENETIC-VARIABILITY
  • SIGN EPISTASIS
  • PLEIOTROPY
  • MAINTENANCE
  • COMPLEXITY
  • CONSTRAINT
  • SELECTION
  • TRADEOFF
  • CDC42

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