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Robustness versus evolvability analysis for regulatory feed-forward loops.

Debika Choudhury1, Amit Agarwal1, Supreet Saini1

  • 11 Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India.

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|November 22, 2017
PubMed
Summary

Phenotypic robustness and evolvability are often seen as opposing forces. This study reveals that in certain Feed Forward Loops (FFLs), these traits can be positively correlated, challenging previous assumptions.

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FFLsRobustnessevolvability

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Area of Science:

  • Systems Biology
  • Evolutionary Biology
  • Genetics

Background:

  • Phenotypic robustness and evolvability are key concepts in evolutionary biology.
  • Existing research presents conflicting evidence regarding the relationship between robustness and evolvability.
  • Feed Forward Loops (FFLs) are common regulatory motifs in biological systems.

Purpose of the Study:

  • To investigate the relationship between phenotypic robustness and evolvability using Feed Forward Loops (FFLs) as a model system.
  • To develop a framework for quantifying robustness and evolvability in FFLs.
  • To compare the robustness-evolvability relationship in FFLs with other regulatory topologies.

Main Methods:

  • Utilized a computational framework to model and analyze Feed Forward Loops (FFLs).
  • Quantified robustness by assessing the stability of steady-state gene expression.
  • Quantified evolvability by measuring the capacity to evolve towards novel phenotypes.

Main Results:

  • Demonstrated a positive correlation between robustness and evolvability in specific FFL topologies.
  • Showed that this positive correlation is not universally observed across all small regulatory topologies.
  • Highlighted that FFLs exhibit a unique relationship between these two critical traits.

Conclusions:

  • The positive linkage between robustness and evolvability in FFLs may explain their prevalence in biological organisms.
  • FFLs possess distinct properties that differentiate them from other regulatory networks.
  • This finding offers new insights into the evolutionary dynamics of gene regulatory networks.