Literature DB >> 21802129

Tradeoffs and optimality in the evolution of gene regulation.

Frank J Poelwijk1, Marjon G J de Vos, Sander J Tans.   

Abstract

Cellular regulation is believed to evolve in response to environmental variability. However, this has been difficult to test directly. Here, we show that a gene regulation system evolves to the optimal regulatory response when challenged with variable environments. We engineered a genetic module subject to regulation by the lac repressor (LacI) in E. coli, whose expression is beneficial in one environmental condition and detrimental in another. Measured tradeoffs in fitness between environments predict the competition between regulatory phenotypes. We show that regulatory evolution in adverse environments is delayed at specific boundaries in the phenotype space of the regulatory LacI protein. Once this constraint is relieved by mutation, adaptation proceeds toward the optimum, yielding LacI with an altered allosteric mechanism that enables an opposite response to its regulatory ligand IPTG. Our results indicate that regulatory evolution can be understood in terms of tradeoff optimization theory.
Copyright © 2011 Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 21802129     DOI: 10.1016/j.cell.2011.06.035

Source DB:  PubMed          Journal:  Cell        ISSN: 0092-8674            Impact factor:   41.582


  47 in total

1.  Breaking evolutionary constraint with a tradeoff ratchet.

Authors:  Marjon G J de Vos; Alexandre Dawid; Vanda Sunderlikova; Sander J Tans
Journal:  Proc Natl Acad Sci U S A       Date:  2015-11-13       Impact factor: 11.205

2.  Collective Properties of a Transcription Initiation Model Under Varying Environment.

Authors:  Yucheng Hu; John S Lowengrub
Journal:  J Comput Biol       Date:  2015-12-08       Impact factor: 1.479

3.  Bounded population sizes, fluctuating selection and the tempo and mode of coexistence.

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Journal:  Proc Natl Acad Sci U S A       Date:  2013-09-27       Impact factor: 11.205

4.  Regulatory revolution: evolving the "anti-LacI" repressor.

Authors:  Christopher J Marx
Journal:  Cell       Date:  2011-08-05       Impact factor: 41.582

5.  Optimal control of bacterial growth for the maximization of metabolite production.

Authors:  Ivan Yegorov; Francis Mairet; Hidde de Jong; Jean-Luc Gouzé
Journal:  J Math Biol       Date:  2018-10-17       Impact factor: 2.259

6.  Diminishing returns and tradeoffs constrain the laboratory optimization of an enzyme.

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Journal:  Nat Commun       Date:  2012       Impact factor: 14.919

7.  Fine-tuning gene networks using simple sequence repeats.

Authors:  Robert G Egbert; Eric Klavins
Journal:  Proc Natl Acad Sci U S A       Date:  2012-08-27       Impact factor: 11.205

8.  Epistasis as the primary factor in molecular evolution.

Authors:  Michael S Breen; Carsten Kemena; Peter K Vlasov; Cedric Notredame; Fyodor A Kondrashov
Journal:  Nature       Date:  2012-10-14       Impact factor: 49.962

9.  Evolution-guided engineering of small-molecule biosensors.

Authors:  Tim Snoek; Evan K Chaberski; Francesca Ambri; Stefan Kol; Sara P Bjørn; Bo Pang; Jesus F Barajas; Ditte H Welner; Michael K Jensen; Jay D Keasling
Journal:  Nucleic Acids Res       Date:  2020-01-10       Impact factor: 16.971

Review 10.  Computer aided enzyme design and catalytic concepts.

Authors:  Maria P Frushicheva; Matthew J L Mills; Patrick Schopf; Manoj K Singh; Ram B Prasad; Arieh Warshel
Journal:  Curr Opin Chem Biol       Date:  2014-05-08       Impact factor: 8.822

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