Literature DB >> 22682889

Synthetic approaches to understanding biological constraints.

Andrea Velenich1, Jeff Gore.   

Abstract

Microbes can be readily cultured and their genomes can be easily manipulated. For these reasons, laboratory systems of unicellular organisms are increasingly used to develop and test theories about biological constraints, which manifest themselves at different levels of biological organization, from optimal gene-expression levels to complex individual and social behaviors. The quantitative description of biological constraints has recently advanced in several areas, such as the formulation of global laws governing the entire economy of a cell, the direct experimental measurement of the trade-offs leading to optimal gene expression, the description of naturally occurring fitness landscapes, and the appreciation of the requirements for a stable bacterial ecosystem.
Copyright © 2012 Elsevier Ltd. All rights reserved.

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Year:  2012        PMID: 22682889      PMCID: PMC3432407          DOI: 10.1016/j.cbpa.2012.05.199

Source DB:  PubMed          Journal:  Curr Opin Chem Biol        ISSN: 1367-5931            Impact factor:   8.822


  63 in total

1.  Interdependence of cell growth and gene expression: origins and consequences.

Authors:  Matthew Scott; Carl W Gunderson; Eduard M Mateescu; Zhongge Zhang; Terence Hwa
Journal:  Science       Date:  2010-11-19       Impact factor: 47.728

2.  Phenotypic diversity, population growth, and information in fluctuating environments.

Authors:  Edo Kussell; Stanislas Leibler
Journal:  Science       Date:  2005-08-25       Impact factor: 47.728

3.  Mutational reversions during adaptive protein evolution.

Authors:  Mark A DePristo; Daniel L Hartl; Daniel M Weinreich
Journal:  Mol Biol Evol       Date:  2007-06-07       Impact factor: 16.240

4.  Genome evolution and adaptation in a long-term experiment with Escherichia coli.

Authors:  Jeffrey E Barrick; Dong Su Yu; Sung Ho Yoon; Haeyoung Jeong; Tae Kwang Oh; Dominique Schneider; Richard E Lenski; Jihyun F Kim
Journal:  Nature       Date:  2009-10-18       Impact factor: 49.962

5.  A mixture of "cheats" and "co-operators" can enable maximal group benefit.

Authors:  R Craig MaClean; Ayari Fuentes-Hernandez; Duncan Greig; Laurence D Hurst; Ivana Gudelj
Journal:  PLoS Biol       Date:  2010-09-14       Impact factor: 8.029

6.  The rate at which asexual populations cross fitness valleys.

Authors:  Daniel B Weissman; Michael M Desai; Daniel S Fisher; Marcus W Feldman
Journal:  Theor Popul Biol       Date:  2009-03-13       Impact factor: 1.570

7.  The Prisoner's Dilemma and polymorphism in yeast SUC genes.

Authors:  Duncan Greig; Michael Travisano
Journal:  Proc Biol Sci       Date:  2004-02-07       Impact factor: 5.349

8.  Non-adaptive origins of interactome complexity.

Authors:  Ariel Fernández; Michael Lynch
Journal:  Nature       Date:  2011-05-18       Impact factor: 49.962

9.  A synthetic Escherichia coli predator-prey ecosystem.

Authors:  Frederick K Balagaddé; Hao Song; Jun Ozaki; Cynthia H Collins; Matthew Barnet; Frances H Arnold; Stephen R Quake; Lingchong You
Journal:  Mol Syst Biol       Date:  2008-04-15       Impact factor: 11.429

10.  An epistatic ratchet constrains the direction of glucocorticoid receptor evolution.

Authors:  Jamie T Bridgham; Eric A Ortlund; Joseph W Thornton
Journal:  Nature       Date:  2009-09-24       Impact factor: 49.962

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  6 in total

1.  Dynamic control and quantification of bacterial population dynamics in droplets.

Authors:  Shuqiang Huang; Jaydeep K Srimani; Anna J Lee; Ying Zhang; Allison J Lopatkin; Kam W Leong; Lingchong You
Journal:  Biomaterials       Date:  2015-05-19       Impact factor: 12.479

2.  Scaling laws governing stochastic growth and division of single bacterial cells.

Authors:  Srividya Iyer-Biswas; Charles S Wright; Jonathan T Henry; Klevin Lo; Stanislav Burov; Yihan Lin; Gavin E Crooks; Sean Crosson; Aaron R Dinner; Norbert F Scherer
Journal:  Proc Natl Acad Sci U S A       Date:  2014-10-27       Impact factor: 11.205

3.  Range expansion promotes cooperation in an experimental microbial metapopulation.

Authors:  Manoshi Sen Datta; Kirill S Korolev; Ivana Cvijovic; Carmel Dudley; Jeff Gore
Journal:  Proc Natl Acad Sci U S A       Date:  2013-04-08       Impact factor: 11.205

4.  Secreting and sensing the same molecule allows cells to achieve versatile social behaviors.

Authors:  Hyun Youk; Wendell A Lim
Journal:  Science       Date:  2014-02-07       Impact factor: 47.728

5.  The strength of genetic interactions scales weakly with mutational effects.

Authors:  Andrea Velenich; Jeff Gore
Journal:  Genome Biol       Date:  2013-07-26       Impact factor: 13.583

6.  Detecting the Collapse of Cooperation in Evolving Networks.

Authors:  Matteo Cavaliere; Guoli Yang; Vincent Danos; Vasilis Dakos
Journal:  Sci Rep       Date:  2016-08-05       Impact factor: 4.379

  6 in total

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