Literature DB >> 21481583

The role of cellular objectives and selective pressures in metabolic pathway evolution.

Hojung Nam1, Tom M Conrad, Nathan E Lewis.   

Abstract

Evolution results from molecular-level changes in an organism, thereby producing novel phenotypes and, eventually novel species. However, changes in a single gene can lead to significant changes in biomolecular networks through the gain and loss of many molecular interactions. Thus, significant insights into microbial evolution have been gained through the analysis and comparison of reconstructed metabolic networks. However, challenges remain from reconstruction incompleteness and the inability to experiment with evolution on the timescale necessary for new species to arise. Despite these challenges, experimental laboratory evolution of microbes has provided some insights into the cellular objectives underlying evolution, under the constraints of nutrient availability and the use of mechanisms that protect cells from extreme conditions.
Copyright © 2011 Elsevier Ltd. All rights reserved.

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Year:  2011        PMID: 21481583      PMCID: PMC3173765          DOI: 10.1016/j.copbio.2011.03.006

Source DB:  PubMed          Journal:  Curr Opin Biotechnol        ISSN: 0958-1669            Impact factor:   9.740


  38 in total

Review 1.  Network biology: understanding the cell's functional organization.

Authors:  Albert-László Barabási; Zoltán N Oltvai
Journal:  Nat Rev Genet       Date:  2004-02       Impact factor: 53.242

2.  Metabolic gene-deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes.

Authors:  Stephen S Fong; Bernhard Ø Palsson
Journal:  Nat Genet       Date:  2004-09-26       Impact factor: 38.330

3.  On the Evolution of Biochemical Syntheses.

Authors:  N H Horowitz
Journal:  Proc Natl Acad Sci U S A       Date:  1945-06       Impact factor: 11.205

4.  Large-scale reconstruction and phylogenetic analysis of metabolic environments.

Authors:  Elhanan Borenstein; Martin Kupiec; Marcus W Feldman; Eytan Ruppin
Journal:  Proc Natl Acad Sci U S A       Date:  2008-09-11       Impact factor: 11.205

Review 5.  Enzyme recruitment in evolution of new function.

Authors:  R A Jensen
Journal:  Annu Rev Microbiol       Date:  1976       Impact factor: 15.500

6.  In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data.

Authors:  J S Edwards; R U Ibarra; B O Palsson
Journal:  Nat Biotechnol       Date:  2001-02       Impact factor: 54.908

7.  Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models.

Authors:  Nathan E Lewis; Kim K Hixson; Tom M Conrad; Joshua A Lerman; Pep Charusanti; Ashoka D Polpitiya; Joshua N Adkins; Gunnar Schramm; Samuel O Purvine; Daniel Lopez-Ferrer; Karl K Weitz; Roland Eils; Rainer König; Richard D Smith; Bernhard Ø Palsson
Journal:  Mol Syst Biol       Date:  2010-07       Impact factor: 11.429

8.  Chance and necessity in the evolution of minimal metabolic networks.

Authors:  Csaba Pál; Balázs Papp; Martin J Lercher; Péter Csermely; Stephen G Oliver; Laurence D Hurst
Journal:  Nature       Date:  2006-03-30       Impact factor: 49.962

9.  Origin of structural difference in metabolic networks with respect to temperature.

Authors:  Kazuhiro Takemoto; Tatsuya Akutsu
Journal:  BMC Syst Biol       Date:  2008-09-22

10.  Understanding the adaptive growth strategy of Lactobacillus plantarum by in silico optimisation.

Authors:  Bas Teusink; Anne Wiersma; Leo Jacobs; Richard A Notebaart; Eddy J Smid
Journal:  PLoS Comput Biol       Date:  2009-06-12       Impact factor: 4.475

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  12 in total

1.  Engineering furfural tolerance in Escherichia coli improves the fermentation of lignocellulosic sugars into renewable chemicals.

Authors:  Xuan Wang; Lorraine P Yomano; James Y Lee; Sean W York; Huabao Zheng; Michael T Mullinnix; K T Shanmugam; Lonnie O Ingram
Journal:  Proc Natl Acad Sci U S A       Date:  2013-02-19       Impact factor: 11.205

Review 2.  Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods.

Authors:  Nathan E Lewis; Harish Nagarajan; Bernhard O Palsson
Journal:  Nat Rev Microbiol       Date:  2012-02-27       Impact factor: 60.633

3.  Network context and selection in the evolution to enzyme specificity.

Authors:  Hojung Nam; Nathan E Lewis; Joshua A Lerman; Dae-Hee Lee; Roger L Chang; Donghyuk Kim; Bernhard O Palsson
Journal:  Science       Date:  2012-08-31       Impact factor: 47.728

4.  Dynamic interactive events in gene regulation using E. coli dehydrogenase as a model.

Authors:  Sampada Puranik; Hemant J Purohit
Journal:  Funct Integr Genomics       Date:  2014-11-30       Impact factor: 3.410

5.  Induction of resistance mechanisms in Rhodotorula toruloides for growth in sugarcane hydrolysate with high inhibitor content.

Authors:  Helberth Júnnior Santos Lopes; Nemailla Bonturi; Everson Alves Miranda
Journal:  Appl Microbiol Biotechnol       Date:  2021-11-11       Impact factor: 4.813

6.  Metabolic network modularity in archaea depends on growth conditions.

Authors:  Kazuhiro Takemoto; Suritalatu Borjigin
Journal:  PLoS One       Date:  2011-10-06       Impact factor: 3.240

7.  Current understanding of the formation and adaptation of metabolic systems based on network theory.

Authors:  Kazuhiro Takemoto
Journal:  Metabolites       Date:  2012-07-12

8.  Limited influence of oxygen on the evolution of chemical diversity in metabolic networks.

Authors:  Kazuhiro Takemoto; Ikumi Yoshitake
Journal:  Metabolites       Date:  2013-10-16

9.  Limitations of a metabolic network-based reverse ecology method for inferring host-pathogen interactions.

Authors:  Kazuhiro Takemoto; Kazuki Aie
Journal:  BMC Bioinformatics       Date:  2017-05-25       Impact factor: 3.169

Review 10.  Adaptive laboratory evolution -- principles and applications for biotechnology.

Authors:  Martin Dragosits; Diethard Mattanovich
Journal:  Microb Cell Fact       Date:  2013-07-01       Impact factor: 5.328

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