Literature DB >> 23379300

Free energy rhythms in Saccharomyces cerevisiae: a dynamic perspective with implications for ribosomal biogenesis.

A Gross1, Caroline M Li, F Remacle, R D Levine.   

Abstract

To describe the time course of cellular systems, we integrate ideas from thermodynamics and information theory to discuss the work needed to change the state of the cell. The biological example analyzed is experimental microarray transcription level oscillations of yeast in the different phases as characterized by oxygen consumption. Surprisal analysis was applied to identify groups of transcripts that oscillate in concert and thereby to compute changes in free energy with time. Three dominant transcript groups were identified by surprisal analysis. The groups correspond to the respiratory, early, and late reductive phases. Genes involved in ribosome biogenesis peaked at the respiratory phase. The work to prepare the state is shown to be the sum of the contributions of these groups. We paid particular attention to work requirements during ribosomal building, and the correlation with ATP levels and dissolved oxygen. The suggestion that cells in the respiratory phase likely build ribosomes, an energy intensive process, in preparation for protein production during the S phase of the cell cycle is validated by an experiment. Surprisal analysis thereby provided a useful tool for determining the synchronization of transcription events and energetics in a cell in real time.

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Year:  2013        PMID: 23379300      PMCID: PMC3655800          DOI: 10.1021/bi3016982

Source DB:  PubMed          Journal:  Biochemistry        ISSN: 0006-2960            Impact factor:   3.162


  26 in total

1.  A genomewide oscillation in transcription gates DNA replication and cell cycle.

Authors:  Robert R Klevecz; James Bolen; Gerald Forrest; Douglas B Murray
Journal:  Proc Natl Acad Sci U S A       Date:  2004-01-20       Impact factor: 11.205

2.  Dynamics of oscillatory phenotypes in Saccharomyces cerevisiae reveal a network of genome-wide transcriptional oscillators.

Authors:  Shwe L Chin; Ian M Marcus; Robert R Klevecz; Caroline M Li
Journal:  FEBS J       Date:  2012-02-27       Impact factor: 5.542

3.  Logic of the yeast metabolic cycle: temporal compartmentalization of cellular processes.

Authors:  Benjamin P Tu; Andrzej Kudlicki; Maga Rowicka; Steven L McKnight
Journal:  Science       Date:  2005-10-27       Impact factor: 47.728

4.  Discovery of principles of nature from mathematical modeling of DNA microarray data.

Authors:  Orly Alter
Journal:  Proc Natl Acad Sci U S A       Date:  2006-10-23       Impact factor: 11.205

5.  Restriction of DNA replication to the reductive phase of the metabolic cycle protects genome integrity.

Authors:  Zheng Chen; Elizabeth A Odstrcil; Benjamin P Tu; Steven L McKnight
Journal:  Science       Date:  2007-06-29       Impact factor: 47.728

Review 6.  Genomic signal processing: from matrix algebra to genetic networks.

Authors:  Orly Alter
Journal:  Methods Mol Biol       Date:  2007

Review 7.  Powering through ribosome assembly.

Authors:  Bethany S Strunk; Katrin Karbstein
Journal:  RNA       Date:  2009-10-22       Impact factor: 4.942

8.  Oscillations in continuous cultures of budding yeast: a segregated parameter analysis.

Authors:  D Porro; E Martegani; B M Ranzi; L Alberghina
Journal:  Biotechnol Bioeng       Date:  1988-08-05       Impact factor: 4.530

9.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization.

Authors:  P T Spellman; G Sherlock; M Q Zhang; V R Iyer; K Anders; M B Eisen; P O Brown; D Botstein; B Futcher
Journal:  Mol Biol Cell       Date:  1998-12       Impact factor: 4.138

10.  AmiGO: online access to ontology and annotation data.

Authors:  Seth Carbon; Amelia Ireland; Christopher J Mungall; ShengQiang Shu; Brad Marshall; Suzanna Lewis
Journal:  Bioinformatics       Date:  2008-11-25       Impact factor: 6.937

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  8 in total

1.  Thermodynamically inspired classifier for molecular phenotypes of health and disease.

Authors:  Marc T Facciotti
Journal:  Proc Natl Acad Sci U S A       Date:  2013-11-07       Impact factor: 11.205

2.  Statistical thermodynamics of transcription profiles in normal development and tumorigeneses in cohorts of patients.

Authors:  F Remacle; R D Levine
Journal:  Eur Biophys J       Date:  2015-08-20       Impact factor: 1.733

3.  Surprisal analysis characterizes the free energy time course of cancer cells undergoing epithelial-to-mesenchymal transition.

Authors:  Sohila Zadran; Rameshkumar Arumugam; Harvey Herschman; Michael E Phelps; R D Levine
Journal:  Proc Natl Acad Sci U S A       Date:  2014-08-25       Impact factor: 11.205

4.  miRNA and mRNA cancer signatures determined by analysis of expression levels in large cohorts of patients.

Authors:  Sohila Zadran; F Remacle; R D Levine
Journal:  Proc Natl Acad Sci U S A       Date:  2013-10-07       Impact factor: 11.205

5.  Surprisal analysis of transcripts expression levels in the presence of noise: a reliable determination of the onset of a tumor phenotype.

Authors:  Ayelet Gross; Raphael D Levine
Journal:  PLoS One       Date:  2013-04-23       Impact factor: 3.240

6.  Computational surprisal analysis speeds-up genomic characterization of cancer processes.

Authors:  Nataly Kravchenko-Balasha; Simcha Simon; R D Levine; F Remacle; Iaakov Exman
Journal:  PLoS One       Date:  2014-11-18       Impact factor: 3.240

7.  TMEA: A Thermodynamically Motivated Framework for Functional Characterization of Biological Responses to System Acclimation.

Authors:  Kevin Schneider; Benedikt Venn; Timo Mühlhaus
Journal:  Entropy (Basel)       Date:  2020-09-15       Impact factor: 2.524

8.  Translational Components Contribute to Acclimation Responses to High Light, Heat, and Cold in Arabidopsis.

Authors:  Antoni Garcia-Molina; Tatjana Kleine; Kevin Schneider; Timo Mühlhaus; Martin Lehmann; Dario Leister
Journal:  iScience       Date:  2020-07-01
  8 in total

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