Literature DB >> 11531388

The dynamics of the macromolecular composition of biomass.

P P Hanegraaf1, E B Muller.   

Abstract

The biomass composition of microorganisms depends on the growth conditions. This study explores whether a two-component model can explain how the elemental and macromolecular composition of the biomass of bacteria varies with the specific growth rate. The model describes the rates at which microorganisms assimilate substrates into reserves and utilize reserves for maintenance and growth. Crucial model assumptions are that biomass consists of reserves and structure and that each of these components has an invariant composition. The composition of biomass can vary when the ratio between reserves and structure varies. Literature data on the macromolecular composition of Escherichia coli, cultivated on various substrates, show that the protein, RNA and DNA content of biomass follow a distinctive trend when plotted as a function of the dry-weight-specific growth rate. This observation leads to the proposition that the macromolecular composition of E. coli depends directly on the growth rate, and only indirectly on the carbon- and energy-source used as substrate. We show that the variation of the macromolecular composition of E. coli over its entire range of growth rates can be described with invariant macromolecular compositions of the reserve and structural components of biomass. The model is also applied to our data on a succinate-limited continuous culture of Paracoccus denitrificans. Copyright 2001 Academic Press.

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Year:  2001        PMID: 11531388     DOI: 10.1006/jtbi.2001.2369

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  10 in total

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Authors:  Tin Klanjscek; Roger M Nisbet; John H Priester; Patricia A Holden
Journal:  Ecotoxicology       Date:  2013-01-06       Impact factor: 2.823

2.  Temperature affects stoichiometry and biochemical composition of Escherichia coli.

Authors:  James B Cotner; Wataru Makino; Bopaiah A Biddanda
Journal:  Microb Ecol       Date:  2006-06-10       Impact factor: 4.552

3.  Sublethal toxicant effects with dynamic energy budget theory: model formulation.

Authors:  Erik B Muller; Roger M Nisbet; Heather A Berkley
Journal:  Ecotoxicology       Date:  2009-07-25       Impact factor: 2.823

4.  Modeling phenotypic metabolic adaptations of Mycobacterium tuberculosis H37Rv under hypoxia.

Authors:  Xin Fang; Anders Wallqvist; Jaques Reifman
Journal:  PLoS Comput Biol       Date:  2012-09-13       Impact factor: 4.475

5.  Modeling physiological processes that relate toxicant exposure and bacterial population dynamics.

Authors:  Tin Klanjscek; Roger M Nisbet; John H Priester; Patricia A Holden
Journal:  PLoS One       Date:  2012-02-06       Impact factor: 3.240

6.  Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism.

Authors:  Daniel Machado; Markus Herrgård
Journal:  PLoS Comput Biol       Date:  2014-04-24       Impact factor: 4.475

7.  Modeling Host-Pathogen Interaction to Elucidate the Metabolic Drug Response of Intracellular Mycobacterium tuberculosis.

Authors:  Rienk A Rienksma; Peter J Schaap; Vitor A P Martins Dos Santos; Maria Suarez-Diez
Journal:  Front Cell Infect Microbiol       Date:  2019-05-08       Impact factor: 5.293

8.  An optimal growth law for RNA composition and its partial implementation through ribosomal and tRNA gene locations in bacterial genomes.

Authors:  Xiao-Pan Hu; Martin J Lercher
Journal:  PLoS Genet       Date:  2021-11-29       Impact factor: 5.917

9.  Reconciling in vivo and in silico key biological parameters of Pseudomonas putida KT2440 during growth on glucose under carbon-limited condition.

Authors:  Jozef B J H van Duuren; Jacek Puchałka; Astrid E Mars; René Bücker; Gerrit Eggink; Christoph Wittmann; Vítor A P Martins Dos Santos
Journal:  BMC Biotechnol       Date:  2013-10-29       Impact factor: 2.563

10.  Fine-tuning the P. pastoris iMT1026 genome-scale metabolic model for improved prediction of growth on methanol or glycerol as sole carbon sources.

Authors:  Màrius Tomàs-Gamisans; Pau Ferrer; Joan Albiol
Journal:  Microb Biotechnol       Date:  2017-11-21       Impact factor: 5.813

  10 in total

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