Literature DB >> 27294303

Ribosome biogenesis in replicating cells: Integration of experiment and theory.

Tyler M Earnest1,2, John A Cole2, Joseph R Peterson3, Michael J Hallock4, Thomas E Kuhlman1,2, Zaida Luthey-Schulten1,2,3.   

Abstract

Ribosomes-the primary macromolecular machines responsible for translating the genetic code into proteins-are complexes of precisely folded RNA and proteins. The ways in which their production and assembly are managed by the living cell is of deep biological importance. Here we extend a recent spatially resolved whole-cell model of ribosome biogenesis in a fixed volume [Earnest et al., Biophys J 2015, 109, 1117-1135] to include the effects of growth, DNA replication, and cell division. All biological processes are described in terms of reaction-diffusion master equations and solved stochastically using the Lattice Microbes simulation software. In order to determine the replication parameters, we construct and analyze a series of Escherichia coli strains with fluorescently labeled genes distributed evenly throughout their chromosomes. By measuring these cells' lengths and number of gene copies at the single-cell level, we could fit a statistical model of the initiation and duration of chromosome replication. We found that for our slow-growing (120 min doubling time) E. coli cells, replication was initiated 42 min into the cell cycle and completed after an additional 42 min. While simulations of the biogenesis model produce the correct ribosome and mRNA counts over the cell cycle, the kinetic parameters for transcription and degradation are lower than anticipated from a recent analytical time dependent model of in vivo mRNA production. Describing expression in terms of a simple chemical master equation, we show that the discrepancies are due to the lack of nonribosomal genes in the extended biogenesis model which effects the competition of mRNA for ribosome binding, and suggest corrections to parameters to be used in the whole-cell model when modeling expression of the entire transcriptome.
© 2016 Wiley Periodicals, Inc. Biopolymers 105: 735-751, 2016. © 2016 Wiley Periodicals, Inc.

Entities:  

Keywords:  bacterial cell division; ribosome biogenesis; stochastic gene expression

Mesh:

Substances:

Year:  2016        PMID: 27294303      PMCID: PMC4958520          DOI: 10.1002/bip.22892

Source DB:  PubMed          Journal:  Biopolymers        ISSN: 0006-3525            Impact factor:   2.505


  52 in total

1.  Intermediates and time kinetics of the in vivo assembly of Escherichia coli ribosomes.

Authors:  L Lindahl
Journal:  J Mol Biol       Date:  1975-02-15       Impact factor: 5.469

2.  The CyberCell Database (CCDB): a comprehensive, self-updating, relational database to coordinate and facilitate in silico modeling of Escherichia coli.

Authors:  Shan Sundararaj; Anchi Guo; Bahram Habibi-Nazhad; Melania Rouani; Paul Stothard; Michael Ellison; David S Wishart
Journal:  Nucleic Acids Res       Date:  2004-01-01       Impact factor: 16.971

3.  Intrinsic and extrinsic contributions to stochasticity in gene expression.

Authors:  Peter S Swain; Michael B Elowitz; Eric D Siggia
Journal:  Proc Natl Acad Sci U S A       Date:  2002-09-17       Impact factor: 11.205

4.  A model for statistics of the cell division process.

Authors:  A L KOCH; M SCHAECHTER
Journal:  J Gen Microbiol       Date:  1962-11

5.  Growth rate and generation time of bacteria, with special reference to continuous culture.

Authors:  E O POWELL
Journal:  J Gen Microbiol       Date:  1956-12

6.  Chromosome and replisome dynamics in E. coli: loss of sister cohesion triggers global chromosome movement and mediates chromosome segregation.

Authors:  David Bates; Nancy Kleckner
Journal:  Cell       Date:  2005-06-17       Impact factor: 41.582

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Authors:  David Bates; Jessica Epstein; Erik Boye; Karen Fahrner; Howard Berg; Nancy Kleckner
Journal:  Mol Microbiol       Date:  2005-07       Impact factor: 3.501

8.  One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products.

Authors:  K A Datsenko; B L Wanner
Journal:  Proc Natl Acad Sci U S A       Date:  2000-06-06       Impact factor: 11.205

9.  Precise determinations of C and D periods by flow cytometry in Escherichia coli K-12 and B/r.

Authors:  Ole Michelsen; M Joost Teixeira de Mattos; Peter Ruhdal Jensen; Flemming G Hansen
Journal:  Microbiology (Reading)       Date:  2003-04       Impact factor: 2.777

10.  Genome-wide expression profiling in Escherichia coli K-12.

Authors:  C S Richmond; J D Glasner; R Mau; H Jin; F R Blattner
Journal:  Nucleic Acids Res       Date:  1999-10-01       Impact factor: 16.971

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  6 in total

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Authors:  John A Cole; Zaida Luthey-Schulten
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2.  Essential metabolism for a minimal cell.

Authors:  Marian Breuer; Tyler M Earnest; Chuck Merryman; Kim S Wise; Lijie Sun; Michaela R Lynott; Clyde A Hutchison; Hamilton O Smith; John D Lapek; David J Gonzalez; Valérie de Crécy-Lagard; Drago Haas; Andrew D Hanson; Piyush Labhsetwar; John I Glass; Zaida Luthey-Schulten
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Journal:  J Phys Chem B       Date:  2017-03-30       Impact factor: 2.991

4.  Multiscale Stochastic Reaction-Diffusion Algorithms Combining Markov Chain Models with Stochastic Partial Differential Equations.

Authors:  Hye-Won Kang; Radek Erban
Journal:  Bull Math Biol       Date:  2019-06-04       Impact factor: 1.758

5.  Kinetic Modeling of the Genetic Information Processes in a Minimal Cell.

Authors:  Zane R Thornburg; Marcelo C R Melo; David Bianchi; Troy A Brier; Cole Crotty; Marian Breuer; Hamilton O Smith; Clyde A Hutchison; John I Glass; Zaida Luthey-Schulten
Journal:  Front Mol Biosci       Date:  2019-11-28

6.  The correlation between cell and nucleus size is explained by an eukaryotic cell growth model.

Authors:  Yufei Wu; Adrian F Pegoraro; David A Weitz; Paul Janmey; Sean X Sun
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  6 in total

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