Literature DB >> 25695966

Mechanistic links between cellular trade-offs, gene expression, and growth.

Andrea Y Weiße1, Diego A Oyarzún2, Vincent Danos1, Peter S Swain3.   

Abstract

Intracellular processes rarely work in isolation but continually interact with the rest of the cell. In microbes, for example, we now know that gene expression across the whole genome typically changes with growth rate. The mechanisms driving such global regulation, however, are not well understood. Here we consider three trade-offs that, because of limitations in levels of cellular energy, free ribosomes, and proteins, are faced by all living cells and we construct a mechanistic model that comprises these trade-offs. Our model couples gene expression with growth rate and growth rate with a growing population of cells. We show that the model recovers Monod's law for the growth of microbes and two other empirical relationships connecting growth rate to the mass fraction of ribosomes. Further, we can explain growth-related effects in dosage compensation by paralogs and predict host-circuit interactions in synthetic biology. Simulating competitions between strains, we find that the regulation of metabolic pathways may have evolved not to match expression of enzymes to levels of extracellular substrates in changing environments but rather to balance a trade-off between exploiting one type of nutrient over another. Although coarse-grained, the trade-offs that the model embodies are fundamental, and, as such, our modeling framework has potentially wide application, including in both biotechnology and medicine.

Keywords:  evolutionarily stable strategy; host–circuit interactions; mathematical cell model; synthetic biology; systems biology

Mesh:

Year:  2015        PMID: 25695966      PMCID: PMC4352769          DOI: 10.1073/pnas.1416533112

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  53 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.  Molecular crowding limits translation and cell growth.

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Journal:  Proc Natl Acad Sci U S A       Date:  2013-09-30       Impact factor: 11.205

3.  Sensitivity, robustness, and identifiability in stochastic chemical kinetics models.

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Journal:  Proc Natl Acad Sci U S A       Date:  2011-05-06       Impact factor: 11.205

4.  A whole-cell computational model predicts phenotype from genotype.

Authors:  Jonathan R Karr; Jayodita C Sanghvi; Derek N Macklin; Miriam V Gutschow; Jared M Jacobs; Benjamin Bolival; Nacyra Assad-Garcia; John I Glass; Markus W Covert
Journal:  Cell       Date:  2012-07-20       Impact factor: 41.582

5.  Effect of growth rate on the amounts of ribosomal and transfer ribonucleic acids in yeast.

Authors:  C Waldron; F Lacroute
Journal:  J Bacteriol       Date:  1975-06       Impact factor: 3.490

6.  Informing biological design by integration of systems and synthetic biology.

Authors:  Christina D Smolke; Pamela A Silver
Journal:  Cell       Date:  2011-03-18       Impact factor: 41.582

7.  Empirical model and in vivo characterization of the bacterial response to synthetic gene expression show that ribosome allocation limits growth rate.

Authors:  Javier Carrera; Guillermo Rodrigo; Vijai Singh; Boris Kirov; Alfonso Jaramillo
Journal:  Biotechnol J       Date:  2011-06-16       Impact factor: 4.677

Review 8.  Does the ribosome translate cancer?

Authors:  Davide Ruggero; Pier Paolo Pandolfi
Journal:  Nat Rev Cancer       Date:  2003-03       Impact factor: 60.716

9.  Need-based up-regulation of protein levels in response to deletion of their duplicate genes.

Authors:  Alexander DeLuna; Michael Springer; Marc W Kirschner; Roy Kishony
Journal:  PLoS Biol       Date:  2010-03-30       Impact factor: 8.029

10.  Emergence of robust growth laws from optimal regulation of ribosome synthesis.

Authors:  Matthew Scott; Stefan Klumpp; Eduard M Mateescu; Terence Hwa
Journal:  Mol Syst Biol       Date:  2014-08-22       Impact factor: 11.429

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  111 in total

1.  Design of a bistable switch to control cellular uptake.

Authors:  Diego A Oyarzún; Madalena Chaves
Journal:  J R Soc Interface       Date:  2015-12-06       Impact factor: 4.118

2.  A little cooperation helps murine cytomegalovirus (MCMV) go a long way: MCMV co-infection rescues a chemokine salivary gland defect.

Authors:  Pranay Dogra; Mindy Miller-Kittrell; Elisabeth Pitt; Joseph W Jackson; Tom Masi; Courtney Copeland; Shuen Wu; William E Miller; Tim Sparer
Journal:  J Gen Virol       Date:  2016-09-13       Impact factor: 3.891

3.  Elementary Growth Modes provide a molecular description of cellular self-fabrication.

Authors:  Daan H de Groot; Josephus Hulshof; Bas Teusink; Frank J Bruggeman; Robert Planqué
Journal:  PLoS Comput Biol       Date:  2020-01-27       Impact factor: 4.475

4.  Modelling and measuring intracellular competition for finite resources during gene expression.

Authors:  Renana Sabi; Tamir Tuller
Journal:  J R Soc Interface       Date:  2019-05-31       Impact factor: 4.118

5.  A Practical Step-by-Step Guide for Quantifying Retroactivity in Gene Networks.

Authors:  Andras Gyorgy
Journal:  Methods Mol Biol       Date:  2021

6.  Prediction of Cellular Burden with Host-Circuit Models.

Authors:  Evangelos-Marios Nikolados; Andrea Y Weiße; Diego A Oyarzún
Journal:  Methods Mol Biol       Date:  2021

7.  Optimal control of bacterial growth for the maximization of metabolite production.

Authors:  Ivan Yegorov; Francis Mairet; Hidde de Jong; Jean-Luc Gouzé
Journal:  J Math Biol       Date:  2018-10-17       Impact factor: 2.259

8.  Role of carbon allocation efficiency in the temperature dependence of autotroph growth rates.

Authors:  Bernardo García-Carreras; Sofía Sal; Daniel Padfield; Dimitrios-Georgios Kontopoulos; Elvire Bestion; C-Elisa Schaum; Gabriel Yvon-Durocher; Samrāt Pawar
Journal:  Proc Natl Acad Sci U S A       Date:  2018-07-18       Impact factor: 11.205

9.  The paths of mortality: how understanding the biology of aging can help explain systems behavior of single cells.

Authors:  Matthew M Crane; Matt Kaeberlein
Journal:  Curr Opin Syst Biol       Date:  2017-12-06

Review 10.  Understanding and Engineering Distributed Biochemical Pathways in Microbial Communities.

Authors:  Xinyun Cao; Joshua J Hamilton; Ophelia S Venturelli
Journal:  Biochemistry       Date:  2018-11-20       Impact factor: 3.162

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